Sensors

57 pages, 6039 KiB

Open AccessEditor’s ChoiceReview

Breath Analysis: A Promising Tool for Disease Diagnosis—The Role of Sensors

by Maria Kaloumenou, Evangelos Skotadis, Nefeli Lagopati, Efstathios Efstathopoulos and Dimitris Tsoukalas

Sensors 2022, 22(3), 1238; https://doi.org/10.3390/s22031238 - 6 Feb 2022

Cited by 50 | Viewed by 11614

Abstract

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive [...] Read more.

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field. Full article

(This article belongs to the Special Issue Immunoassays and Biosensors)

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25 pages, 8093 KiB

Open AccessEditor’s ChoiceArticle

Cuffless Blood Pressure Estimation Based on Monte Carlo Simulation Using Photoplethysmography Signals

by Chowdhury Azimul Haque, Tae-Ho Kwon and Ki-Doo Kim

Sensors 2022, 22(3), 1175; https://doi.org/10.3390/s22031175 - 4 Feb 2022

Cited by 10 | Viewed by 3316

Abstract

Blood pressure measurements are one of the most routinely performed medical tests globally. Blood pressure is an important metric since it provides information that can be used to diagnose several vascular diseases. Conventional blood pressure measurement systems use cuff-based devices to measure the [...] Read more.

Blood pressure measurements are one of the most routinely performed medical tests globally. Blood pressure is an important metric since it provides information that can be used to diagnose several vascular diseases. Conventional blood pressure measurement systems use cuff-based devices to measure the blood pressure, which may be uncomfortable and sometimes burdensome to the subjects. Therefore, in this study, we propose a cuffless blood pressure estimation model based on Monte Carlo simulation (MCS). We propose a heterogeneous finger model for the MCS at wavelengths of 905 nm and 940 nm. After recording the photon intensities from the MCS over a certain range of blood pressure values, the actual photoplethysmography (PPG) signals were used to estimate blood pressure. We used both publicly available and self-made datasets to evaluate the performance of the proposed model. In case of the publicly available dataset for transmission-type MCS, the mean absolute errors are 3.32 ± 6.03 mmHg for systolic blood pressure (SBP), 2.02 ± 2.64 mmHg for diastolic blood pressure (DBP), and 1.76 ± 2.8 mmHg for mean arterial pressure (MAP). The self-made dataset is used for both transmission- and reflection-type MCSs; its mean absolute errors are 2.54 ± 4.24 mmHg for SBP, 1.49 ± 2.82 mmHg for DBP, and 1.51 ± 2.41 mmHg for MAP in the transmission-type case as well as 3.35 ± 5.06 mmHg for SBP, 2.07 ± 2.83 mmHg for DBP, and 2.12 ± 2.83 mmHg for MAP in the reflection-type case. The estimated results of the SBP and DBP satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standards and are within Grade A according to the British Hypertension Society (BHS) standards. These results show that the proposed model is efficient for estimating blood pressures using fingertip PPG signals. Full article

(This article belongs to the Section Biomedical Sensors)

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10 pages, 2988 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Flexible Inkjet-Printed Heaters Utilizing Graphene-Based Inks

by Dimitris Barmpakos, Vassiliki Belessi, Nikolaos Xanthopoulos, Christoforos A. Krontiras and Grigoris Kaltsas

Sensors 2022, 22(3), 1173; https://doi.org/10.3390/s22031173 - 3 Feb 2022

Cited by 13 | Viewed by 3048

Abstract

Thermal sensors are mainly based on the selective heating of specific areas, which in most cases is a critical feature for both the operation and the performance of the thermal device. In this work, we evaluate the thermoelectrical response of two graphitic materials, [...] Read more.

Thermal sensors are mainly based on the selective heating of specific areas, which in most cases is a critical feature for both the operation and the performance of the thermal device. In this work, we evaluate the thermoelectrical response of two graphitic materials, namely (a) a commercial 2.4%wt graphene–ethyl cellulose dispersion in cycloxehanone and terpineol (G) and (b) a custom functionalized reduced graphene oxide (f-rGO) ink in the range of −40 to 100 °C. Both inks were printed on a flexible polyimide substrate and the Thermal Coefficients of Resistance (TCR) were extracted as TCR_G = −1.05 × 10⁻³ °C⁻¹ (R² = 0.9938) and TCR_f-_rGO = −3.86 × 10⁻³ °C⁻¹ (R² = 0.9967). Afterward, the inkjet-printed devices were evaluated as microheaters, in order to exploit their advantage for cost-effective production with minimal material waste. f-rGO and G printed heaters reached a maximum temperature of 97.5 °C at 242 mW and 89.9 °C at 314 mW, respectively, applied by a constant current source and monitored by an infrared camera. Repeatability experiments were conducted, highlighting the high robustness in long-term use. The power–temperature behavior was extracted by self-heating experiments to demonstrate the ability of the devices to serve as heaters. Both static and dynamic evaluation were performed in order to study the device behaviors and extract the corresponding parameters. After all the experimental processes, the resistance of the samples was again evaluated and found to differ less than 13% from the initial value. In this work, fabrication via inkjet printing and demonstration of efficient and stable microheaters utilizing a custom ink (f-rGO) and a commercial graphene ink are presented. This approach is suitable for fabricating selectively heated geometries on non-planar substrate with high repeatability and endurance in heat cycles. Full article

(This article belongs to the Section Electronic Sensors)

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15 pages, 4698 KiB

Open AccessEditor’s ChoiceArticle

Quantitative Evaluation System of Upper Limb Motor Function of Stroke Patients Based on Desktop Rehabilitation Robot

by Mingliang Zhang, Jing Chen, Zongquan Ling, Bochao Zhang, Yanxin Yan, Daxi Xiong and Liquan Guo

Sensors 2022, 22(3), 1170; https://doi.org/10.3390/s22031170 - 3 Feb 2022

Cited by 16 | Viewed by 3635

Abstract

Rehabilitation training and movement evaluation after stroke have become a research hotspot as stroke has become a very common and harmful disease. However, traditional rehabilitation training and evaluation are mainly conducted under the guidance of rehabilitation doctors. The evaluation process is time-consuming and [...] Read more.

Rehabilitation training and movement evaluation after stroke have become a research hotspot as stroke has become a very common and harmful disease. However, traditional rehabilitation training and evaluation are mainly conducted under the guidance of rehabilitation doctors. The evaluation process is time-consuming and the evaluation results are greatly influenced by doctors. In this study, a desktop upper limb rehabilitation robot was designed and a quantitative evaluation system of upper limb motor function for stroke patients was proposed. The kinematics and dynamics data of stroke patients during active training were collected by sensors. Combined with the scores of patients’ upper limb motor function by rehabilitation doctors using the Wolf Motor Function Test (WMFT) scale, three different quantitative evaluation models of upper limb motor function based on Back Propagation Neural Network (BPNN), K-Nearest Neighbors (KNN), and Support Vector Regression (SVR) algorithms were established. To verify the effectiveness of the quantitative evaluation system, 10 healthy subjects and 21 stroke patients were recruited for experiments. The experimental results show that the BPNN model has the best evaluation performance among the three quantitative evaluation models. The scoring accuracy of the BPNN model reached up to 87.1%. Moreover, there was a significant correlation between the models′ scores and the doctors′ scores. The proposed system can help doctors to quantitatively evaluate the upper limb motor function of stroke patients and accurately master the rehabilitation progress of patients. Full article

(This article belongs to the Special Issue Rehabilitation Robots and Sensors)

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14 pages, 3971 KiB

Open AccessEditor’s ChoiceArticle

Connected Vehicles: V2V and V2I Road Weather and Traffic Communication Using Cellular Technologies

by Muhammad Naeem Tahir, Pekka Leviäkangas and Marcos Katz

Sensors 2022, 22(3), 1142; https://doi.org/10.3390/s22031142 - 2 Feb 2022

Cited by 45 | Viewed by 12221

Abstract

There is a continuous need to design and develop wireless technologies to meet the increasing demands for high-speed wireless data transfer to incorporate advanced intelligent transport systems. Different wireless technologies are continuously evolving including short-range and long-range (WiMAX, LTE, and 5G) cellular standards. [...] Read more.

