Sensors

26 pages, 2809 KiB

Open AccessEditor’s ChoiceArticle

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter

by Nabil Shaukat, Ahmed Ali, Muhammad Javed Iqbal, Muhammad Moinuddin and Pablo Otero

Sensors 2021, 21(4), 1149; https://doi.org/10.3390/s21041149 - 6 Feb 2021

Cited by 49 | Viewed by 6382

Abstract

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result [...] Read more.

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF. In the proposed algorithm, the RBF neural network is utilized to compensate the lack of ESKF performance by improving the innovation error term. The weights and centers of the RBF neural network are designed by minimizing the estimation mean square error (MSE) using the steepest descent optimization approach. To test the performance, the proposed RBF-augmented ESKF multi-sensor fusion was compared with the conventional ESKF under three different realistic scenarios using Monte Carlo simulations. We found that our proposed method provides better navigation and localization results despite high nonlinearity, modeling uncertainty, and external disturbances. Full article

(This article belongs to the Special Issue Information Fusion and Machine Learning for Sensors)

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25 pages, 11942 KiB

Open AccessEditor’s ChoiceArticle

Dynamic Optimization and Heuristics Based Online Coverage Path Planning in 3D Environment for UAVs

by Aurelio G. Melo, Milena F. Pinto, Andre L. M. Marcato, Leonardo M. Honório and Fabrício O. Coelho

Sensors 2021, 21(4), 1108; https://doi.org/10.3390/s21041108 - 5 Feb 2021

Cited by 29 | Viewed by 3547

Abstract

Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about [...] Read more.

Path planning is one of the most important issues in the robotics field, being applied in many domains ranging from aerospace technology and military tasks to manufacturing and agriculture. Path planning is a branch of autonomous navigation. In autonomous navigation, dynamic decisions about the path have to be taken while the robot moves towards its goal. Among the navigation area, an important class of problems is Coverage Path Planning (CPP). The CPP technique is associated with determining a collision-free path that passes through all viewpoints in a specific area. This paper presents a method to perform CPP in 3D environment for Unmanned Aerial Vehicles (UAVs) applications, namely 3D dynamic for CPP applications (3DD-CPP). The proposed method can be deployed in an unknown environment through a combination of linear optimization and heuristics. A model to estimate cost matrices accounting for UAV power usage is proposed and evaluated for a few different flight speeds. As linear optimization methods can be computationally demanding to be used on-board a UAV, this work also proposes a distributed execution of the algorithm through fog-edge computing. Results showed that 3DD-CPP had a good performance in both local execution and fog-edge for different simulated scenarios. The proposed heuristic is capable of re-optimization, enabling execution in environments with local knowledge of the environments. Full article

(This article belongs to the Special Issue Efficient Planning and Mapping for Multi-Robot Systems)

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24 pages, 3428 KiB

Open AccessEditor’s ChoiceReview

Head-Mounted Display-Based Therapies for Adults Post-Stroke: A Systematic Review and Meta-Analysis

by Guillermo Palacios-Navarro and Neville Hogan

Sensors 2021, 21(4), 1111; https://doi.org/10.3390/s21041111 - 5 Feb 2021

Cited by 39 | Viewed by 5352

Abstract

Immersive virtual reality techniques have been applied to the rehabilitation of patients after stroke, but evidence of its clinical effectiveness is scarce. The present review aims to find studies that evaluate the effects of immersive virtual reality (VR) therapies intended for motor function [...] Read more.

Immersive virtual reality techniques have been applied to the rehabilitation of patients after stroke, but evidence of its clinical effectiveness is scarce. The present review aims to find studies that evaluate the effects of immersive virtual reality (VR) therapies intended for motor function rehabilitation compared to conventional rehabilitation in people after stroke and make recommendations for future studies. Data from different databases were searched from inception until October 2020. Studies that investigated the effects of immersive VR interventions on post-stroke adult subjects via a head-mounted display (HMD) were included. These studies included a control group that received conventional therapy or another non-immersive VR intervention. The studies reported statistical data for the groups involved in at least the posttest as well as relevant outcomes measuring functional or motor recovery of either lower or upper limbs. Most of the studies found significant improvements in some outcomes after the intervention in favor of the virtual rehabilitation group. Although evidence is limited, immersive VR therapies constitute an interesting tool to improve motor learning when used in conjunction with traditional rehabilitation therapies, providing a non-pharmacological therapeutic pathway for people after stroke. Full article

(This article belongs to the Special Issue Sensors, Devices and Advanced Applications for Virtual Reality – Based Patient Rehabilitation)

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21 pages, 755 KiB

Open AccessEditor’s ChoiceArticle

Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods

by Ming Zhong, Yajin Zhou and Gang Chen

Sensors 2021, 21(4), 1113; https://doi.org/10.3390/s21041113 - 5 Feb 2021

Cited by 66 | Viewed by 7327

Abstract

IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network [...] Read more.

IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers. Full article

(This article belongs to the Special Issue Security and Privacy in Large-Scale Data Networks)

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24 pages, 5033 KiB

Open AccessEditor’s ChoiceArticle

Digital Twin Generation: Re-Conceptualizing Agent Systems for Behavior-Centered Cyber-Physical System Development

by Christian Stary

Sensors 2021, 21(4), 1096; https://doi.org/10.3390/s21041096 - 5 Feb 2021

Cited by 36 | Viewed by 4567

Abstract

Cyber-Physical Systems (CPS) form the new backbone of digital ecosystems. Upcoming CPS will be operated on a unifying basis, the Internet of Behaviors (IoB). It features autonomous while federated CPS architectures and requires corresponding behavior modeling for design and control. CPS design and [...] Read more.

Cyber-Physical Systems (CPS) form the new backbone of digital ecosystems. Upcoming CPS will be operated on a unifying basis, the Internet of Behaviors (IoB). It features autonomous while federated CPS architectures and requires corresponding behavior modeling for design and control. CPS design and control involves stakeholders in different roles with different expertise accessing behavior models, termed Digital twins. They mirror the physical CPS part and integrate it with the digital part. Representing role-specific behaviors and provided with automated execution capabilities Digital twins facilitate dynamic adaptation and (re-)configuration. This paper proposes to conceptualize agent-based design for behavior-based Digital twins through subject-oriented models. These models can be executed and, thus, increase the transparency at design and runtime. Patterns recognizing environmental factors and operation details facilitate the configuration of CPS. Subject-oriented runtime support enables dynamic adaptation and the federated use of CPS components. Full article

(This article belongs to the Section Communications)

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17 pages, 2503 KiB

Open AccessEditor’s ChoiceArticle

CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN

by Feng Qin, Tao Zuo and Xing Wang

Sensors 2021, 21(4), 1114; https://doi.org/10.3390/s21041114 - 5 Feb 2021

Cited by 55 | Viewed by 6066

Abstract

WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which [...] Read more.

WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively. Full article

(This article belongs to the Section Intelligent Sensors)

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35 pages, 3606 KiB

Open AccessEditor’s ChoiceReview

A Review on Biosensors and Recent Development of Nanostructured Materials-Enabled Biosensors

by Varnakavi. Naresh and Nohyun Lee

Sensors 2021, 21(4), 1109; https://doi.org/10.3390/s21041109 - 5 Feb 2021

Cited by 735 | Viewed by 61224

Abstract

A biosensor is an integrated receptor-transducer device, which can convert a biological response into an electrical signal. The design and development of biosensors have taken a center stage for researchers or scientists in the recent decade owing to the wide range of biosensor [...] Read more.

A biosensor is an integrated receptor-transducer device, which can convert a biological response into an electrical signal. The design and development of biosensors have taken a center stage for researchers or scientists in the recent decade owing to the wide range of biosensor applications, such as health care and disease diagnosis, environmental monitoring, water and food quality monitoring, and drug delivery. The main challenges involved in the biosensor progress are (i) the efficient capturing of biorecognition signals and the transformation of these signals into electrochemical, electrical, optical, gravimetric, or acoustic signals (transduction process), (ii) enhancing transducer performance i.e., increasing sensitivity, shorter response time, reproducibility, and low detection limits even to detect individual molecules, and (iii) miniaturization of the biosensing devices using micro-and nano-fabrication technologies. Those challenges can be met through the integration of sensing technology with nanomaterials, which range from zero- to three-dimensional, possessing a high surface-to-volume ratio, good conductivities, shock-bearing abilities, and color tunability. Nanomaterials (NMs) employed in the fabrication and nanobiosensors include nanoparticles (NPs) (high stability and high carrier capacity), nanowires (NWs) and nanorods (NRs) (capable of high detection sensitivity), carbon nanotubes (CNTs) (large surface area, high electrical and thermal conductivity), and quantum dots (QDs) (color tunability). Furthermore, these nanomaterials can themselves act as transduction elements. This review summarizes the evolution of biosensors, the types of biosensors based on their receptors, transducers, and modern approaches employed in biosensors using nanomaterials such as NPs (e.g., noble metal NPs and metal oxide NPs), NWs, NRs, CNTs, QDs, and dendrimers and their recent advancement in biosensing technology with the expansion of nanotechnology. Full article

(This article belongs to the Special Issue Advances of Nanotechnologies in Biosensing and Bioimaging)

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19 pages, 1639 KiB

Open AccessEditor’s ChoiceReview

Wearable Devices Suitable for Monitoring Twenty Four Hour Heart Rate Variability in Military Populations

by Katrina Hinde, Graham White and Nicola Armstrong

Sensors 2021, 21(4), 1061; https://doi.org/10.3390/s21041061 - 4 Feb 2021

Cited by 77 | Viewed by 16518

Abstract

Heart rate variability (HRV) measurements provide information on the autonomic nervous system and the balance between parasympathetic and sympathetic activity. A high HRV can be advantageous, reflecting the ability of the autonomic nervous system to adapt, whereas a low HRV can be indicative [...] Read more.