There is a continuous need to design and develop wireless technologies to meet the increasing demands for high-speed wireless data transfer to incorporate advanced intelligent transport systems. Different wireless technologies are continuously evolving including short-range and long-range (WiMAX, LTE, and 5G) cellular standards. These emerging technologies can considerably enhance the operational performance of communication between vehicles and road-side infrastructure. This paper analyzes the performance of cellular-based long-term evolution (LTE) and 5GTN (5G Test Network) in pilot field measurements (i.e., vehicle-to-vehicle and vehicle-to-infrastructure) when delivering road weather and traffic information in real-time environments. Measurements were conducted on a test track operated and owned by the Finnish Meteorological Institute (FMI), Finland. The results showed that 5GTN outperformed LTE when exchanging road weather and traffic data messages in V2V and V2I scenarios. This comparison was made by mainly considering bandwidth, throughput, packet loss, and latency. The safety critical messages were transmitted at a transmission frequency of 10 Hz. The performance of both compared technologies (i.e., LTE and 5GTN) fulfilled the minimum requirements of the ITS-Assisted Road weather and traffic platform to offer reliable communication for enhanced road traffic safety. The field measurement results also illustrate the advantage of cellular networks (LTE and 5GTN) with a clear potential to use it heterogeneously in future field tests with short-range protocols, e.g., IEEE 802.11p. Full article

(This article belongs to the Special Issue Vehicle-to-Everything (V2X) Communications)

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15 pages, 3630 KiB

Open AccessEditor’s ChoiceArticle

Pre-Anodized Graphite Pencil Electrode Coated with a Poly(Thionine) Film for Simultaneous Sensing of 3-Nitrophenol and 4-Nitrophenol in Environmental Water Samples

by Vijaya Gopalan Sree, Jung Inn Sohn and Hyunsik Im

Sensors 2022, 22(3), 1151; https://doi.org/10.3390/s22031151 - 2 Feb 2022

Cited by 14 | Viewed by 2453

Abstract

A very simple, as well as sensitive and selective, sensing protocol was developed on a pre-anodized graphite pencil electrode surface coated using poly(thionine) (APGE/PTH). The poly(thionine) coated graphite pencil was then used for simultaneous sensing of 3-nitrophenol (3-NP) and 4-nitrophenol (4-NP). The poly(thionine) [...] Read more.

A very simple, as well as sensitive and selective, sensing protocol was developed on a pre-anodized graphite pencil electrode surface coated using poly(thionine) (APGE/PTH). The poly(thionine) coated graphite pencil was then used for simultaneous sensing of 3-nitrophenol (3-NP) and 4-nitrophenol (4-NP). The poly(thionine) coated electrode exhibited an enhanced electrocatalytic property towards nitrophenol (3-NP and 4-NP) reduction. Redox peak potential and current of both nitrophenols were found well resolved and their simultaneous analysis was studied. Under optimized experimental conditions, APGE/PTH showed a long linear concentration range from 20 to 230 nM and 15 nM to 280 nM with a calculated limit of detection (LOD) of 4.5 and 4 nM and a sensitivity of 22.45 µA/nM and 27.12 µA/nM for 3-NP and 4-NP, respectively. Real sample analysis using the prepared sensor was tested with different environmental water samples and the sensors exhibited excellent recovery results in the range from 98.16 to 103.43%. Finally, the sensor exposed an promising selectivity, stability, and reproducibility towards sensing of 3-NP and 4-NP. Full article

(This article belongs to the Special Issue Electrochemical Sensors and Biosensors for Rapid Trace Analysis of Pollutants and Contaminants)

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17 pages, 906 KiB

Open AccessEditor’s ChoiceArticle

DoSGuard: Mitigating Denial-of-Service Attacks in Software-Defined Networks

by Jishuai Li, Tengfei Tu, Yongsheng Li, Sujuan Qin, Yijie Shi and Qiaoyan Wen

Sensors 2022, 22(3), 1061; https://doi.org/10.3390/s22031061 - 29 Jan 2022

Cited by 13 | Viewed by 2451

Abstract

Software-defined networking (SDN) is a new networking paradigm that realizes the fast management and optimal configuration of network resources by decoupling control logic and forwarding functions. However, centralized network architecture brings new security problems, and denial-of-service (DoS) attacks are among the most critical [...] Read more.

Software-defined networking (SDN) is a new networking paradigm that realizes the fast management and optimal configuration of network resources by decoupling control logic and forwarding functions. However, centralized network architecture brings new security problems, and denial-of-service (DoS) attacks are among the most critical threats. Due to the lack of an effective message-verification mechanism in SDN, attackers can easily launch a DoS attack by faking the source address information. This paper presents DoSGuard, an efficient and protocol-independent defense framework for SDN networks to detect and mitigate such attacks. DoSGuard is a lightweight extension module on SDN controllers that mainly consists of three key components: a monitor, a detector, and a mitigator. The monitor maintains the information between the switches and the hosts for anomaly detection. The detector utilizes OpenFlow message and flow features to detect the attack. The mitigator protects networks by filtering malicious packets. We implement a prototype of DoSGuard in the floodlight controller and evaluate its effectiveness in a simulation environment. Experimental results show the DoSGuard achieves 98.72% detecion precision, and the average CPU utilization of the controller is only around 8%. The results demonstrate that DoSGuard can effectively mitigate DoS attacks against SDN with limited overhead. Full article

(This article belongs to the Section Sensor Networks)

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45 pages, 3862 KiB

Open AccessEditor’s ChoiceReview

Quantification of Movement in Stroke Patients under Free Living Conditions Using Wearable Sensors: A Systematic Review

by Mariano Bernaldo de Quirós, E.H. Douma, Inge van den Akker-Scheek, Claudine J. C. Lamoth and Natasha M. Maurits

Sensors 2022, 22(3), 1050; https://doi.org/10.3390/s22031050 - 28 Jan 2022

Cited by 9 | Viewed by 4515

Abstract

Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of [...] Read more.

Stroke is a main cause of long-term disability worldwide, placing a large burden on individuals and health care systems. Wearable technology can potentially objectively assess and monitor patients outside clinical environments, enabling a more detailed evaluation of their impairment and allowing individualization of rehabilitation therapies. The aim of this review is to provide an overview of setups used in literature to measure movement of stroke patients under free living conditions using wearable sensors, and to evaluate the relation between such sensor-based outcomes and the level of functioning as assessed by existing clinical evaluation methods. After a systematic search we included 32 articles, totaling 1076 stroke patients from acute to chronic phases and 236 healthy controls. We summarized the results by type and location of sensors, and by sensor-based outcome measures and their relation with existing clinical evaluation tools. We conclude that sensor-based measures of movement provide additional information in relation to clinical evaluation tools assessing motor functioning and both are needed to gain better insight in patient behavior and recovery. However, there is a strong need for standardization and consensus, regarding clinical assessments, but also regarding the use of specific algorithms and metrics for unsupervised measurements during daily life. Full article

(This article belongs to the Special Issue Intelligent Systems for Clinical Care and Remote Patient Monitoring)

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28 pages, 2715 KiB

Open AccessEditor’s ChoiceArticle

Quantitative Evaluation for Magnetoelectric Sensor Systems in Biomagnetic Diagnostics

by Eric Elzenheimer, Christin Bald, Erik Engelhardt, Johannes Hoffmann, Patrick Hayes, Johan Arbustini, Andreas Bahr, Eckhard Quandt, Michael Höft and Gerhard Schmidt

Sensors 2022, 22(3), 1018; https://doi.org/10.3390/s22031018 - 28 Jan 2022

Cited by 30 | Viewed by 4022

Abstract

Dedicated research is currently being conducted on novel thin film magnetoelectric (ME) sensor concepts for medical applications. These concepts enable a contactless magnetic signal acquisition in the presence of large interference fields such as the magnetic field of the Earth and are operational [...] Read more.

Dedicated research is currently being conducted on novel thin film magnetoelectric (ME) sensor concepts for medical applications. These concepts enable a contactless magnetic signal acquisition in the presence of large interference fields such as the magnetic field of the Earth and are operational at room temperature. As more and more different ME sensor concepts are accessible to medical applications, the need for comparative quality metrics significantly arises. For a medical application, both the specification of the sensor itself and the specification of the readout scheme must be considered. Therefore, from a medical user’s perspective, a system consideration is better suited to specific quantitative measures that consider the sensor readout scheme as well. The corresponding sensor system evaluation should be performed in reproducible measurement conditions (e.g., magnetically, electrically and acoustically shielded environment). Within this contribution, an ME sensor system evaluation scheme will be described and discussed. The quantitative measures will be determined exemplarily for two ME sensors: a resonant ME sensor and an electrically modulated ME sensor. In addition, an application-related signal evaluation scheme will be introduced and exemplified for cardiovascular application. The utilized prototype signal is based on a magnetocardiogram (MCG), which was recorded with a superconducting quantum-interference device. As a potential figure of merit for a quantitative signal assessment, an application specific capacity (ASC) is introduced. In conclusion, this contribution highlights metrics for the quantitative characterization of ME sensor systems and their resulting output signals in biomagnetism. Finally, different ASC values and signal-to-noise ratios (SNRs) could be clearly presented for the resonant ME sensor (SNR:

- 90

dB, ASC:

9.8 \times 10^{- 7}

dB Hz) and also the electrically modulated ME sensor (SNR:

- 11

dB, ASC: 23 dB Hz), showing that the electrically modulated ME sensor is better suited for a possible MCG application under ideal conditions. The presented approach is transferable to other magnetic sensors and applications. Full article

(This article belongs to the Special Issue Magnetoelectric Sensor Systems and Applications)

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26 pages, 14121 KiB

Open AccessEditor’s ChoiceArticle

Deep Learning Empowered Wearable-Based Behavior Recognition for Search and Rescue Dogs

by Panagiotis Kasnesis, Vasileios Doulgerakis, Dimitris Uzunidis, Dimitris G. Kogias, Susana I. Funcia, Marta B. González, Christos Giannousis and Charalampos Z. Patrikakis

Sensors 2022, 22(3), 993; https://doi.org/10.3390/s22030993 - 27 Jan 2022

Cited by 21 | Viewed by 5755

Abstract

Search and Rescue (SaR) dogs are important assets in the hands of first responders, as they have the ability to locate the victim even in cases where the vision and or the sound is limited, due to their inherent talents in olfactory and [...] Read more.