Heart rate variability (HRV) measurements provide information on the autonomic nervous system and the balance between parasympathetic and sympathetic activity. A high HRV can be advantageous, reflecting the ability of the autonomic nervous system to adapt, whereas a low HRV can be indicative of fatigue, overtraining or health issues. There has been a surge in wearable devices that claim to measure HRV. Some of these include spot measurements, whilst others only record during periods of rest and/or sleep. Few are capable of continuously measuring HRV (≥24 h). We undertook a narrative review of the literature with the aim to determine which currently available wearable devices are capable of measuring continuous, precise HRV measures. The review also aims to evaluate which devices would be suitable in a field setting specific to military populations. The Polar H10 appears to be the most accurate wearable device when compared to criterion measures and even appears to supersede traditional methods during exercise. However, currently, the H10 must be paired with a watch to enable the raw data to be extracted for HRV analysis if users need to avoid using an app (for security or data ownership reasons) which incurs additional cost. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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18 pages, 682 KiB

Open AccessEditor’s ChoiceReview

Pain and Stress Detection Using Wearable Sensors and Devices—A Review

by Jerry Chen, Maysam Abbod and Jiann-Shing Shieh

Sensors 2021, 21(4), 1030; https://doi.org/10.3390/s21041030 - 3 Feb 2021

Cited by 90 | Viewed by 26762

Abstract

Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation [...] Read more.

Pain is a subjective feeling; it is a sensation that every human being must have experienced all their life. Yet, its mechanism and the way to immune to it is still a question to be answered. This review presents the mechanism and correlation of pain and stress, their assessment and detection approach with medical devices and wearable sensors. Various physiological signals (i.e., heart activity, brain activity, muscle activity, electrodermal activity, respiratory, blood volume pulse, skin temperature) and behavioral signals are organized for wearables sensors detection. By reviewing the wearable sensors used in the healthcare domain, we hope to find a way for wearable healthcare-monitoring system to be applied on pain and stress detection. Since pain leads to multiple consequences or symptoms such as muscle tension and depression that are stress related, there is a chance to find a new approach for chronic pain detection using daily life sensors or devices. Then by integrating modern computing techniques, there is a chance to handle pain and stress management issue. Full article

(This article belongs to the Special Issue Advanced Signal Processing in Wearable Sensors for Health Monitoring)

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26 pages, 9983 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

by Qiaodi Wen, Ziqi Luo, Ruitao Chen, Yifan Yang and Guofa Li

Sensors 2021, 21(4), 1033; https://doi.org/10.3390/s21041033 - 3 Feb 2021

Cited by 57 | Viewed by 5049

Abstract

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are [...] Read more.

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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19 pages, 5925 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning-Based Industry 4.0 and Internet of Things towards Effective Energy Management for Smart Buildings

by Mahmoud Elsisi, Minh-Quang Tran, Karar Mahmoud, Matti Lehtonen and Mohamed M. F. Darwish

Sensors 2021, 21(4), 1038; https://doi.org/10.3390/s21041038 - 3 Feb 2021

Cited by 124 | Viewed by 9374

Abstract

Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between [...] Read more.

Worldwide, energy consumption and saving represent the main challenges for all sectors, most importantly in industrial and domestic sectors. The internet of things (IoT) is a new technology that establishes the core of Industry 4.0. The IoT enables the sharing of signals between devices and machines via the internet. Besides, the IoT system enables the utilization of artificial intelligence (AI) techniques to manage and control the signals between different machines based on intelligence decisions. The paper’s innovation is to introduce a deep learning and IoT based approach to control the operation of air conditioners in order to reduce energy consumption. To achieve such an ambitious target, we have proposed a deep learning-based people detection system utilizing the YOLOv3 algorithm to count the number of persons in a specific area. Accordingly, the operation of the air conditioners could be optimally managed in a smart building. Furthermore, the number of persons and the status of the air conditioners are published via the internet to the dashboard of the IoT platform. The proposed system enhances decision making about energy consumption. To affirm the efficacy and effectiveness of the proposed approach, intensive test scenarios are simulated in a specific smart building considering the existence of air conditioners. The simulation results emphasize that the proposed deep learning-based recognition algorithm can accurately detect the number of persons in the specified area, thanks to its ability to model highly non-linear relationships in data. The detection status can also be successfully published on the dashboard of the IoT platform. Another vital application of the proposed promising approach is in the remote management of diverse controllable devices. Full article

(This article belongs to the Section Intelligent Sensors)

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17 pages, 2544 KiB

Open AccessEditor’s ChoiceArticle

Structural Health Monitoring Using Ultrasonic Guided-Waves and the Degree of Health Index

by Sergio Cantero-Chinchilla, Gerardo Aranguren, José Manuel Royo, Manuel Chiachío, Josu Etxaniz and Andrea Calvo-Echenique

Sensors 2021, 21(3), 993; https://doi.org/10.3390/s21030993 - 2 Feb 2021

Cited by 22 | Viewed by 4759

Abstract

This paper proposes a new damage index named degree of health (DoH) to efficiently tackle structural damage monitoring in real-time. As a key contribution, the proposed index relies on a pattern matching methodology that measures the time-of-flight mismatch of sequential ultrasonic guided-wave measurements [...] Read more.

This paper proposes a new damage index named degree of health (DoH) to efficiently tackle structural damage monitoring in real-time. As a key contribution, the proposed index relies on a pattern matching methodology that measures the time-of-flight mismatch of sequential ultrasonic guided-wave measurements using fuzzy logic fundamentals. The ultrasonic signals are generated using the transmission beamforming technique with a phased-array of piezoelectric transducers. The acquisition is carried out by two phased-arrays to compare the influence of pulse-echo and pitch-catch modes in the damage assessment. The proposed monitoring approach is illustrated in a fatigue test of an aluminum sheet with an initial notch. As an additional novelty, the proposed pattern matching methodology uses the data stemming from the transmission beamforming technique for structural health monitoring. The results demonstrate the efficiency and robustness of the proposed framework in providing a qualitative and quantitative assessment for fatigue crack damage. Full article

(This article belongs to the Special Issue Structural Health Monitoring with Ultrasonic Guided-Waves Sensors)

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18 pages, 3429 KiB

Open AccessEditor’s ChoiceReview

Carbon Nanotube Field-Effect Transistor-Based Chemical and Biological Sensors

by Xuesong Yao, Yalei Zhang, Wanlin Jin, Youfan Hu and Yue Cui

Sensors 2021, 21(3), 995; https://doi.org/10.3390/s21030995 - 2 Feb 2021

Cited by 48 | Viewed by 8324

Abstract

Chemical and biological sensors have attracted great interest due to their importance in applications of healthcare, food quality monitoring, environmental monitoring, etc. Carbon nanotube (CNT)-based field-effect transistors (FETs) are novel sensing device configurations and are very promising for their potential to drive many [...] Read more.

Chemical and biological sensors have attracted great interest due to their importance in applications of healthcare, food quality monitoring, environmental monitoring, etc. Carbon nanotube (CNT)-based field-effect transistors (FETs) are novel sensing device configurations and are very promising for their potential to drive many technological advancements in this field due to the extraordinary electrical properties of CNTs. This review focuses on the implementation of CNT-based FETs (CNTFETs) in chemical and biological sensors. It begins with the introduction of properties, and surface functionalization of CNTs for sensing. Then, configurations and sensing mechanisms for CNT FETs are introduced. Next, recent progresses of CNTFET-based chemical sensors, and biological sensors are summarized. Finally, we end the review with an overview about the current application status and the remaining challenges for the CNTFET-based chemical and biological sensors. Full article

(This article belongs to the Special Issue State-of-the-Art Biosensors Technology in China 2020–2021)

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39 pages, 4936 KiB

Open AccessEditor’s ChoiceSystematic Review

Collaborative Indoor Positioning Systems: A Systematic Review

by Pavel Pascacio, Sven Casteleyn, Joaquín Torres-Sospedra, Elena Simona Lohan and Jari Nurmi

Sensors 2021, 21(3), 1002; https://doi.org/10.3390/s21031002 - 2 Feb 2021

Cited by 90 | Viewed by 10352

Abstract

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning [...] Read more.

Research and development in Collaborative Indoor Positioning Systems (CIPSs) is growing steadily due to their potential to improve on the performance of their non-collaborative counterparts. In contrast to the outdoors scenario, where Global Navigation Satellite System is widely adopted, in (collaborative) indoor positioning systems a large variety of technologies, techniques, and methods is being used. Moreover, the diversity of evaluation procedures and scenarios hinders a direct comparison. This paper presents a systematic review that gives a general view of the current CIPSs. A total of 84 works, published between 2006 and 2020, have been identified. These articles were analyzed and classified according to the described system’s architecture, infrastructure, technologies, techniques, methods, and evaluation. The results indicate a growing interest in collaborative positioning, and the trend tend to be towards the use of distributed architectures and infrastructure-less systems. Moreover, the most used technologies to determine the collaborative positioning between users are wireless communication technologies (Wi-Fi, Ultra-WideBand, and Bluetooth). The predominant collaborative positioning techniques are Received Signal Strength Indication, Fingerprinting, and Time of Arrival/Flight, and the collaborative methods are particle filters, Belief Propagation, Extended Kalman Filter, and Least Squares. Simulations are used as the main evaluation procedure. On the basis of the analysis and results, several promising future research avenues and gaps in research were identified. Full article

(This article belongs to the Special Issue Novel Applications of Positioning Systems and Sensors)

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50 pages, 1074 KiB

Open AccessEditor’s ChoiceReview

Comprehensive Review of Vision-Based Fall Detection Systems

by Jesús Gutiérrez, Víctor Rodríguez and Sergio Martin

Sensors 2021, 21(3), 947; https://doi.org/10.3390/s21030947 - 1 Feb 2021

Cited by 72 | Viewed by 8734

Abstract

Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this [...] Read more.

Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers. Full article

(This article belongs to the Special Issue Healthcare Monitoring and Management with Artificial Intelligence)

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14 pages, 2243 KiB

Open AccessEditor’s ChoiceArticle

A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM Autoencoders

by Xanthi Bampoula, Georgios Siaterlis, Nikolaos Nikolakis and Kosmas Alexopoulos

Sensors 2021, 21(3), 972; https://doi.org/10.3390/s21030972 - 1 Feb 2021

Cited by 65 | Viewed by 8598

Abstract

Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, [...] Read more.

Condition monitoring of industrial equipment, combined with machine learning algorithms, may significantly improve maintenance activities on modern cyber-physical production systems. However, data of proper quality and of adequate quantity, modeling both good operational conditions as well as abnormal situations throughout the operational lifecycle, are required. Nevertheless, this is difficult to acquire in a non-destructive approach. In this context, this study investigates an approach to enable a transition from preventive maintenance activities, that are scheduled at predetermined time intervals, into predictive ones. In order to enable such approaches in a cyber-physical production system, a deep learning algorithm is used, allowing for maintenance activities to be planned according to the actual operational status of the machine and not in advance. An autoencoder-based methodology is employed for classifying real-world machine and sensor data, into a set of condition-related labels. Real-world data collected from manufacturing operations are used for training and testing a prototype implementation of Long Short-Term Memory autoencoders for estimating the remaining useful life of the monitored equipment. Finally, the proposed approach is evaluated in a use case related to a steel industry production process. Full article

(This article belongs to the Special Issue Cyberphysical Sensing Systems for Fault Detection and Identification)

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15 pages, 2543 KiB

Open AccessEditor’s ChoiceArticle

Reduced Graphene Oxide and Polyaniline Nanofibers Nanocomposite for the Development of an Amperometric Glucose Biosensor

by Anton Popov, Ruta Aukstakojyte, Justina Gaidukevic, Viktorija Lisyte, Asta Kausaite-Minkstimiene, Jurgis Barkauskas and Almira Ramanaviciene

Sensors 2021, 21(3), 948; https://doi.org/10.3390/s21030948 - 1 Feb 2021

Cited by 48 | Viewed by 4817

Abstract

The control of glucose concentration is a crucial factor in clinical diagnosis and the food industry. Electrochemical biosensors based on reduced graphene oxide (rGO) and conducting polymers have a high potential for practical application. A novel thermal reduction protocol of graphene oxide (GO) [...] Read more.

The control of glucose concentration is a crucial factor in clinical diagnosis and the food industry. Electrochemical biosensors based on reduced graphene oxide (rGO) and conducting polymers have a high potential for practical application. A novel thermal reduction protocol of graphene oxide (GO) in the presence of malonic acid was applied for the synthesis of rGO. The rGO was characterized by scanning electron microscopy, X-ray diffraction analysis, Fourier-transform infrared spectroscopy, and Raman spectroscopy. rGO in combination with polyaniline (PANI), Nafion, and glucose oxidase (GOx) was used to develop an amperometric glucose biosensor. A graphite rod (GR) electrode premodified with a dispersion of PANI nanostructures and rGO, Nafion, and GOx was proposed as the working electrode of the biosensor. The optimal ratio of PANI and rGO in the dispersion used as a matrix for GOx immobilization was equal to 1:10. The developed glucose biosensor was characterized by a wide linear range (from 0.5 to 50 mM), low limit of detection (0.089 mM), good selectivity, reproducibility, and stability. Therefore, the developed biosensor is suitable for glucose determination in human serum. The PANI nanostructure and rGO dispersion is a promising material for the construction of electrochemical glucose biosensors. Full article

(This article belongs to the Section Biosensors)

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20 pages, 659 KiB

Open AccessEditor’s ChoiceArticle

Experimental Analysis of the Application of Serverless Computing to IoT Platforms

by Priscilla Benedetti, Mauro Femminella, Gianluca Reali and Kris Steenhaut

Sensors 2021, 21(3), 928; https://doi.org/10.3390/s21030928 - 30 Jan 2021

Cited by 29 | Viewed by 4681

Abstract

Serverless computing, especially implemented through Function-as-a-Service (FaaS) platforms, has recently been gaining popularity as an application deployment model in which functions are automatically instantiated when called and scaled when needed. When a warm start deployment mode is used, the FaaS platform gives users [...] Read more.

Serverless computing, especially implemented through Function-as-a-Service (FaaS) platforms, has recently been gaining popularity as an application deployment model in which functions are automatically instantiated when called and scaled when needed. When a warm start deployment mode is used, the FaaS platform gives users the perception of constantly available resources. Conversely, when a cold start mode is used, containers running the application’s modules are automatically destroyed when the application has been executed. The latter can lead to considerable resource and cost savings. In this paper, we explore the suitability of both modes for deploying Internet of Things (IoT) applications considering a low resources testbed comparable to an edge node. We discuss the implementation and the experimental analysis of an IoT serverless platform that includes typical IoT service elements. A performance study in terms of resource consumption and latency is presented for the warm and cold start deployment mode, and implemented using OpenFaaS, a well-known open-source FaaS framework which allows to test a cold start deployment with precise inactivity time setup thanks to its flexibility. This experimental analysis allows to evaluate the aptness of the two deployment modes under different operating conditions: Exploiting OpenFaaS minimum inactivity time setup, we find that the cold start mode can be convenient in order to save edge nodes limited resources, but only if the data transmission period is significantly higher than the time needed to trigger containers shutdown. Full article

(This article belongs to the Special Issue Communications and Computing in Sensor Network)

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23 pages, 3512 KiB

Open AccessEditor’s ChoiceArticle

Measurement of Rock Joint Surfaces by Using Smartphone Structure from Motion (SfM) Photogrammetry

by Pengju An, Kun Fang, Qiangqiang Jiang, Haihua Zhang and Yi Zhang

Sensors 2021, 21(3), 922; https://doi.org/10.3390/s21030922 - 30 Jan 2021

Cited by 39 | Viewed by 5073

Abstract

The measurement of rock joint surfaces is essential for the estimation of the shear strength of the rock discontinuities in rock engineering. Commonly used techniques for the acquisition of the morphology of the surfaces, such as profilometers and laser scanners, either have low [...] Read more.

The measurement of rock joint surfaces is essential for the estimation of the shear strength of the rock discontinuities in rock engineering. Commonly used techniques for the acquisition of the morphology of the surfaces, such as profilometers and laser scanners, either have low accuracy or high cost. Therefore, a high-speed, low-cost, and high-accuracy method for obtaining the topography of the joint surfaces is necessary. In this paper, a smartphone structure from motion (SfM) photogrammetric solution for measuring rock joint surfaces is presented and evaluated. Image datasets of two rock joint specimens were taken under two different modes by using an iPhone 6s, a Pixel 2, and a T329t and subsequently processed through SfM-based software to obtain 3D models. The technique for measuring rock joint surfaces was evaluated using the root mean square error (RMSE) of the cloud-to-cloud distance and the mean error of the joint roughness coefficient (JRC). The results show that the RMSEs by using the iPhone 6s and Pixel 2 are both less than 0.08 mm. The mean errors of the JRC are −7.54 and −5.27% with point intervals of 0.25 and 1.0 mm, respectively. The smartphone SfM photogrammetric method has comparable accuracy to a 3D laser scanner approach for reconstructing laboratory-sized rock joint surfaces, and it has the potential to become a popular method for measuring rock joint surfaces. Full article

(This article belongs to the Special Issue Advanced Applications in Smartphone-Based Analysis)

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23 pages, 433 KiB

Open AccessEditor’s ChoiceArticle

WSN-SLAP: Secure and Lightweight Mutual Authentication Protocol for Wireless Sensor Networks

by Deok Kyu Kwon, Sung Jin Yu, Joon Young Lee, Seung Hwan Son and Young Ho Park

Sensors 2021, 21(3), 936; https://doi.org/10.3390/s21030936 - 30 Jan 2021

Cited by 47 | Viewed by 5732

Abstract

Wireless sensor networks (WSN) are widely used to provide users with convenient services such as health-care, and smart home. To provide convenient services, sensor nodes in WSN environments collect and send the sensing data to the gateway. However, it can suffer from serious [...] Read more.

Wireless sensor networks (WSN) are widely used to provide users with convenient services such as health-care, and smart home. To provide convenient services, sensor nodes in WSN environments collect and send the sensing data to the gateway. However, it can suffer from serious security issues because susceptible messages are exchanged through an insecure channel. Therefore, secure authentication protocols are necessary to prevent security flaws in WSN. In 2020, Moghadam et al. suggested an efficient authentication and key agreement scheme in WSN. Unfortunately, we discover that Moghadam et al.’s scheme cannot prevent insider and session-specific random number leakage attacks. We also prove that Moghadam et al.’s scheme does not ensure perfect forward secrecy. To prevent security vulnerabilities of Moghadam et al.’s scheme, we propose a secure and lightweight mutual authentication protocol for WSNs (WSN-SLAP). WSN-SLAP has the resistance from various security drawbacks, and provides perfect forward secrecy and mutual authentication. We prove the security of WSN-SLAP by using Burrows-Abadi-Needham (BAN) logic, Real-or-Random (ROR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. In addition, we evaluate the performance of WSN-SLAP compared with existing related protocols. We demonstrate that WSN-SLAP is more secure and suitable than previous protocols for WSN environments. Full article

(This article belongs to the Special Issue Cryptography and Information Security in Wireless Sensor Networks)

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14 pages, 993 KiB

Open AccessEditor’s ChoiceArticle

Validity of the Polar H7 Heart Rate Sensor for Heart Rate Variability Analysis during Exercise in Different Age, Body Composition and Fitness Level Groups

by Adrián Hernández-Vicente, David Hernando, Jorge Marín-Puyalto, Germán Vicente-Rodríguez, Nuria Garatachea, Esther Pueyo and Raquel Bailón

Sensors 2021, 21(3), 902; https://doi.org/10.3390/s21030902 - 29 Jan 2021

Cited by 32 | Viewed by 6233

Abstract

This work aims to validate the Polar H7 heart rate (HR) sensor for heart rate variability (HRV) analysis at rest and during various exercise intensities in a cohort of male volunteers with different age, body composition and fitness level. Cluster analysis was carried [...] Read more.

This work aims to validate the Polar H7 heart rate (HR) sensor for heart rate variability (HRV) analysis at rest and during various exercise intensities in a cohort of male volunteers with different age, body composition and fitness level. Cluster analysis was carried out to evaluate how these phenotypic characteristics influenced HR and HRV measurements. For this purpose, sixty-seven volunteers performed a test consisting of the following consecutive segments: sitting rest, three submaximal exercise intensities in cycle-ergometer and sitting recovery. The agreement between HRV indices derived from Polar H7 and a simultaneous electrocardiogram (ECG) was assessed using concordance correlation coefficient (CCC). The percentage of subjects not reaching excellent agreement (CCC > 0.90) was higher for high-frequency power (P_HF) than for low-frequency power (P_LF) of HRV and increased with exercise intensity. A cluster of unfit and not young volunteers with high trunk fat percentage showed the highest error in HRV indices. This study indicates that Polar H7 and ECG were interchangeable at rest. During exercise, HR and P_LF showed excellent agreement between devices. However, during the highest exercise intensity, CCC for P_HF was lower than 0.90 in as many as 60% of the volunteers. During recovery, HR but not HRV measurements were accurate. As a conclusion, phenotypic differences between subjects can represent one of the causes for disagreement between HR sensors and ECG devices, which should be considered specifically when using Polar H7 and, generally, in the validation of any HR sensor for HRV analysis. Full article

(This article belongs to the Special Issue Smartphones and Wearable Sensors for Monitoring Heart Rate and Heart Rate Variability)

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23 pages, 822 KiB

Open AccessEditor’s ChoiceReview

Unobtrusive Health Monitoring in Private Spaces: The Smart Home

by Ju Wang, Nicolai Spicher, Joana M. Warnecke, Mostafa Haghi, Jonas Schwartze and Thomas M. Deserno

Sensors 2021, 21(3), 864; https://doi.org/10.3390/s21030864 - 28 Jan 2021

Cited by 60 | Viewed by 8476

Abstract

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to [...] Read more.

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in

n = 55

papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (

n = 38

), time spent on activities (

n = 18

)) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (

n = 5

). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking. Full article

(This article belongs to the Special Issue Simplified Sensing for Ambient Assisted Living in Smart Homes)

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41 pages, 3906 KiB

Open AccessEditor’s ChoiceArticle

Lights and Shadows: A Comprehensive Survey on Cooperative and Precoding Schemes to Overcome LOS Blockage and Interference in Indoor VLC

by Máximo Morales Céspedes, Borja Genovés Guzmán and Víctor P. Gil Jiménez

Sensors 2021, 21(3), 861; https://doi.org/10.3390/s21030861 - 28 Jan 2021

Cited by 22 | Viewed by 4005

Abstract

Visible light communications (VLC) have received significant attention as a way of moving part of the saturated indoor wireless traffic to the wide and unregulated visible optical spectrum. Nowadays, VLC are considered as a suitable technology, for several applications such as high-rate data [...] Read more.

Visible light communications (VLC) have received significant attention as a way of moving part of the saturated indoor wireless traffic to the wide and unregulated visible optical spectrum. Nowadays, VLC are considered as a suitable technology, for several applications such as high-rate data transmission, supporting internet of things communications or positioning. The signal processing originally derived from radio-frequency (RF) systems such as cooperative or precoding schemes can be applied to VLC. However, its implementation is not straightforward. Furthermore, unlike RF transmission, VLC present a predominant line-of-sight link, although a weak non-LoS component may appear due to the reflection of the light on walls, floor, ceiling and nearby objects. Blocking effects may compromise the performance of the aforementioned transmission schemes. There exist several surveys in the literature focused on VLC and its applications, but the management of the shadowing and interference in VLC requires a comprehensive study. To fill this gap, this work introduces the implementation of cooperative and precoding schemes to VLC, while remarking their benefits and drawbacks for overcoming the shadowing effects. After that, the combination of both cooperative and precoding schemes is analyzed as a way of providing resilient VLC networks. Finally, we propose several open issues that the cooperative and precoding schemes must face in order to provide satisfactory VLC performance in indoor scenarios. Full article

(This article belongs to the Special Issue State-of-the-Art Indoor and Outdoor Visible Light Communications (VLC) Applications and Systems)

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24 pages, 9806 KiB

Open AccessEditor’s ChoiceArticle

Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

by Zhongzheng Fu, Xinrun He, Enkai Wang, Jun Huo, Jian Huang and Dongrui Wu

Sensors 2021, 21(3), 885; https://doi.org/10.3390/s21030885 - 28 Jan 2021

Cited by 49 | Viewed by 5799

Abstract

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. [...] Read more.

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%. Full article

(This article belongs to the Special Issue Wearable Sensor for Activity Analysis and Context Recognition)

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21 pages, 5651 KiB

Open AccessEditor’s ChoiceArticle

Dense Deployment of LoRa Networks: Expectations and Limits of Channel Activity Detection and Capture Effect for Radio Channel Access

by Congduc Pham and Muhammad Ehsan

Sensors 2021, 21(3), 825; https://doi.org/10.3390/s21030825 - 26 Jan 2021

Cited by 29 | Viewed by 5025

Abstract

With worldwide deployment of LoRa/LoRaWAN LPWAN networks in a large variety of applications, it is crucial to improve the robustness of LoRa channel access which is largely ALOHA-like to support environments with higher node density. This article presents extensive experiments on LoRa Channel [...] Read more.

With worldwide deployment of LoRa/LoRaWAN LPWAN networks in a large variety of applications, it is crucial to improve the robustness of LoRa channel access which is largely ALOHA-like to support environments with higher node density. This article presents extensive experiments on LoRa Channel Activity Detection and Capture Effect property in order to better understand how a competition-based channel access mechanisms can be optimized for LoRa LPWAN radio technology. In the light of these experimentation results, the contribution continues by identifying design guidelines for a channel access mechanism in LoRa and by proposing a channel access method with a lightweight collision avoidance mechanism that can operate without a reliable Clear Channel Assessment procedure. The proposed channel access mechanism has been implemented and preliminary tests show promising capabilities in increasing the Packet Delivery Rate in dense configurations. Full article

(This article belongs to the Special Issue Massive and Reliable Sensor Communications with LPWANs Technologies)

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23 pages, 1553 KiB

Open AccessEditor’s ChoiceReview

Smart Textiles and Sensorized Garments for Physiological Monitoring: A Review of Available Solutions and Techniques

by Alessandra Angelucci, Matteo Cavicchioli, Ilaria A. Cintorrino, Giuseppe Lauricella, Chiara Rossi, Sara Strati and Andrea Aliverti

Sensors 2021, 21(3), 814; https://doi.org/10.3390/s21030814 - 26 Jan 2021

Cited by 80 | Viewed by 14047

Abstract

Several wearable devices for physiological and activity monitoring are found on the market, but most of them only allow spot measurements. However, the continuous detection of physiological parameters without any constriction in time or space would be useful in several fields such as [...] Read more.

Several wearable devices for physiological and activity monitoring are found on the market, but most of them only allow spot measurements. However, the continuous detection of physiological parameters without any constriction in time or space would be useful in several fields such as healthcare, fitness, and work. This can be achieved with the application of textile technologies for sensorized garments, where the sensors are completely embedded in the fabric. The complete integration of sensors in the fabric leads to several manufacturing techniques that allow dealing with both the technological challenges entailed by the physiological parameters under investigation, and the basic requirements of a garment such as perspiration, washability, and comfort. This review is intended to provide a detailed description of the textile technologies in terms of materials and manufacturing processes employed in the production of sensorized fabrics. The focus is pointed at the technical challenges and the advanced solutions introduced with respect to conventional sensors for recording different physiological parameters, and some interesting textile implementations for the acquisition of biopotentials, respiratory parameters, temperature and sweat are proposed. In the last section, an overview of the main garments on the market is depicted, also exploring some relevant projects under development. Full article

(This article belongs to the Special Issue Electronic Textiles and Innovative Wearables)

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28 pages, 4342 KiB

Open AccessEditor’s ChoiceReview

Review of Current Guided Wave Ultrasonic Testing (GWUT) Limitations and Future Directions

by Samuel Chukwuemeka Olisa, Muhammad A. Khan and Andrew Starr

Sensors 2021, 21(3), 811; https://doi.org/10.3390/s21030811 - 26 Jan 2021

Cited by 63 | Viewed by 9755

Abstract

Damage is an inevitable occurrence in metallic structures and when unchecked could result in a catastrophic breakdown of structural assets. Non-destructive evaluation (NDE) is adopted in industries for assessment and health inspection of structural assets. Prominent among the NDE techniques is guided wave [...] Read more.