Search and Rescue (SaR) dogs are important assets in the hands of first responders, as they have the ability to locate the victim even in cases where the vision and or the sound is limited, due to their inherent talents in olfactory and auditory senses. In this work, we propose a deep-learning-assisted implementation incorporating a wearable device, a base station, a mobile application, and a cloud-based infrastructure that can first monitor in real-time the activity, the audio signals, and the location of a SaR dog, and second, recognize and alert the rescuing team whenever the SaR dog spots a victim. For this purpose, we employed deep Convolutional Neural Networks (CNN) both for the activity recognition and the sound classification, which are trained using data from inertial sensors, such as 3-axial accelerometer and gyroscope and from the wearable’s microphone, respectively. The developed deep learning models were deployed on the wearable device, while the overall proposed implementation was validated in two discrete search and rescue scenarios, managing to successfully spot the victim (i.e., obtained F1-score more than 99%) and inform the rescue team in real-time for both scenarios. Full article

(This article belongs to the Special Issue Artificial Neural Networks for IoT-Enabled Smart Applications)

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14 pages, 35197 KiB

Open AccessEditor’s ChoiceReview

Wearable Sensing Systems for Monitoring Mental Health

by Mijeong Kang and Kyunghwan Chai

Sensors 2022, 22(3), 994; https://doi.org/10.3390/s22030994 - 27 Jan 2022

Cited by 19 | Viewed by 9016

Abstract

Wearable systems for monitoring biological signals have opened the door to personalized healthcare and have advanced a great deal over the past decade with the development of flexible electronics, efficient energy storage, wireless data transmission, and information processing technologies. As there are cumulative [...] Read more.

Wearable systems for monitoring biological signals have opened the door to personalized healthcare and have advanced a great deal over the past decade with the development of flexible electronics, efficient energy storage, wireless data transmission, and information processing technologies. As there are cumulative understanding of mechanisms underlying the mental processes and increasing desire for lifetime mental wellbeing, various wearable sensors have been devised to monitor the mental status from physiological activities, physical movements, and biochemical profiles in body fluids. This review summarizes the recent progress in wearable healthcare monitoring systems that can be utilized in mental healthcare, especially focusing on the biochemical sensors (i.e., biomarkers associated with mental status, sensing modalities, and device materials) and discussing their promises and challenges. Full article

(This article belongs to the Special Issue Flexible Biosensors Based on Material Science and Their Healthcare Applications)

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19 pages, 4064 KiB

Open AccessEditor’s ChoiceArticle

Linear Interval Approximation for Smart Sensors and IoT Devices

by Marin B. Marinov, Nikolay Nikolov, Slav Dimitrov, Todor Todorov, Yana Stoyanova and Georgi T. Nikolov

Sensors 2022, 22(3), 949; https://doi.org/10.3390/s22030949 - 26 Jan 2022

Cited by 17 | Viewed by 3059

Abstract

In this work, we introduce and use an innovative approach for adaptive piecewise linear interval approximation of sensor characteristics, which are differentiable functions. The aim is to obtain a discreet type of inverse sensor characteristic, with a predefined maximum approximation error, with minimization [...] Read more.

In this work, we introduce and use an innovative approach for adaptive piecewise linear interval approximation of sensor characteristics, which are differentiable functions. The aim is to obtain a discreet type of inverse sensor characteristic, with a predefined maximum approximation error, with minimization of the number of points defining the characteristic, which in turn is related to the possibilities for using microcontrollers with limited energy and memory resources. In this context, the results from the study indicate that to overcome the problems arising from the resource constraints of smart devices, appropriate “lightweight” algorithms are needed that allow efficient connectivity and intelligent management of the measurement processes. The method has two benefits: first, low-cost microcontrollers could be used for hardware implementation of the industrial sensor devices; second, the optimal subdivision of the measurement range reduces the space in the memory of the microcontroller necessary for storage of the parameters of the linearized characteristic. Although the discussed computational examples are aimed at building adaptive approximations for temperature sensors, the algorithm can easily be extended to many other sensor types and can improve the performance of resource-constrained devices. For prescribed maximum approximation error, the inverse sensor characteristic is found directly in the linearized form. Further advantages of the proposed approach are: (i) the maximum error under linearization of the inverse sensor characteristic at all intervals, except in the general case of the last one, is the same; (ii) the approach allows non-uniform distribution of maximum approximation error, i.e., different maximum approximation errors could be assigned to particular intervals; (iii) the approach allows the application to the general type of differentiable sensor characteristics with piecewise concave/convex properties. Full article

(This article belongs to the Section Internet of Things)

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19 pages, 7338 KiB

Open AccessEditor’s ChoiceArticle

Kinetic Electromagnetic Energy Harvester for Railway Applications—Development and Test with Wireless Sensor

by Zdenek Hadas, Ondrej Rubes, Filip Ksica and Jan Chalupa

Sensors 2022, 22(3), 905; https://doi.org/10.3390/s22030905 - 25 Jan 2022

Cited by 12 | Viewed by 3710

Abstract

This paper deals with a development and lab testing of energy harvesting technology for autonomous sensing in railway applications. Moving trains are subjected to high levels of vibrations and rail deformations that could be converted via energy harvesting into useful electricity. Modern maintenance [...] Read more.

This paper deals with a development and lab testing of energy harvesting technology for autonomous sensing in railway applications. Moving trains are subjected to high levels of vibrations and rail deformations that could be converted via energy harvesting into useful electricity. Modern maintenance solutions of a rail trackside typically consist of a large number of integrated sensing systems, which greatly benefit from autonomous source of energy. Although the amount of energy provided by conventional energy harvesting devices is usually only around several milliwatts, it is sufficient as a source of electrical power for low power sensing devices. The main aim of this paper is to design and test a kinetic electromagnetic energy harvesting system that could use energy from a passing train to deliver sufficient electrical power for sensing nodes. Measured mechanical vibrations of regional and express trains were used in laboratory testing of the developed energy harvesting device with an integrated resistive load and wireless transmission system, and based on these tests the proposed technology shows a high potential for railway applications. Full article

(This article belongs to the Special Issue Vibration Energy Harvesting for Wireless Sensors)

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61 pages, 4074 KiB

Open AccessEditor’s ChoiceReview

Sensors and Actuation Technologies in Exoskeletons: A Review

by Monica Tiboni, Alberto Borboni, Fabien Vérité, Chiara Bregoli and Cinzia Amici

Sensors 2022, 22(3), 884; https://doi.org/10.3390/s22030884 - 24 Jan 2022

Cited by 59 | Viewed by 11876

Abstract

Exoskeletons are robots that closely interact with humans and that are increasingly used for different purposes, such as rehabilitation, assistance in the activities of daily living (ADLs), performance augmentation or as haptic devices. In the last few decades, the research activity on these [...] Read more.

Exoskeletons are robots that closely interact with humans and that are increasingly used for different purposes, such as rehabilitation, assistance in the activities of daily living (ADLs), performance augmentation or as haptic devices. In the last few decades, the research activity on these robots has grown exponentially, and sensors and actuation technologies are two fundamental research themes for their development. In this review, an in-depth study of the works related to exoskeletons and specifically to these two main aspects is carried out. A preliminary phase investigates the temporal distribution of scientific publications to capture the interest in studying and developing novel ideas, methods or solutions for exoskeleton design, actuation and sensors. The distribution of the works is also analyzed with respect to the device purpose, body part to which the device is dedicated, operation mode and design methods. Subsequently, actuation and sensing solutions for the exoskeletons described by the studies in literature are analyzed in detail, highlighting the main trends in their development and spread. The results are presented with a schematic approach, and cross analyses among taxonomies are also proposed to emphasize emerging peculiarities. Full article

(This article belongs to the Special Issue Sensor Technologies for Human Health Monitoring)

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24 pages, 52875 KiB

Open AccessEditor’s ChoiceArticle

Post-Earthquake Building Evaluation Using UAVs: A BIM-Based Digital Twin Framework

by Nathaniel M. Levine and Billie F. Spencer, Jr.

Sensors 2022, 22(3), 873; https://doi.org/10.3390/s22030873 - 24 Jan 2022

Cited by 53 | Viewed by 8073

Abstract

Computer vision has shown potential for assisting post-earthquake inspection of buildings through automatic damage detection in images. However, assessing the safety of an earthquake-damaged building requires considering this damage in the context of its global impact on the structural system. Thus, an inspection [...] Read more.