Damage is an inevitable occurrence in metallic structures and when unchecked could result in a catastrophic breakdown of structural assets. Non-destructive evaluation (NDE) is adopted in industries for assessment and health inspection of structural assets. Prominent among the NDE techniques is guided wave ultrasonic testing (GWUT). This method is cost-effective and possesses an enormous capability for long-range inspection of corroded structures, detection of sundries of crack and other metallic damage structures at low frequency and energy attenuation. However, the parametric features of the GWUT are affected by structural and environmental operating conditions and result in masking damage signal. Most studies focused on identifying individual damage under varying conditions while combined damage phenomena can coexist in structure and hasten its deterioration. Hence, it is an impending task to study the effect of combined damage on a structure under varying conditions and correlate it with GWUT parametric features. In this respect, this work reviewed the literature on UGWs, damage inspection, severity, temperature influence on the guided wave and parametric characteristics of the inspecting wave. The review is limited to the piezoelectric transduction unit. It was keenly observed that no significant work had been done to correlate the parametric feature of GWUT with combined damage effect under varying conditions. It is therefore proposed to investigate this impending task. Full article

(This article belongs to the Special Issue Sensors for Fault Diagnosis and Prognostics)

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22 pages, 6191 KiB

Open AccessEditor’s ChoiceReview

Fiber-Optic Localized Surface Plasmon Resonance Sensors Based on Nanomaterials

by Seunghun Lee, Hyerin Song, Heesang Ahn, Seungchul Kim, Jong-ryul Choi and Kyujung Kim

Sensors 2021, 21(3), 819; https://doi.org/10.3390/s21030819 - 26 Jan 2021

Cited by 49 | Viewed by 7143

Abstract

Applying fiber-optics on surface plasmon resonance (SPR) sensors is aimed at practical usability over conventional SPR sensors. Recently, field localization techniques using nanostructures or nanoparticles have been investigated on optical fibers for further sensitivity enhancement and significant target selectivity. In this review article, [...] Read more.

Applying fiber-optics on surface plasmon resonance (SPR) sensors is aimed at practical usability over conventional SPR sensors. Recently, field localization techniques using nanostructures or nanoparticles have been investigated on optical fibers for further sensitivity enhancement and significant target selectivity. In this review article, we explored varied recent research approaches of fiber-optics based localized surface plasmon resonance (LSPR) sensors. The article contains interesting experimental results using fiber-optic LSPR sensors for three different application categories: (1) chemical reactions measurements, (2) physical properties measurements, and (3) biological events monitoring. In addition, novel techniques which can create synergy combined with fiber-optic LSPR sensors were introduced. The review article suggests fiber-optic LSPR sensors have lots of potential for measurements of varied targets with high sensitivity. Moreover, the previous results show that the sensitivity enhancements which can be applied with creative varied plasmonic nanomaterials make it possible to detect minute changes including quick chemical reactions and tiny molecular activities. Full article

(This article belongs to the Special Issue Plasmonic Sensing Techniques with Nanomaterials)

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19 pages, 4762 KiB

Open AccessEditor’s ChoiceArticle

Optimization of a Low-Power Chemoresistive Gas Sensor: Predictive Thermal Modelling and Mechanical Failure Analysis

by Andrea Gaiardo, David Novel, Elia Scattolo, Michele Crivellari, Antonino Picciotto, Francesco Ficorella, Erica Iacob, Alessio Bucciarelli, Luisa Petti, Paolo Lugli and Alvise Bagolini

Sensors 2021, 21(3), 783; https://doi.org/10.3390/s21030783 - 25 Jan 2021

Cited by 23 | Viewed by 3656

Abstract

The substrate plays a key role in chemoresistive gas sensors. It acts as mechanical support for the sensing material, hosts the heating element and, also, aids the sensing material in signal transduction. In recent years, a significant improvement in the substrate production process [...] Read more.

The substrate plays a key role in chemoresistive gas sensors. It acts as mechanical support for the sensing material, hosts the heating element and, also, aids the sensing material in signal transduction. In recent years, a significant improvement in the substrate production process has been achieved, thanks to the advances in micro- and nanofabrication for micro-electro-mechanical system (MEMS) technologies. In addition, the use of innovative materials and smaller low-power consumption silicon microheaters led to the development of high-performance gas sensors. Various heater layouts were investigated to optimize the temperature distribution on the membrane, and a suspended membrane configuration was exploited to avoid heat loss by conduction through the silicon bulk. However, there is a lack of comprehensive studies focused on predictive models for the optimization of the thermal and mechanical properties of a microheater. In this work, three microheater layouts in three membrane sizes were developed using the microfabrication process. The performance of these devices was evaluated to predict their thermal and mechanical behaviors by using both experimental and theoretical approaches. Finally, a statistical method was employed to cross-correlate the thermal predictive model and the mechanical failure analysis, aiming at microheater design optimization for gas-sensing applications. Full article

(This article belongs to the Special Issue Advanced Micro and Nano Technologies for Gas Sensing)

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20 pages, 8467 KiB

Open AccessEditor’s ChoiceArticle

Inter-Beam Co-Channel Downlink and Uplink Interference for 5G New Radio in mm-Wave Bands

by Kamil Bechta, Jan M. Kelner, Cezary Ziółkowski and Leszek Nowosielski

Sensors 2021, 21(3), 793; https://doi.org/10.3390/s21030793 - 25 Jan 2021

Cited by 21 | Viewed by 4288

Abstract

This paper presents a methodology for assessing co-channel interference that arises in multi-beam transmitting and receiving antennas used in fifth-generation (5G) systems. This evaluation is essential for minimizing spectral resources, which allows for using the same frequency bands in angularly separated antenna beams [...] Read more.

This paper presents a methodology for assessing co-channel interference that arises in multi-beam transmitting and receiving antennas used in fifth-generation (5G) systems. This evaluation is essential for minimizing spectral resources, which allows for using the same frequency bands in angularly separated antenna beams of a 5G-based station (gNodeB). In the developed methodology, a multi-ellipsoidal propagation model (MPM) provides a mapping of the multipath propagation phenomenon and considers the directivity of antenna beams. To demonstrate the designation procedure of interference level we use simulation tests. For exemplary scenarios in downlink and uplink, we showed changes in a signal-to-interference ratio versus a separation angle between the serving (useful) and interfering beams and the distance between the gNodeB and user equipment. This evaluation is the basis for determining the minimum separation angle for which an acceptable interference level is ensured. The analysis was carried out for the lower millimeter-wave band, which is planned to use in 5G micro-cells base stations. Full article

(This article belongs to the Special Issue Advanced Physical-Layer Technologies for beyond 5G Wireless Communication Networks)

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18 pages, 5887 KiB

Open AccessEditor’s ChoiceReview

Genetically Encoded Biosensors Based on Fluorescent Proteins

by Hyunbin Kim, Jeongmin Ju, Hae Nim Lee, Hyeyeon Chun and Jihye Seong

Sensors 2021, 21(3), 795; https://doi.org/10.3390/s21030795 - 25 Jan 2021

Cited by 28 | Viewed by 9611

Abstract

Genetically encoded biosensors based on fluorescent proteins (FPs) allow for the real-time monitoring of molecular dynamics in space and time, which are crucial for the proper functioning and regulation of complex cellular processes. Depending on the types of molecular events to be monitored, [...] Read more.

Genetically encoded biosensors based on fluorescent proteins (FPs) allow for the real-time monitoring of molecular dynamics in space and time, which are crucial for the proper functioning and regulation of complex cellular processes. Depending on the types of molecular events to be monitored, different sensing strategies need to be applied for the best design of FP-based biosensors. Here, we review genetically encoded biosensors based on FPs with various sensing strategies, for example, translocation, fluorescence resonance energy transfer (FRET), reconstitution of split FP, pH sensitivity, maturation speed, and so on. We introduce general principles of each sensing strategy and discuss critical factors to be considered if available, then provide representative examples of these FP-based biosensors. These will help in designing the best sensing strategy for the successful development of new genetically encoded biosensors based on FPs. Full article

(This article belongs to the Special Issue DNA-Based Sensors for Single-Molecule Biology)

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24 pages, 1263 KiB

Open AccessEditor’s ChoiceReview

Wearable Devices for Ergonomics: A Systematic Literature Review

by Elena Stefana, Filippo Marciano, Diana Rossi, Paola Cocca and Giuseppe Tomasoni

Sensors 2021, 21(3), 777; https://doi.org/10.3390/s21030777 - 24 Jan 2021

Cited by 76 | Viewed by 10469

Abstract

Wearable devices are pervasive solutions for increasing work efficiency, improving workers’ well-being, and creating interactions between users and the environment anytime and anywhere. Although several studies on their use in various fields have been performed, there are no systematic reviews on their utilisation [...] Read more.

Wearable devices are pervasive solutions for increasing work efficiency, improving workers’ well-being, and creating interactions between users and the environment anytime and anywhere. Although several studies on their use in various fields have been performed, there are no systematic reviews on their utilisation in ergonomics. Therefore, we conducted a systematic review to identify wearable devices proposed in the scientific literature for ergonomic purposes and analyse how they can support the improvement of ergonomic conditions. Twenty-eight papers were retrieved and analysed thanks to eleven comparison dimensions related to ergonomic factors, purposes, and criteria, populations, application and validation. The majority of the available devices are sensor systems composed of different types and numbers of sensors located in diverse body parts. These solutions also represent the technology most frequently employed for monitoring and reducing the risk of awkward postures. In addition, smartwatches, body-mounted smartphones, insole pressure systems, and vibrotactile feedback interfaces have been developed for evaluating and/or controlling physical loads or postures. The main results and the defined framework of analysis provide an overview of the state of the art of smart wearables in ergonomics, support the selection of the most suitable ones in industrial and non-industrial settings, and suggest future research directions. Full article

(This article belongs to the Special Issue Advances in Design and Integration of Wearable Sensors for Ergonomics)

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25 pages, 25970 KiB

Open AccessEditor’s ChoiceArticle

An Efficient Plaintext-Related Chaotic Image Encryption Scheme Based on Compressive Sensing

by Zhen Li, Changgen Peng, Weijie Tan and Liangrong Li

Sensors 2021, 21(3), 758; https://doi.org/10.3390/s21030758 - 23 Jan 2021

Cited by 28 | Viewed by 3554

Abstract

With the development of mobile communication network, especially 5G today and 6G in the future, the security and privacy of digital images are important in network applications. Meanwhile, high resolution images will take up a lot of bandwidth and storage space in the [...] Read more.

With the development of mobile communication network, especially 5G today and 6G in the future, the security and privacy of digital images are important in network applications. Meanwhile, high resolution images will take up a lot of bandwidth and storage space in the cloud applications. Facing the demands, an efficient and secure plaintext-related chaotic image encryption scheme is proposed based on compressive sensing for achieving the compression and encryption simultaneously. In the proposed scheme, the internal keys for controlling the whole process of compression and encryption is first generated by plain image and initial key. Subsequently, discrete wavelets transform is used in order to convert the plain image to the coefficient matrix. After that, the permutation processing, which is controlled by the two-dimensional Sine improved Logistic iterative chaotic map (2D-SLIM), was done on the coefficient matrix in order to make the matrix energy dispersive. Furthermore, a plaintext related compressive sensing has been done utilizing a measurement matrix generated by 2D-SLIM. In order to make the cipher image lower correlation and distribute uniform, measurement results quantified the 0∼255 and the permutation and diffusion operation is done under the controlling by two-dimensional Logistic-Sine-coupling map (2D-LSCM). Finally, some common compression and security performance analysis methods are used to test our scheme. The test and comparison results shown in our proposed scheme have both excellent security and compression performance when compared with other recent works, thus ensuring the digital image application in the network. Full article

(This article belongs to the Special Issue Machine Learning in Sensors and Imaging)

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23 pages, 7037 KiB

Open AccessEditor’s ChoiceArticle

Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

by Canh Nguyen, Vasit Sagan, Matthew Maimaitiyiming, Maitiniyazi Maimaitijiang, Sourav Bhadra and Misha T. Kwasniewski

Sensors 2021, 21(3), 742; https://doi.org/10.3390/s21030742 - 22 Jan 2021

Cited by 100 | Viewed by 12854

Abstract

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at [...] Read more.

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples. Full article

(This article belongs to the Section Sensing and Imaging)

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41 pages, 2176 KiB

Open AccessEditor’s ChoiceReview

Design Strategies for Electrochemical Aptasensors for Cancer Diagnostic Devices

by Kamila Malecka, Edyta Mikuła and Elena E. Ferapontova

Sensors 2021, 21(3), 736; https://doi.org/10.3390/s21030736 - 22 Jan 2021

Cited by 36 | Viewed by 6131

Abstract

Improved outcomes for many types of cancer achieved during recent years is due, among other factors, to the earlier detection of tumours and the greater availability of screening tests. With this, non-invasive, fast and accurate diagnostic devices for cancer diagnosis strongly improve the [...] Read more.

Improved outcomes for many types of cancer achieved during recent years is due, among other factors, to the earlier detection of tumours and the greater availability of screening tests. With this, non-invasive, fast and accurate diagnostic devices for cancer diagnosis strongly improve the quality of healthcare by delivering screening results in the most cost-effective and safe way. Biosensors for cancer diagnostics exploiting aptamers offer several important advantages over traditional antibodies-based assays, such as the in-vitro aptamer production, their inexpensive and easy chemical synthesis and modification, and excellent thermal stability. On the other hand, electrochemical biosensing approaches allow sensitive, accurate and inexpensive way of sensing, due to the rapid detection with lower costs, smaller equipment size and lower power requirements. This review presents an up-to-date assessment of the recent design strategies and analytical performance of the electrochemical aptamer-based biosensors for cancer diagnosis and their future perspectives in cancer diagnostics. Full article

(This article belongs to the Special Issue Electrochemical Aptamer-Based Biosensors)

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24 pages, 10566 KiB

Open AccessEditor’s ChoiceReview

A Review on Humidity, Temperature and Strain Printed Sensors—Current Trends and Future Perspectives

by Dimitris Barmpakos and Grigoris Kaltsas

Sensors 2021, 21(3), 739; https://doi.org/10.3390/s21030739 - 22 Jan 2021

Cited by 59 | Viewed by 7288

Abstract

Printing technologies have been attracting increasing interest in the manufacture of electronic devices and sensors. They offer a unique set of advantages such as additive material deposition and low to no material waste, digitally-controlled design and printing, elimination of multiple steps for device [...] Read more.

Printing technologies have been attracting increasing interest in the manufacture of electronic devices and sensors. They offer a unique set of advantages such as additive material deposition and low to no material waste, digitally-controlled design and printing, elimination of multiple steps for device manufacturing, wide material compatibility and large scale production to name but a few. Some of the most popular and interesting sensors are relative humidity, temperature and strain sensors. In that regard, this review analyzes the utilization and involvement of printing technologies for full or partial sensor manufacturing; production methods, material selection, sensing mechanisms and performance comparison are presented for each category, while grouping of sensor sub-categories is performed in all applicable cases. A key aim of this review is to provide a reference for sensor designers regarding all the aforementioned parameters, by highlighting strengths and weaknesses for different approaches in printed humidity, temperature and strain sensor manufacturing with printing technologies. Full article

(This article belongs to the Special Issue 2D/3D Printed Sensors and Electronics)

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14 pages, 1597 KiB

Open AccessEditor’s ChoiceArticle

IoT-Based Bee Swarm Activity Acoustic Classification Using Deep Neural Networks

by Andrej Zgank

Sensors 2021, 21(3), 676; https://doi.org/10.3390/s21030676 - 20 Jan 2021

Cited by 37 | Viewed by 4958

Abstract

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural [...] Read more.

Animal activity acoustic monitoring is becoming one of the necessary tools in agriculture, including beekeeping. It can assist in the control of beehives in remote locations. It is possible to classify bee swarm activity from audio signals using such approaches. A deep neural networks IoT-based acoustic swarm classification is proposed in this paper. Audio recordings were obtained from the Open Source Beehive project. Mel-frequency cepstral coefficients features were extracted from the audio signal. The lossless WAV and lossy MP3 audio formats were compared for IoT-based solutions. An analysis was made of the impact of the deep neural network parameters on the classification results. The best overall classification accuracy with uncompressed audio was 94.09%, but MP3 compression degraded the DNN accuracy by over 10%. The evaluation of the proposed deep neural networks IoT-based bee activity acoustic classification showed improved results if compared to the previous hidden Markov models system. Full article

(This article belongs to the Special Issue AI for IoT)

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25 pages, 11129 KiB

Open AccessEditor’s ChoiceArticle

Flexural Damage Diagnosis in Reinforced Concrete Beams Using a Wireless Admittance Monitoring System—Tests and Finite Element Analysis

by Constantin E. Chalioris, Violetta K. Kytinou, Maristella E. Voutetaki and Chris G. Karayannis

Sensors 2021, 21(3), 679; https://doi.org/10.3390/s21030679 - 20 Jan 2021

Cited by 60 | Viewed by 5603

Abstract

The utilization and effectiveness of a custom-made, portable and low-cost structural health monitoring (SHM) system that implements the PZT-based electro-mechanical admittance (EMA) methodology for the detection and evaluation of the damage of flexural reinforced concrete (RC) beams is presented. Tests of large-scale beams [...] Read more.