Computer vision has shown potential for assisting post-earthquake inspection of buildings through automatic damage detection in images. However, assessing the safety of an earthquake-damaged building requires considering this damage in the context of its global impact on the structural system. Thus, an inspection must consider the expected damage progression of the associated component and the component’s contribution to structural system performance. To address this issue, a digital twin framework is proposed for post-earthquake building evaluation that integrates unmanned aerial vehicle (UAV) imagery, component identification, and damage evaluation using a Building Information Model (BIM) as a reference platform. The BIM guides selection of optimal sets of images for each building component. Then, if damage is identified, each image pixel is assigned to a specific BIM component, using a GrabCut-based segmentation method. In addition, 3D point cloud change detection is employed to identify nonstructural damage and associate that damage with specific BIM components. Two example applications are presented. The first develops a digital twin for an existing reinforced concrete moment frame building and demonstrates BIM-guided image selection and component identification. The second uses a synthetic graphics environment to demonstrate 3D point cloud change detection for identifying damaged nonstructural masonry walls. In both examples, observed damage is tied to BIM components, enabling damage to be considered in the context of each component’s known design and expected earthquake performance. The goal of this framework is to combine component-wise damage estimates with a pre-earthquake structural analysis of the building to predict a building’s post-earthquake safety based on an external UAV survey. Full article

(This article belongs to the Special Issue Toward Data-Driven Structural Health Monitoring: New Approaches and Sensor-Based Methods)

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13 pages, 3057 KiB

Open AccessEditor’s ChoiceArticle

Multi-Gas Detection System Based on Non-Dispersive Infrared (NDIR) Spectral Technology

by Manlin Xu, Bo Peng, Xiangyi Zhu and Yongcai Guo

Sensors 2022, 22(3), 836; https://doi.org/10.3390/s22030836 - 22 Jan 2022

Cited by 30 | Viewed by 4828

Abstract

Automobile exhaust gases, such as carbon dioxide (CO₂), carbon monoxide (CO), and propane (C₃H₈), cause the greenhouse effect, photochemical smog, and haze, threatening the urban atmosphere and human health. In this study, a non-dispersive infrared (NDIR) multi-gas [...] Read more.

Automobile exhaust gases, such as carbon dioxide (CO₂), carbon monoxide (CO), and propane (C₃H₈), cause the greenhouse effect, photochemical smog, and haze, threatening the urban atmosphere and human health. In this study, a non-dispersive infrared (NDIR) multi-gas detection system consisting of a single broadband light source, gas cell, and four-channel pyroelectric detector was developed. The system can be used to economically detect gas concentration in the range of 0–5000 ppm for C₃H₈, 0–14% for CO, and 0–20% for CO₂. According to the experimental data, the concentration inversion model was established using the least squares between the voltage ratio and the concentration. Additionally, the interference coefficient between different gases was tested. Therefore, the interference models between the three gases were established by the least square method. The concentration inversion model was experimentally verified, and it was observed that the full-scale error of the sensor changed less than 3.5%, the detection repeatability error was lower than 4.5%, and the detection stability was less than 2.7%. Therefore, the detection system is economical and energy efficient and it is a promising method for the analysis of automobile exhaust gases. Full article

(This article belongs to the Section Optical Sensors)

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18 pages, 23878 KiB

Open AccessEditor’s ChoiceArticle

Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System

by Anna Nora Tassetti, Alessandro Galdelli, Jacopo Pulcinella, Adriano Mancini and Luca Bolognini

Sensors 2022, 22(3), 839; https://doi.org/10.3390/s22030839 - 22 Jan 2022

Cited by 21 | Viewed by 5099

Abstract

During the last decade vessel-position-recording devices, such as the Vessel Monitoring System and the Automatic Identification System, have increasingly given accurate spatial and quantitative information of industrial fisheries. On the other hand, small-scale fisheries (vessels below 12 m) remain untracked and largely unregulated [...] Read more.

During the last decade vessel-position-recording devices, such as the Vessel Monitoring System and the Automatic Identification System, have increasingly given accurate spatial and quantitative information of industrial fisheries. On the other hand, small-scale fisheries (vessels below 12 m) remain untracked and largely unregulated even though they play an important socio-economic and cultural role in European waters and coastal communities and account for most of the total EU fishing fleet. The typically low-technological capacity of these small-scale fishing boats—for which space and power onboard are often limited—as well their reduced operative range encourage the development of efficient, low-cost, and low-burden tracking solutions. In this context, we designed a cost-effective and scalable prototypic architecture to gather and process positional data from small-scale vessels, making use of a LoRaWAN/cellular network. Data collected by our first installation are presented, as well as its preliminary processing. The emergence of a such low-cost and open-source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data, and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management. Full article

(This article belongs to the Special Issue Selected Papers from the 2021 IEEE International Workshop on Metrology for Sea)

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12 pages, 2786 KiB

Open AccessEditor’s ChoiceCommunication

UHF RFID Temperature Sensor Tag Integrated into a Textile Yarn

by Sofia Benouakta, Florin Doru Hutu and Yvan Duroc

Sensors 2022, 22(3), 818; https://doi.org/10.3390/s22030818 - 21 Jan 2022

Cited by 13 | Viewed by 3116

Abstract

This paper presents the design of an ultra high-frequency (UHF) radio frequency identification (RFID) sensor tag integrated into a textile yarn and manufactured using the E-Thread^® technology. The temperature detection concept is based on the modification of the impedance matching between RFID [...] Read more.

This paper presents the design of an ultra high-frequency (UHF) radio frequency identification (RFID) sensor tag integrated into a textile yarn and manufactured using the E-Thread^® technology. The temperature detection concept is based on the modification of the impedance matching between RFID tag’s antenna and the chip. This modification is created by the change in the resistance of a thermistor integrated within the tag system due to a temperature variation. Moreover, in order to obtain an environment independent detection, a differential approach is proposed that avoids the use of a pre-calibration phase by the use of a reference tag. Experimental characterization demonstrates the RFID sensor’s potential of detecting a temperature variation or a temperature threshold between

25

and 70 °C through the variation of the transmitted differential activation power. Full article

(This article belongs to the Special Issue RF Sensors: Design, Optimization and Applications)

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15 pages, 6284 KiB

Open AccessEditor’s ChoiceArticle

Accuracy and Speed Improvement of Event Camera Motion Estimation Using a Bird’s-Eye View Transformation

by Takehiro Ozawa, Yusuke Sekikawa and Hideo Saito

Sensors 2022, 22(3), 773; https://doi.org/10.3390/s22030773 - 20 Jan 2022

Cited by 11 | Viewed by 3239

Abstract

Event cameras are bio-inspired sensors that have a high dynamic range and temporal resolution. This property enables motion estimation from textures with repeating patterns, which is difficult to achieve with RGB cameras. Therefore, motion estimation of an event camera is expected to be [...] Read more.

Event cameras are bio-inspired sensors that have a high dynamic range and temporal resolution. This property enables motion estimation from textures with repeating patterns, which is difficult to achieve with RGB cameras. Therefore, motion estimation of an event camera is expected to be applied to vehicle position estimation. An existing method, called contrast maximization, is one of the methods that can be used for event camera motion estimation by capturing road surfaces. However, contrast maximization tends to fall into a local solution when estimating three-dimensional motion, which makes correct estimation difficult. To solve this problem, we propose a method for motion estimation by optimizing contrast in the bird’s-eye view space. Instead of performing three-dimensional motion estimation, we reduced the dimensionality to two-dimensional motion estimation by transforming the event data to a bird’s-eye view using homography calculated from the event camera position. This transformation mitigates the problem of the loss function becoming non-convex, which occurs in conventional methods. As a quantitative experiment, we created event data by using a car simulator and evaluated our motion estimation method, showing an improvement in accuracy and speed. In addition, we conducted estimation from real event data and evaluated the results qualitatively, showing an improvement in accuracy. Full article

(This article belongs to the Special Issue Feature Extraction for Unconventional Visual Sensors or Specific Applications)

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14 pages, 3219 KiB

Open AccessEditor’s ChoiceArticle

Photonic Label-Free Biosensors for Fast and Multiplex Detection of Swine Viral Diseases

by Maribel Gómez-Gómez, Carles Sánchez, Sergio Peransi, David Zurita, Laurent Bellieres, Sara Recuero, Manuel Rodrigo, Santiago Simón, Alessandra Camarca, Alessandro Capo, Maria Staiano, Antonio Varriale, Sabato D’Auria, Georgios Manessis, Athnasios I. Gelasakis, Ioannis Bossis, Gyula Balka, Lilla Dénes, Maciej Frant, Lapo Nannucci, Matteo Bonasso, Alessandro Giusti and Amadeu Griol Show full author list Hide full author list

Sensors 2022, 22(3), 708; https://doi.org/10.3390/s22030708 - 18 Jan 2022

Cited by 10 | Viewed by 3374

Abstract

In this paper we present the development of photonic integrated circuit (PIC) biosensors for the label-free detection of six emerging and endemic swine viruses, namely: African Swine Fever Virus (ASFV), Classical Swine Fever Virus (CSFV), Porcine Reproductive and Respiratory Syndrome Virus (PPRSV), Porcine [...] Read more.

In this paper we present the development of photonic integrated circuit (PIC) biosensors for the label-free detection of six emerging and endemic swine viruses, namely: African Swine Fever Virus (ASFV), Classical Swine Fever Virus (CSFV), Porcine Reproductive and Respiratory Syndrome Virus (PPRSV), Porcine Parvovirus (PPV), Porcine Circovirus 2 (PCV2), and Swine Influenza Virus A (SIV). The optical biosensors are based on evanescent wave technology and, in particular, on Resonant Rings (RRs) fabricated in silicon nitride. The novel biosensors were packaged in an integrated sensing cartridge that included a microfluidic channel for buffer/sample delivery and an optical fiber array for the optical operation of the PICs. Antibodies were used as molecular recognition elements (MREs) and were selected based on western blotting and ELISA experiments to ensure the high sensitivity and specificity of the novel sensors. MREs were immobilized on RR surfaces to capture viral antigens. Antibody–antigen interactions were transduced via the RRs to a measurable resonant shift. Cell culture supernatants for all of the targeted viruses were used to validate the biosensors. Resonant shift responses were dose-dependent. The results were obtained within the framework of the SWINOSTICS project, contributing to cover the need of the novel diagnostic tools to tackle swine viral diseases. Full article

(This article belongs to the Special Issue Label-Free Sensor/Biosensors for Biomedical and Environmental Monitoring)

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15 pages, 12630 KiB

Open AccessEditor’s ChoiceArticle

A Real-Time Zanthoxylum Target Detection Method for an Intelligent Picking Robot under a Complex Background, Based on an Improved YOLOv5s Architecture

by Zhibo Xu, Xiaopeng Huang, Yuan Huang, Haobo Sun and Fangxin Wan

Sensors 2022, 22(2), 682; https://doi.org/10.3390/s22020682 - 17 Jan 2022

Cited by 22 | Viewed by 3644

Abstract

The target recognition algorithm is one of the core technologies of Zanthoxylum pepper-picking robots. However, most existing detection algorithms cannot effectively detect Zanthoxylum fruit covered by branches, leaves and other fruits in natural scenes. To improve the work efficiency and adaptability of the [...] Read more.

The target recognition algorithm is one of the core technologies of Zanthoxylum pepper-picking robots. However, most existing detection algorithms cannot effectively detect Zanthoxylum fruit covered by branches, leaves and other fruits in natural scenes. To improve the work efficiency and adaptability of the Zanthoxylum-picking robot in natural environments, and to recognize and detect fruits in complex environments under different lighting conditions, this paper presents a Zanthoxylum-picking-robot target detection method based on improved YOLOv5s. Firstly, an improved CBF module based on the CBH module in the backbone is raised to improve the detection accuracy. Secondly, the Specter module based on CBF is presented to replace the bottleneck CSP module, which improves the speed of detection with a lightweight structure. Finally, the Zanthoxylum fruit algorithm is checked by the improved YOLOv5 framework, and the differences in detection between YOLOv3, YOLOv4 and YOLOv5 are analyzed and evaluated. Through these improvements, the recall rate, recognition accuracy and mAP of the YOLOv5s are 4.19%, 28.7% and 14.8% higher than those of the original YOLOv5s, YOLOv3 and YOLOv4 models, respectively. Furthermore, the model is transferred to the computing platform of the robot with the cutting-edge NVIDIA Jetson TX2 device. Several experiments are implemented on the TX2, yielding an average time of inference of 0.072, with an average GPU load in 30 s of 20.11%. This method can provide technical support for pepper-picking robots to detect multiple pepper fruits in real time. Full article

(This article belongs to the Section Intelligent Sensors)

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24 pages, 566 KiB

Open AccessEditor’s ChoiceArticle

Towards LoRaWAN without Data Loss: Studying the Performance of Different Channel Access Approaches

by Frank Loh, Noah Mehling and Tobias Hoßfeld

Sensors 2022, 22(2), 691; https://doi.org/10.3390/s22020691 - 17 Jan 2022

Cited by 21 | Viewed by 3295

Abstract

The Long Range Wide Area Network (LoRaWAN) is one of the fastest growing Internet of Things (IoT) access protocols. It operates in the license free 868 MHz band and gives everyone the possibility to create their own small sensor networks. The drawback of [...] Read more.

The Long Range Wide Area Network (LoRaWAN) is one of the fastest growing Internet of Things (IoT) access protocols. It operates in the license free 868 MHz band and gives everyone the possibility to create their own small sensor networks. The drawback of this technology is often unscheduled or random channel access, which leads to message collisions and potential data loss. For that reason, recent literature studies alternative approaches for LoRaWAN channel access. In this work, state-of-the-art random channel access is compared with alternative approaches from the literature by means of collision probability. Furthermore, a time scheduled channel access methodology is presented to completely avoid collisions in LoRaWAN. For this approach, an exhaustive simulation study was conducted and the performance was evaluated with random access cross-traffic. In a general theoretical analysis the limits of the time scheduled approach are discussed to comply with duty cycle regulations in LoRaWAN. Full article

(This article belongs to the Special Issue Wireless Sensor Networks in the IoT: New Challenges)

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17 pages, 6673 KiB

Open AccessEditor’s ChoiceArticle

A Mass-Producible Washable Smart Garment with Embedded Textile EMG Electrodes for Control of Myoelectric Prostheses: A Pilot Study

by Milad Alizadeh-Meghrazi, Gurjant Sidhu, Saransh Jain, Michael Stone, Ladan Eskandarian, Amirali Toossi and Milos R. Popovic

Sensors 2022, 22(2), 666; https://doi.org/10.3390/s22020666 - 15 Jan 2022

Cited by 21 | Viewed by 4119

Abstract

Electromyography (EMG) is the resulting electrical signal from muscle activity, commonly used as a proxy for users’ intent in voluntary control of prosthetic devices. EMG signals are recorded with gold standard Ag/AgCl gel electrodes, though there are limitations in continuous use applications, with [...] Read more.

Electromyography (EMG) is the resulting electrical signal from muscle activity, commonly used as a proxy for users’ intent in voluntary control of prosthetic devices. EMG signals are recorded with gold standard Ag/AgCl gel electrodes, though there are limitations in continuous use applications, with potential skin irritations and discomfort. Alternative dry solid metallic electrodes also face long-term usability and comfort challenges due to their inflexible and non-breathable structures. This is critical when the anatomy of the targeted body region is variable (e.g., residual limbs of individuals with amputation), and conformal contact is essential. In this study, textile electrodes were developed, and their performance in recording EMG signals was compared to gel electrodes. Additionally, to assess the reusability and robustness of the textile electrodes, the effect of 30 consumer washes was investigated. Comparisons were made between the signal-to-noise ratio (SNR), with no statistically significant difference, and with the power spectral density (PSD), showing a high correlation. Subsequently, a fully textile sleeve was fabricated covering the forearm, with 14 textile electrodes. For three individuals, an artificial neural network model was trained, capturing the EMG of 7 distinct finger movements. The personalized models were then used to successfully control a myoelectric prosthetic hand. Full article

(This article belongs to the Special Issue Smart Textiles Technologies and Wearable Sensors)

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12 pages, 4083 KiB

Open AccessEditor’s ChoiceArticle

A Dielectric Elastomer-Based Multimodal Capacitive Sensor

by Yuting Zhu, Tim Giffney and Kean Aw

Sensors 2022, 22(2), 622; https://doi.org/10.3390/s22020622 - 14 Jan 2022

Cited by 13 | Viewed by 3514

Abstract

Dielectric elastomer (DE) sensors have been widely used in a wide variety of applications, such as in robotic hands, wearable sensors, rehabilitation devices, etc. A unique dielectric elastomer-based multimodal capacitive sensor has been developed to quantify the pressure and the location of any [...] Read more.

Dielectric elastomer (DE) sensors have been widely used in a wide variety of applications, such as in robotic hands, wearable sensors, rehabilitation devices, etc. A unique dielectric elastomer-based multimodal capacitive sensor has been developed to quantify the pressure and the location of any touch simultaneously. This multimodal sensor is a soft, flexible, and stretchable dielectric elastomer (DE) capacitive pressure mat that is composed of a multi-layer soft and stretchy DE sensor. The top layer measures the applied pressure, while the underlying sensor array enables location identification. The sensor is placed on a passive elastomeric substrate in order to increase deformation and optimize the sensor’s sensitivity. This DE multimodal capacitive sensor, with pressure and localization capability, paves the way for further development with potential applications in bio-mechatronics technology and other humanoid devices. The sensor design could be useful for robotic and other applications, such as fruit picking or as a bio-instrument for the diabetic insole. Full article

(This article belongs to the Special Issue Stimuli-Responsive Flexible Sensors)

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35 pages, 17742 KiB

Open AccessEditor’s ChoiceReview

Comprehensive Review on Wearable Sweat-Glucose Sensors for Continuous Glucose Monitoring

by Hima Zafar, Asma Channa, Varun Jeoti and Goran M. Stojanović

Sensors 2022, 22(2), 638; https://doi.org/10.3390/s22020638 - 14 Jan 2022

Cited by 128 | Viewed by 21697

Abstract

The incidence of diabetes is increasing at an alarming rate, and regular glucose monitoring is critical in order to manage diabetes. Currently, glucose in the body is measured by an invasive method of blood sugar testing. Blood glucose (BG) monitoring devices measure the [...] Read more.