The utilization and effectiveness of a custom-made, portable and low-cost structural health monitoring (SHM) system that implements the PZT-based electro-mechanical admittance (EMA) methodology for the detection and evaluation of the damage of flexural reinforced concrete (RC) beams is presented. Tests of large-scale beams under monotonic and cyclic reversal-imposed deformations have been carried out using an integrated wireless impedance/admittance monitoring system (WiAMS) that employs the voltage measurements of PZT transducers. Small-sized PZT patches that have been epoxy-bonded on the steel bars surface and on the external concrete face of the beams are utilized to diagnose damages caused by steel yielding and concrete cracking. Excitations and simultaneous measurements of the voltage signal responses of the PZT transducers have been carried out at different levels of the applied load during the tests using the developed SHM devices, which are remotely controlled by a terminal emulator. Each PZT output voltage versus frequency response is transferred wireless and in real-time. Statistical index values are calculated based on the signals of the PZT transducers to represent the differences between their baseline response at the healthy state of the beam and their response at each loading/damage level. Finite Element Modeling (FEM) simulation of the tested beams has also been performed to acquire numerical results concerning the internal cracks, the steel strains and the energy dissipation and instability parameters. FEM analyses are used to verify the experimental results and to support the visual observations for a more precise damage evaluation. Findings of this study indicate that the proposed SHM system with the implementation of two different PZT transducer settings can be effectively utilized for the assessment of structural damage caused by concrete cracking and steel yielding in flexural beams under monotonic and cyclic loading. Full article

(This article belongs to the Special Issue Damage Detection of Structures Based on Piezoelectric Sensors)

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19 pages, 6261 KiB

Open AccessEditor’s ChoiceArticle

Hyperspectral Imagery for Assessing Laser-Induced Thermal State Change in Liver

by Martina De Landro, Ignacio Espíritu García-Molina, Manuel Barberio, Eric Felli, Vincent Agnus, Margherita Pizzicannella, Michele Diana, Emanuele Zappa and Paola Saccomandi

Sensors 2021, 21(2), 643; https://doi.org/10.3390/s21020643 - 18 Jan 2021

Cited by 20 | Viewed by 3310

Abstract

This work presents the potential of hyperspectral imaging (HSI) to monitor the thermal outcome of laser ablation therapy used for minimally invasive tumor removal. Our main goal is the establishment of indicators of the thermal damage of living tissues, which can be used [...] Read more.

This work presents the potential of hyperspectral imaging (HSI) to monitor the thermal outcome of laser ablation therapy used for minimally invasive tumor removal. Our main goal is the establishment of indicators of the thermal damage of living tissues, which can be used to assess the effect of the procedure. These indicators rely on the spectral variation of temperature-dependent tissue chromophores, i.e., oxyhemoglobin, deoxyhemoglobin, methemoglobin, and water. Laser treatment was performed at specific temperature thresholds (from 60 to 110 °C) on in-vivo animal liver and was assessed with a hyperspectral camera (500–995 nm) during and after the treatment. The indicators were extracted from the hyperspectral images after the following processing steps: the breathing motion compensation and the spectral and spatial filtering, the selection of spectral bands corresponding to specific tissue chromophores, and the analysis of the areas under the curves for each spectral band. Results show that properly combining spectral information related to deoxyhemoglobin, methemoglobin, lipids, and water allows for the segmenting of different zones of the laser-induced thermal damage. This preliminary investigation provides indicators for describing the thermal state of the liver, which can be employed in the future as clinical endpoints of the procedure outcome. Full article

(This article belongs to the Special Issue Trends and Prospects in Medical Hyperspectral Imagery)

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19 pages, 779 KiB

Open AccessEditor’s ChoiceArticle

Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning

by Luigi Borzì, Ivan Mazzetta, Alessandro Zampogna, Antonio Suppa, Gabriella Olmo and Fernanda Irrera

Sensors 2021, 21(2), 614; https://doi.org/10.3390/s21020614 - 17 Jan 2021

Cited by 64 | Viewed by 7149

Abstract

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published [...] Read more.

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-one-subject-out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness. Full article

(This article belongs to the Special Issue Wearable/Wireless Body Sensor Networks for Healthcare Applications)

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15 pages, 1300 KiB

Open AccessEditor’s ChoiceArticle

Using Wearable Sensor Technology to Measure Motion Complexity in Infants at High Familial Risk for Autism Spectrum Disorder

by Rujuta B. Wilson, Sitaram Vangala, David Elashoff, Tabitha Safari and Beth A. Smith

Sensors 2021, 21(2), 616; https://doi.org/10.3390/s21020616 - 17 Jan 2021

Cited by 32 | Viewed by 4156

Abstract

Background: Motor dysfunction has been reported as one of the first signs of atypical development in infants at high familial risk for autism spectrum disorder (ASD) (HR infants). However, studies have shown inconsistent results regarding the nature of motor dysfunction and whether it [...] Read more.

Background: Motor dysfunction has been reported as one of the first signs of atypical development in infants at high familial risk for autism spectrum disorder (ASD) (HR infants). However, studies have shown inconsistent results regarding the nature of motor dysfunction and whether it can be predictive of later ASD diagnosis. This is likely because current standardized motor assessments may not identify subtle and specific motor impairments that precede clinically observable motor dysfunction. Quantitative measures of motor development may address these limitations by providing objective evaluation of subtle motor differences in infancy. Methods: We used Opal wearable sensors to longitudinally evaluate full day motor activity in HR infants, and develop a measure of motion complexity. We focus on complexity of motion because optimal motion complexity is crucial to normal motor development and less complex behaviors might represent repetitive motor behaviors, a core diagnostic symptom of ASD. As proof of concept, the relationship of the motion complexity measure to developmental outcomes was examined in a small set of HR infants. Results: HR infants with a later diagnosis of ASD show lower motion complexity compared to those that do not. There is a stronger correlation between motion complexity and ASD outcome compared to outcomes of cognitive ability and adaptive skills. Conclusions: Objective measures of motor development are needed to identify characteristics of atypical infant motor function that are sensitive and specific markers of later ASD risk. Motion complexity could be used to track early infant motor development and to discriminate HR infants that go on to develop ASD. Full article

(This article belongs to the Section Wearables)

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25 pages, 7804 KiB

Open AccessEditor’s ChoiceArticle

Epidemic Analysis of Wireless Rechargeable Sensor Networks Based on an Attack–Defense Game Model

by Guiyun Liu, Baihao Peng and Xiaojing Zhong

Sensors 2021, 21(2), 594; https://doi.org/10.3390/s21020594 - 15 Jan 2021

Cited by 17 | Viewed by 2734

Abstract

Energy constraint hinders the popularization and development of wireless sensor networks (WSNs). As an emerging technology equipped with rechargeable batteries, wireless rechargeable sensor networks (WRSNs) are being widely accepted and recognized. In this paper, we research the security issues in WRSNs which need [...] Read more.

Energy constraint hinders the popularization and development of wireless sensor networks (WSNs). As an emerging technology equipped with rechargeable batteries, wireless rechargeable sensor networks (WRSNs) are being widely accepted and recognized. In this paper, we research the security issues in WRSNs which need to be addressed urgently. After considering the charging process, the activating anti-malware program process, and the launching malicious attack process in the modeling, the susceptible–infected–anti-malware–low-energy–susceptible (SIALS) model is proposed. Through the method of epidemic dynamics, this paper analyzes the local and global stabilities of the SIALS model. Besides, this paper introduces a five-tuple attack–defense game model to further study the dynamic relationship between malware and WRSNs. By introducing a cost function and constructing a Hamiltonian function, the optimal strategies for malware and WRSNs are obtained based on the Pontryagin Maximum Principle. Furthermore, the simulation results show the validation of the proposed theories and reveal the influence of parameters on the infection. In detail, the Forward–Backward Sweep method is applied to solve the issues of convergence of co-state variables at terminal moment. Full article

(This article belongs to the Special Issue Security and Privacy in the Internet of Things (IoT))

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18 pages, 13311 KiB

Open AccessEditor’s ChoiceArticle

Intelligent Fault Diagnosis of Hydraulic Piston Pump Based on Wavelet Analysis and Improved AlexNet

by Yong Zhu, Guangpeng Li, Rui Wang, Shengnan Tang, Hong Su and Kai Cao

Sensors 2021, 21(2), 549; https://doi.org/10.3390/s21020549 - 14 Jan 2021

Cited by 39 | Viewed by 3987

Abstract

Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods [...] Read more.

Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust. Full article

(This article belongs to the Special Issue Vibration Sensor-Based Diagnosis Technologies and Systems: Part Ⅰ )

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27 pages, 29319 KiB

Open AccessEditor’s ChoiceArticle

Development and Implementation of a Hybrid Wireless Sensor Network of Low Power and Long Range for Urban Environments

by Juan Bravo-Arrabal, J. J. Fernandez-Lozano, Javier Serón, Jose Antonio Gomez-Ruiz and Alfonso García-Cerezo

Sensors 2021, 21(2), 567; https://doi.org/10.3390/s21020567 - 14 Jan 2021

Cited by 25 | Viewed by 4461

Abstract

The urban population, worldwide, is growing exponentially and with it the demand for information on pollution levels, vehicle traffic, or available parking, giving rise to citizens connected to their environment. This article presents an experimental long range (LoRa) and low power consumption network, [...] Read more.

The urban population, worldwide, is growing exponentially and with it the demand for information on pollution levels, vehicle traffic, or available parking, giving rise to citizens connected to their environment. This article presents an experimental long range (LoRa) and low power consumption network, with a combination of static and mobile wireless sensors (hybrid architecture) to tune and validate concentrator placement, to obtain a large coverage in an urban environment. A mobile node has been used, carrying a gateway and various sensors. The Activation By Personalization (ABP) mode has been used, justified for urban applications requiring multicasting. This allows to compare the coverage of each static gateway, being able to make practical decisions about its location. With this methodology, it has been possible to provide service to the city of Malaga, through a single concentrator node. The information acquired is synchronized in an external database, to monitor the data in real time, being able to geolocate the dataframes through web mapping services. This work presents the development and implementation of a hybrid wireless sensor network of long range and low power, configured and tuned to achieve efficient performance in a mid-size city, and tested in experiments in a real urban environment. Full article

(This article belongs to the Special Issue LoRa Sensor Network)

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13 pages, 4830 KiB

Open AccessEditor’s ChoiceArticle

A Convolutional Neural Network-Based Method for Corn Stand Counting in the Field

by Le Wang, Lirong Xiang, Lie Tang and Huanyu Jiang

Sensors 2021, 21(2), 507; https://doi.org/10.3390/s21020507 - 13 Jan 2021

Cited by 36 | Viewed by 3917

Abstract

Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be [...] Read more.