The incidence of diabetes is increasing at an alarming rate, and regular glucose monitoring is critical in order to manage diabetes. Currently, glucose in the body is measured by an invasive method of blood sugar testing. Blood glucose (BG) monitoring devices measure the amount of sugar in a small sample of blood, usually drawn from pricking the fingertip, and placed on a disposable test strip. Therefore, there is a need for non-invasive continuous glucose monitoring, which is possible using a sweat sensor-based approach. As sweat sensors have garnered much interest in recent years, this study attempts to summarize recent developments in non-invasive continuous glucose monitoring using sweat sensors based on different approaches with an emphasis on the devices that can potentially be integrated into a wearable platform. Numerous research entities have been developing wearable sensors for continuous blood glucose monitoring, however, there are no commercially viable, non-invasive glucose monitors on the market at the moment. This review article provides the state-of-the-art in sweat glucose monitoring, particularly keeping in sight the prospect of its commercialization. The challenges relating to sweat collection, sweat sample degradation, person to person sweat amount variation, various detection methods, and their glucose detection sensitivity, and also the commercial viability are thoroughly covered. Full article

(This article belongs to the Special Issue Smart Sensors for Wearable Applications)

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17 pages, 821 KiB

Open AccessEditor’s ChoiceArticle

Exploring Silent Speech Interfaces Based on Frequency-Modulated Continuous-Wave Radar

by David Ferreira, Samuel Silva, Francisco Curado and António Teixeira

Sensors 2022, 22(2), 649; https://doi.org/10.3390/s22020649 - 14 Jan 2022

Cited by 13 | Viewed by 3687

Abstract

Speech is our most natural and efficient form of communication and offers a strong potential to improve how we interact with machines. However, speech communication can sometimes be limited by environmental (e.g., ambient noise), contextual (e.g., need for privacy), or health conditions (e.g., [...] Read more.

Speech is our most natural and efficient form of communication and offers a strong potential to improve how we interact with machines. However, speech communication can sometimes be limited by environmental (e.g., ambient noise), contextual (e.g., need for privacy), or health conditions (e.g., laryngectomy), preventing the consideration of audible speech. In this regard, silent speech interfaces (SSI) have been proposed as an alternative, considering technologies that do not require the production of acoustic signals (e.g., electromyography and video). Unfortunately, despite their plentitude, many still face limitations regarding their everyday use, e.g., being intrusive, non-portable, or raising technical (e.g., lighting conditions for video) or privacy concerns. In line with this necessity, this article explores the consideration of contactless continuous-wave radar to assess its potential for SSI development. A corpus of 13 European Portuguese words was acquired for four speakers and three of them enrolled in a second acquisition session, three months later. Regarding the speaker-dependent models, trained and tested with data from each speaker while using 5-fold cross-validation, average accuracies of 84.50% and 88.00% were respectively obtained from Bagging (BAG) and Linear Regression (LR) classifiers, respectively. Additionally, recognition accuracies of 81.79% and 81.80% were also, respectively, achieved for the session and speaker-independent experiments, establishing promising grounds for further exploring this technology towards silent speech recognition. Full article

(This article belongs to the Special Issue Future Speech Interfaces with Sensors and Machine Intelligence)

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24 pages, 7214 KiB

Open AccessEditor’s ChoiceArticle

Above-Ground Biomass Estimation in Oats Using UAV Remote Sensing and Machine Learning

by Prakriti Sharma, Larry Leigh, Jiyul Chang, Maitiniyazi Maimaitijiang and Melanie Caffé

Sensors 2022, 22(2), 601; https://doi.org/10.3390/s22020601 - 13 Jan 2022

Cited by 40 | Viewed by 5325

Abstract

Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield [...] Read more.

Current strategies for phenotyping above-ground biomass in field breeding nurseries demand significant investment in both time and labor. Unmanned aerial vehicles (UAV) can be used to derive vegetation indices (VIs) with high throughput and could provide an efficient way to predict forage yield with high accuracy. The main objective of the study is to investigate the potential of UAV-based multispectral data and machine learning approaches in the estimation of oat biomass. UAV equipped with a multispectral sensor was flown over three experimental oat fields in Volga, South Shore, and Beresford, South Dakota, USA, throughout the pre- and post-heading growth phases of oats in 2019. A variety of vegetation indices (VIs) derived from UAV-based multispectral imagery were employed to build oat biomass estimation models using four machine-learning algorithms: partial least squares (PLS), support vector machine (SVM), Artificial neural network (ANN), and random forest (RF). The results showed that several VIs derived from the UAV collected images were significantly positively correlated with dry biomass for Volga and Beresford (r = 0.2–0.65), however, in South Shore, VIs were either not significantly or weakly correlated with biomass. For Beresford, approximately 70% of the variance was explained by PLS, RF, and SVM validation models using data collected during the post-heading phase. Likewise for Volga, validation models had lower coefficient of determination (R² = 0.20–0.25) and higher error (RMSE = 700–800 kg/ha) than training models (R² = 0.50–0.60; RMSE = 500–690 kg/ha). In South Shore, validation models were only able to explain approx. 15–20% of the variation in biomass, which is possibly due to the insignificant correlation values between VIs and biomass. Overall, this study indicates that airborne remote sensing with machine learning has potential for above-ground biomass estimation in oat breeding nurseries. The main limitation was inconsistent accuracy in model prediction across locations. Multiple-year spectral data, along with the inclusion of textural features like crop surface model (CSM) derived height and volumetric indicators, should be considered in future studies while estimating biophysical parameters like biomass. Full article

(This article belongs to the Special Issue Methodologies Used in Remote Sensing Data Analysis and Remote Sensors for Precision Agriculture)

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39 pages, 710 KiB

Open AccessEditor’s ChoiceReview

A Systematic Review of Wearable Sensors for Monitoring Physical Activity

by Annica Kristoffersson and Maria Lindén

Sensors 2022, 22(2), 573; https://doi.org/10.3390/s22020573 - 12 Jan 2022

Cited by 34 | Viewed by 7976

Abstract

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological [...] Read more.

This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing. Full article

(This article belongs to the Special Issue Embedded Sensor Systems for Health)

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19 pages, 89983 KiB

Open AccessEditor’s ChoiceArticle

Litter Detection with Deep Learning: A Comparative Study

by Manuel Córdova, Allan Pinto, Christina Carrozzo Hellevik, Saleh Abdel-Afou Alaliyat, Ibrahim A. Hameed, Helio Pedrini and Ricardo da S. Torres

Sensors 2022, 22(2), 548; https://doi.org/10.3390/s22020548 - 11 Jan 2022

Cited by 30 | Viewed by 7613

Abstract

Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby [...] Read more.

Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions have been explored to develop litter detection tools, thereby supporting research, citizen science, and volunteer clean-up initiatives. However, to the best of our knowledge, no work has investigated the performance of state-of-the-art deep learning object detection approaches in the context of litter detection. In particular, no studies have focused on the assessment of those methods aiming their use in devices with low processing capabilities, e.g., mobile phones, typically employed in citizen science activities. In this paper, we fill this literature gap. We performed a comparative study involving state-of-the-art CNN architectures (e.g., Faster RCNN, Mask-RCNN, EfficientDet, RetinaNet and YOLO-v5), two litter image datasets and a smartphone. We also introduce a new dataset for litter detection, named PlastOPol, composed of 2418 images and 5300 annotations. The experimental results demonstrate that object detectors based on the YOLO family are promising for the construction of litter detection solutions, with superior performance in terms of detection accuracy, processing time, and memory footprint. Full article

(This article belongs to the Special Issue Image Sensing and Processing with Convolutional Neural Networks)

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12 pages, 2103 KiB

Open AccessEditor’s ChoiceArticle

Magnetic Nanoparticles Enhanced Surface Plasmon Resonance Biosensor for Rapid Detection of Salmonella Typhimurium in Romaine Lettuce

by Devendra Bhandari, Fur-Chi Chen and Roger C. Bridgman

Sensors 2022, 22(2), 475; https://doi.org/10.3390/s22020475 - 9 Jan 2022

Cited by 18 | Viewed by 2905

Abstract

Salmonella is one of the major foodborne pathogens responsible for many cases of illnesses, hospitalizations and deaths worldwide. Although different methods are available to timely detect Salmonella in foods, surface plasmon resonance (SPR) has the benefit of real-time detection with a high sensitivity [...] Read more.

Salmonella is one of the major foodborne pathogens responsible for many cases of illnesses, hospitalizations and deaths worldwide. Although different methods are available to timely detect Salmonella in foods, surface plasmon resonance (SPR) has the benefit of real-time detection with a high sensitivity and specificity. The purpose of this study was to develop an SPR method in conjunction with magnetic nanoparticles (MNPs) for the rapid detection of Salmonella Typhimurium. The assay utilizes a pair of well-characterized, flagellin-specific monoclonal antibodies; one is immobilized on the sensor surface and the other is coupled to the MNPs. Samples of romaine lettuce contaminated with Salmonella Typhimurium were washed with deionized water, and bacterial cells were captured on a filter membrane by vacuum filtration. SPR assays were compared in three different formats—direct assay, sequential two-step sandwich assay, and preincubation one-step sandwich assay. The interaction of flagellin and MNPs with the antibody-immobilized sensor surface were analyzed. SPR signals from a sequential two-step sandwich assay and preincubation one-step sandwich assay were 7.5 times and 14.0 times higher than the direct assay. The detection limits of the assay were 4.7 log cfu/mL in the buffer and 5.2 log cfu/g in romaine lettuce samples. Full article

(This article belongs to the Collection Enabling Technologies for Biosensors)

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19 pages, 2259 KiB

Open AccessEditor’s ChoiceArticle

Game Theory-Based Energy-Efficient Clustering Algorithm for Wireless Sensor Networks

by Xiao Yan, Cheng Huang, Jianyuan Gan and Xiaobei Wu

Sensors 2022, 22(2), 478; https://doi.org/10.3390/s22020478 - 9 Jan 2022

Cited by 28 | Viewed by 3042

Abstract

Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to [...] Read more.

Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). WSNs collect and transmit data through sensor nodes. However, the energy carried by the sensor nodes is limited. The sensor nodes need to save energy as much as possible to prolong the network lifetime. This paper proposes a game theory-based energy-efficient clustering algorithm (GEC) for wireless sensor networks, where each sensor node is regarded as a player in the game. According to the length of idle listening time in the active state, the sensor node can adopt favorable strategies for itself, and then decide whether to sleep or not. In order to avoid the selfish behavior of sensor nodes, a penalty mechanism is introduced to force the sensor nodes to adopt cooperative strategies in future operations. The simulation results show that the use of game theory can effectively save the energy consumption of the sensor network and increase the amount of network data transmission, so as to achieve the purpose of prolonging the network lifetime. Full article

(This article belongs to the Special Issue Wireless Sensor Networks in Smart Homes)

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20 pages, 8860 KiB

Open AccessEditor’s ChoiceArticle

Visualizing Street Pavement Anomalies through Fog Computing V2I Networks and Machine Learning

by Rogelio Bustamante-Bello, Alec García-Barba, Luis A. Arce-Saenz, Luis A. Curiel-Ramirez, Javier Izquierdo-Reyes and Ricardo A. Ramirez-Mendoza

Sensors 2022, 22(2), 456; https://doi.org/10.3390/s22020456 - 8 Jan 2022

Cited by 20 | Viewed by 2944

Abstract

Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, [...] Read more.

Analyzing data related to the conditions of city streets and avenues could help to make better decisions about public spending on mobility. Generally, streets and avenues are fixed as soon as they have a citizen report or when a major incident occurs. However, it is uncommon for cities to have real-time reactive systems that detect the different problems they have to fix on the pavement. This work proposes a solution to detect anomalies in streets through state analysis using sensors within the vehicles that travel daily and connecting them to a fog-computing architecture on a V2I network. The system detects and classifies the main road problems or abnormal conditions in streets and avenues using Machine Learning Algorithms (MLA), comparing roughness against a flat reference. An instrumented vehicle obtained the reference through accelerometry sensors and then sent the data through a mid-range communication system. With these data, the system compared an Artificial Neural Network (supervised MLA) and a K-Nearest Neighbor (Supervised MLA) to select the best option to handle the acquired data. This system makes it desirable to visualize the streets’ quality and map the areas with the most significant anomalies. Full article

(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles)

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16 pages, 1705 KiB

Open AccessEditor’s ChoiceArticle

Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models

by Jay-Shian Tan, Sawitchaya Tippaya, Tara Binnie, Paul Davey, Kathryn Napier, J. P. Caneiro, Peter Kent, Anne Smith, Peter O’Sullivan and Amity Campbell

Sensors 2022, 22(2), 446; https://doi.org/10.3390/s22020446 - 7 Jan 2022

Cited by 25 | Viewed by 4915

Abstract

Deep learning models developed to predict knee joint kinematics are usually trained on inertial measurement unit (IMU) data from healthy people and only for the activity of walking. Yet, people with knee osteoarthritis have difficulties with other activities and there are a lack [...] Read more.

Deep learning models developed to predict knee joint kinematics are usually trained on inertial measurement unit (IMU) data from healthy people and only for the activity of walking. Yet, people with knee osteoarthritis have difficulties with other activities and there are a lack of studies using IMU training data from this population. Our objective was to conduct a proof-of-concept study to determine the feasibility of using IMU training data from people with knee osteoarthritis performing multiple clinically important activities to predict knee joint sagittal plane kinematics using a deep learning approach. We trained a bidirectional long short-term memory model on IMU data from 17 participants with knee osteoarthritis to estimate knee joint flexion kinematics for phases of walking, transitioning to and from a chair, and negotiating stairs. We tested two models, a double-leg model (four IMUs) and a single-leg model (two IMUs). The single-leg model demonstrated less prediction error compared to the double-leg model. Across the different activity phases, RMSE (SD) ranged from 7.04° (2.6) to 11.78° (6.04), MAE (SD) from 5.99° (2.34) to 10.37° (5.44), and Pearson’s R from 0.85 to 0.99 using leave-one-subject-out cross-validation. This study demonstrates the feasibility of using IMU training data from people who have knee osteoarthritis for the prediction of kinematics for multiple clinically relevant activities. Full article

(This article belongs to the Special Issue Application for Assistive Technologies and Wearable Sensors)

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46 pages, 3853 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Insole-Based Systems for Health Monitoring: Current Solutions and Research Challenges

by Sophini Subramaniam, Sumit Majumder, Abu Ilius Faisal and M. Jamal Deen

Sensors 2022, 22(2), 438; https://doi.org/10.3390/s22020438 - 7 Jan 2022

Cited by 49 | Viewed by 16804

Abstract

Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals’ daily activities, providing information that is reflective of an individual’s health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, [...] Read more.

Wearable health monitoring devices allow for measuring physiological parameters without restricting individuals’ daily activities, providing information that is reflective of an individual’s health and well-being. However, these systems need to be accurate, power-efficient, unobtrusive and simple to use to enable a reliable, convenient, automatic and ubiquitous means of long-term health monitoring. One such system can be embedded in an insole to obtain physiological data from the plantar aspect of the foot that can be analyzed to gain insight into an individual’s health. This manuscript provides a comprehensive review of insole-based sensor systems that measure a variety of parameters useful for overall health monitoring, with a focus on insole-based PPD measurement systems developed in recent years. Existing solutions are reviewed, and several open issues are presented and discussed. The concept of a fully integrated insole-based health monitoring system and considerations for future work are described. By developing a system that is capable of measuring parameters such as PPD, gait characteristics, foot temperature and heart rate, a holistic understanding of an individual’s health and well-being can be obtained without interrupting day-to-day activities. The proposed device can have a multitude of applications, such as for pathology detection, tracking medical conditions and analyzing gait characteristics. Full article

(This article belongs to the Special Issue Advances of Wearables in Health Monitoring)

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21 pages, 11989 KiB

Open AccessEditor’s ChoiceArticle

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

by Mohammad Al-Sa’d, Serkan Kiranyaz, Iftikhar Ahmad, Christian Sundell, Matti Vakkuri and Moncef Gabbouj

Sensors 2022, 22(2), 418; https://doi.org/10.3390/s22020418 - 6 Jan 2022

Cited by 21 | Viewed by 4597

Abstract

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive [...] Read more.

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system’s ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing. Full article

(This article belongs to the Special Issue Computer Visions and Pattern Recognition)

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9 pages, 4541 KiB

Open AccessEditor’s ChoiceArticle

Identification of Corrosion Minerals Using Shortwave Infrared Hyperspectral Imaging

by Thomas De Kerf, Georgios Pipintakos, Zohreh Zahiri, Steve Vanlanduit and Paul Scheunders

Sensors 2022, 22(1), 407; https://doi.org/10.3390/s22010407 - 5 Jan 2022

Cited by 16 | Viewed by 5460

Abstract

In this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900–1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. [...] Read more.

In this study, we propose a new method to identify corrosion minerals in carbon steel using hyperspectral imaging (HSI) in the shortwave infrared range (900–1700 nm). Seven samples were artificially corroded using a neutral salt spray test and examined using a hyperspectral camera. A normalized cross-correlation algorithm is used to identify four different corrosion minerals (goethite, magnetite, lepidocrocite and hematite), using reference spectra. A Fourier Transform Infrared spectrometer (FTIR) analysis of the scraped corrosion powders was used as a ground truth to validate the results obtained by the hyperspectral camera. This comparison shows that the HSI technique effectively detects the dominant mineral present in the samples. In addition, HSI can also accurately predict the changes in mineral composition that occur over time. Full article

(This article belongs to the Section Chemical Sensors)

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20 pages, 10570 KiB

Open AccessEditor’s ChoiceArticle

Motion Capture Sensor-Based Emotion Recognition Using a Bi-Modular Sequential Neural Network

by Yajurv Bhatia, ASM Hossain Bari, Gee-Sern Jison Hsu and Marina Gavrilova

Sensors 2022, 22(1), 403; https://doi.org/10.3390/s22010403 - 5 Jan 2022

Cited by 15 | Viewed by 3809

Abstract

Motion capture sensor-based gait emotion recognition is an emerging sub-domain of human emotion recognition. Its applications span a variety of fields including smart home design, border security, robotics, virtual reality, and gaming. In recent years, several deep learning-based approaches have been successful in [...] Read more.