Accurate corn stand count in the field at early season is of great interest to corn breeders and plant geneticists. However, the commonly used manual counting method is time consuming, laborious, and prone to error. Nowadays, unmanned aerial vehicles (UAV) tend to be a popular base for plant-image-collecting platforms. However, detecting corn stands in the field is a challenging task, primarily because of camera motion, leaf fluttering caused by wind, shadows of plants caused by direct sunlight, and the complex soil background. As for the UAV system, there are mainly two limitations for early seedling detection and counting. First, flying height cannot ensure a high resolution for small objects. It is especially difficult to detect early corn seedlings at around one week after planting, because the plants are small and difficult to differentiate from the background. Second, the battery life and payload of UAV systems cannot support long-duration online counting work. In this research project, we developed an automated, robust, and high-throughput method for corn stand counting based on color images extracted from video clips. A pipeline developed based on the YoloV3 network and Kalman filter was used to count corn seedlings online. The results demonstrate that our method is accurate and reliable for stand counting, achieving an accuracy of over 98% at growth stages V2 and V3 (vegetative stages with two and three visible collars) with an average frame rate of 47 frames per second (FPS). This pipeline can also be mounted easily on manned cart, tractor, or field robotic systems for online corn counting. Full article

(This article belongs to the Special Issue Advances in Hyperspectral Imaging, Sensing and Its Applications: Precision Agriculture and Fire Prevention)

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16 pages, 3459 KiB

Open AccessEditor’s ChoiceArticle

Reliable Industry 4.0 Based on Machine Learning and IoT for Analyzing, Monitoring, and Securing Smart Meters

by Mahmoud Elsisi, Karar Mahmoud, Matti Lehtonen and Mohamed M. F. Darwish

Sensors 2021, 21(2), 487; https://doi.org/10.3390/s21020487 - 12 Jan 2021

Cited by 78 | Viewed by 7025

Abstract

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and [...] Read more.

The modern control infrastructure that manages and monitors the communication between the smart machines represents the most effective way to increase the efficiency of the industrial environment, such as smart grids. The cyber-physical systems utilize the embedded software and internet to connect and control the smart machines that are addressed by the internet of things (IoT). These cyber-physical systems are the basis of the fourth industrial revolution which is indexed by industry 4.0. In particular, industry 4.0 relies heavily on the IoT and smart sensors such as smart energy meters. The reliability and security represent the main challenges that face the industry 4.0 implementation. This paper introduces a new infrastructure based on machine learning to analyze and monitor the output data of the smart meters to investigate if this data is real data or fake. The fake data are due to the hacking and the inefficient meters. The industrial environment affects the efficiency of the meters by temperature, humidity, and noise signals. Furthermore, the proposed infrastructure validates the amount of data loss via communication channels and the internet connection. The decision tree is utilized as an effective machine learning algorithm to carry out both regression and classification for the meters’ data. The data monitoring is carried based on the industrial digital twins’ platform. The proposed infrastructure results provide a reliable and effective industrial decision that enhances the investments in industry 4.0. Full article

(This article belongs to the Section Intelligent Sensors)

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18 pages, 2017 KiB

Open AccessEditor’s ChoiceArticle

Standardized Extraction Techniques for Meat Analysis with the Electronic Tongue: A Case Study of Poultry and Red Meat Adulteration

by John-Lewis Zinia Zaukuu, Zoltan Gillay and Zoltan Kovacs

Sensors 2021, 21(2), 481; https://doi.org/10.3390/s21020481 - 12 Jan 2021

Cited by 21 | Viewed by 3511

Abstract

The electronic tongue (e-tongue) is an advanced sensor-based device capable of detecting low concentration differences in solutions. It could have unparalleled advantages for meat quality control, but the challenges of standardized meat extraction methods represent a backdrop that has led to its scanty [...] Read more.

The electronic tongue (e-tongue) is an advanced sensor-based device capable of detecting low concentration differences in solutions. It could have unparalleled advantages for meat quality control, but the challenges of standardized meat extraction methods represent a backdrop that has led to its scanty application in the meat industry. This study aimed to determine the optimal dilution level of meat extract for e-tongue evaluations and also to develop three standardized meat extraction methods. For practicality, the developed methods were applied to detect low levels of meat adulteration using beef and pork mixtures and turkey and chicken mixtures as case studies. Dilution factor of 1% w/v of liquid meat extract was determined to be the optimum for discriminating 1% w/w, 3% w/w, 5% w/w, 10% w/w, and 20% w/w chicken in turkey and pork in beef with linear discriminant analysis accuracies (LDA) of 78.13% (recognition) and 64.73% (validation). Even higher LDA accuracies of 89.62% (recognition) and 68.77% (validation) were achieved for discriminating 1% w/w, 3% w/w, 5% w/w, 10% w/w, and 20% w/w of pork in beef. Partial least square models could predict both sets of meat mixtures with good accuracies. Extraction by cooking was the best method for discriminating meat mixtures and can be applied for meat quality evaluations with the e-tongue. Full article

(This article belongs to the Special Issue Breakthroughs and Challenges in Food Quality Assessment Using Advanced Rapid Techniques: Electronic Tongue, Electronic Nose, and Near-Infrared Spectroscopy)

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22 pages, 10507 KiB

Open AccessEditor’s ChoiceArticle

Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning

by Hammam Alshazly, Christoph Linse, Erhardt Barth and Thomas Martinetz

Sensors 2021, 21(2), 455; https://doi.org/10.3390/s21020455 - 11 Jan 2021

Cited by 149 | Viewed by 8573

Abstract

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored [...] Read more.

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of

99.4 %

,

99.6 %

,

99.8 %

,

99.6 %

, and

99.4 %

on the SARS-CoV-2 dataset, and

92.9 %

,

91.3 %

,

93.7 %

,

92.2 %

, and

92.5 %

on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models’ predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists. Full article

(This article belongs to the Section Intelligent Sensors)

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18 pages, 7201 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of Coating Thickness Using Lift-Off Insensitivity of Eddy Current Sensor

by Xiaobai Meng, Mingyang Lu, Wuliang Yin, Abdeldjalil Bennecer and Katherine J. Kirk

Sensors 2021, 21(2), 419; https://doi.org/10.3390/s21020419 - 9 Jan 2021

Cited by 21 | Viewed by 3925

Abstract

Defect detection in ferromagnetic substrates is often hampered by nonmagnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of nonmagnetic coatings on [...] Read more.

Defect detection in ferromagnetic substrates is often hampered by nonmagnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of nonmagnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multifrequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3% for an extended lift-off range of up to 10 mm. Full article

(This article belongs to the Section Electronic Sensors)

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10 pages, 2116 KiB

Open AccessEditor’s ChoiceCommunication

Soft Wireless Bioelectronics and Differential Electrodermal Activity for Home Sleep Monitoring

by Hojoong Kim, Shinjae Kwon, Young-Tae Kwon and Woon-Hong Yeo

Sensors 2021, 21(2), 354; https://doi.org/10.3390/s21020354 - 7 Jan 2021

Cited by 23 | Viewed by 5162

Abstract

Sleep is an essential element to human life, restoring the brain and body from accumulated fatigue from daily activities. Quantitative monitoring of daily sleep quality can provide critical feedback to evaluate human health and life patterns. However, the existing sleep assessment system using [...] Read more.

Sleep is an essential element to human life, restoring the brain and body from accumulated fatigue from daily activities. Quantitative monitoring of daily sleep quality can provide critical feedback to evaluate human health and life patterns. However, the existing sleep assessment system using polysomnography is not available for a home sleep evaluation, while it requires multiple sensors, tabletop electronics, and sleep specialists. More importantly, the mandatory sleep in a designated lab facility disrupts a subject’s regular sleep pattern, which does not capture one’s everyday sleep behaviors. Recent studies report that galvanic skin response (GSR) measured on the skin can be one indicator to evaluate the sleep quality daily at home. However, the available GSR detection devices require rigid sensors wrapped on fingers along with separate electronic components for data acquisition, which can interrupt the normal sleep conditions. Here, we report a new class of materials, sensors, electronics, and packaging technologies to develop a wireless, soft electronic system that can measure GSR on the wrist. The single device platform that avoids wires, rigid sensors, and straps offers the maximum comfort to wear on the skin and minimize disruption of a subject’s sleep. A nanomaterial GSR sensor, printed on a soft elastomeric membrane, can have intimate contact with the skin to reduce motion artifact during sleep. A multi-layered flexible circuit mounted on top of the sensor provides a wireless, continuous, real-time recording of GSR to classify sleep stages, validated by the direct comparison with the standard method that measures other physiological signals. Collectively, the soft bioelectronic system shows great potential to be working as a portable, at-home sensor system for assessing sleep quality before a hospital visit. Full article

(This article belongs to the Section Biomedical Sensors)

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