Motion capture sensor-based gait emotion recognition is an emerging sub-domain of human emotion recognition. Its applications span a variety of fields including smart home design, border security, robotics, virtual reality, and gaming. In recent years, several deep learning-based approaches have been successful in solving the Gait Emotion Recognition (GER) problem. However, a vast majority of such methods rely on Deep Neural Networks (DNNs) with a significant number of model parameters, which lead to model overfitting as well as increased inference time. This paper contributes to the domain of knowledge by proposing a new lightweight bi-modular architecture with handcrafted features that is trained using a RMSprop optimizer and stratified data shuffling. The method is highly effective in correctly inferring human emotions from gait, achieving a micro-mean average precision of 0.97 on the Edinburgh Locomotive Mocap Dataset. It outperforms all recent deep-learning methods, while having the lowest inference time of 16.3 milliseconds per gait sample. This research study is beneficial to applications spanning various fields, such as emotionally aware assistive robotics, adaptive therapy and rehabilitation, and surveillance. Full article

(This article belongs to the Special Issue Section “Sensor Networks”: 10th Anniversary)

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24 pages, 2193 KiB

Open AccessEditor’s ChoiceReview

Cost Effective Synthesis of Graphene Nanomaterials for Non-Enzymatic Electrochemical Sensors for Glucose: A Comprehensive Review

by Georgia Balkourani, Theodoros Damartzis, Angeliki Brouzgou and Panagiotis Tsiakaras

Sensors 2022, 22(1), 355; https://doi.org/10.3390/s22010355 - 4 Jan 2022

Cited by 35 | Viewed by 5239

Abstract

The high conductivity of graphene material (or its derivatives) and its very large surface area enhance the direct electron transfer, improving non-enzymatic electrochemical sensors sensitivity and its other characteristics. The offered large pores facilitate analyte transport enabling glucose detection even at very low [...] Read more.

The high conductivity of graphene material (or its derivatives) and its very large surface area enhance the direct electron transfer, improving non-enzymatic electrochemical sensors sensitivity and its other characteristics. The offered large pores facilitate analyte transport enabling glucose detection even at very low concentration values. In the current review paper we classified the enzymeless graphene-based glucose electrocatalysts’ synthesis methods that have been followed into the last few years into four main categories: (i) direct growth of graphene (or oxides) on metallic substrates, (ii) in-situ growth of metallic nanoparticles into graphene (or oxides) matrix, (iii) laser-induced graphene electrodes and (iv) polymer functionalized graphene (or oxides) electrodes. The increment of the specific surface area and the high degree reduction of the electrode internal resistance were recognized as their common targets. Analyzing glucose electrooxidation mechanism over Cu- Co- and Ni-(oxide)/graphene (or derivative) electrocatalysts, we deduced that glucose electrochemical sensing properties, such as sensitivity, detection limit and linear detection limit, totally depend on the route of the mass and charge transport between metal(II)/metal(III); and so both (specific area and internal resistance) should have the optimum values. Full article

(This article belongs to the Special Issue Electrochemical (Bio)sensors for Biomedical Applications)

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24 pages, 5660 KiB

Open AccessEditor’s ChoiceArticle

Mobile Charging Strategy for Wireless Rechargeable Sensor Networks

by Tzung-Shi Chen, Jen-Jee Chen, Xiang-You Gao and Tzung-Cheng Chen

Sensors 2022, 22(1), 359; https://doi.org/10.3390/s22010359 - 4 Jan 2022

Cited by 19 | Viewed by 2980

Abstract

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of [...] Read more.

In a wireless sensor network, the sensing and data transmission for sensors will cause energy depletion, which will lead to the inability to complete the tasks. To solve this problem, wireless rechargeable sensor networks (WRSNs) have been developed to extend the lifetime of the entire network. In WRSNs, a mobile charging robot (MR) is responsible for wireless charging each sensor battery and collecting sensory data from the sensor simultaneously. Thereby, MR needs to traverse along a designed path for all sensors in the WRSNs. In this paper, dual-side charging strategies are proposed for MR traversal planning, which minimize the MR traversal path length, energy consumption, and completion time. Based on MR dual-side charging, neighboring sensors in both sides of a designated path can be wirelessly charged by MR and sensory data sent to MR simultaneously. The constructed path is based on the power diagram according to the remaining power of sensors and distances among sensors in a WRSN. While the power diagram is built, charging strategies with dual-side charging capability are determined accordingly. In addition, a clustering-based approach is proposed to improve minimizing MR moving total distance, saving charging energy and total completion time in a round. Moreover, integrated strategies that apply a clustering-based approach on the dual-side charging strategies are presented in WRSNs. The simulation results show that, no matter with or without clustering, the performances of proposed strategies outperform the baseline strategies in three respects, energy saving, total distance reduced, and completion time reduced for MR in WSRNs. Full article

(This article belongs to the Special Issue Advanced Wireless Sensing Techniques for Communication)

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16 pages, 4057 KiB

Open AccessEditor’s ChoiceArticle

Human Activity Recognition via Hybrid Deep Learning Based Model

by Imran Ullah Khan, Sitara Afzal and Jong Weon Lee

Sensors 2022, 22(1), 323; https://doi.org/10.3390/s22010323 - 1 Jan 2022

Cited by 117 | Viewed by 10215

Abstract

In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features [...] Read more.

In recent years, Human Activity Recognition (HAR) has become one of the most important research topics in the domains of health and human-machine interaction. Many Artificial intelligence-based models are developed for activity recognition; however, these algorithms fail to extract spatial and temporal features due to which they show poor performance on real-world long-term HAR. Furthermore, in literature, a limited number of datasets are publicly available for physical activities recognition that contains less number of activities. Considering these limitations, we develop a hybrid model by incorporating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for activity recognition where CNN is used for spatial features extraction and LSTM network is utilized for learning temporal information. Additionally, a new challenging dataset is generated that is collected from 20 participants using the Kinect V2 sensor and contains 12 different classes of human physical activities. An extensive ablation study is performed over different traditional machine learning and deep learning models to obtain the optimum solution for HAR. The accuracy of 90.89% is achieved via the CNN-LSTM technique, which shows that the proposed model is suitable for HAR applications. Full article

(This article belongs to the Special Issue Human Activity Recognition Using Deep Learning)

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11 pages, 3181 KiB

Open AccessEditor’s ChoiceArticle

Reversible Room Temperature H₂ Gas Sensing Based on Self-Assembled Cobalt Oxysulfide

by Hui Zhou, Kai Xu, Nam Ha, Yinfen Cheng, Rui Ou, Qijie Ma, Yihong Hu, Vien Trinh, Guanghui Ren, Zhong Li and Jian Zhen Ou

Sensors 2022, 22(1), 303; https://doi.org/10.3390/s22010303 - 31 Dec 2021

Cited by 16 | Viewed by 2862

Abstract

Reversible H₂ gas sensing at room temperature has been highly desirable given the booming of the Internet of Things (IoT), zero-emission vehicles, and fuel cell technologies. Conventional metal oxide-based semiconducting gas sensors have been considered as suitable candidates given their low-cost, high [...] Read more.

Reversible H₂ gas sensing at room temperature has been highly desirable given the booming of the Internet of Things (IoT), zero-emission vehicles, and fuel cell technologies. Conventional metal oxide-based semiconducting gas sensors have been considered as suitable candidates given their low-cost, high sensitivity, and long stability. However, the dominant sensing mechanism is based on the chemisorption of gas molecules which requires elevated temperatures to activate the catalytic reaction of target gas molecules with chemisorbed O, leaving the drawbacks of high-power consumption and poor selectivity. In this work, we introduce an alternative candidate of cobalt oxysulfide derived from the calcination of self-assembled cobalt sulfide micro-cages. It is found that the majority of S atoms are replaced by O in cobalt oxysulfide, transforming the crystal structure to tetragonal coordination and slightly expanding the optical bandgap energy. The H₂ gas sensing performances of cobalt oxysulfide are fully reversible at room temperature, demonstrating peculiar p-type gas responses with a magnitude of 15% for 1% H₂ and a high degree of selectivity over CH₄, NO₂, and CO₂. Such excellent performances are possibly ascribed to the physisorption dominating the gas–matter interaction. This work demonstrates the great potentials of transition metal oxysulfide compounds for room-temperature fully reversible gas sensing. Full article

(This article belongs to the Special Issue Chemiresistive Sensors: Materials and Applications)

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59 pages, 2616 KiB

Open AccessEditor’s ChoiceReview

Electrical and Electrochemical Sensors Based on Carbon Nanotubes for the Monitoring of Chemicals in Water—A Review

by Gookbin Cho, Sawsen Azzouzi, Gaël Zucchi and Bérengère Lebental

Sensors 2022, 22(1), 218; https://doi.org/10.3390/s22010218 - 29 Dec 2021

Cited by 39 | Viewed by 5870

Abstract

Carbon nanotubes (CNTs) combine high electrical conductivity with high surface area and chemical stability, which makes them very promising for chemical sensing. While water quality monitoring has particularly strong societal and environmental impacts, a lot of critical sensing needs remain unmet by commercial [...] Read more.

Carbon nanotubes (CNTs) combine high electrical conductivity with high surface area and chemical stability, which makes them very promising for chemical sensing. While water quality monitoring has particularly strong societal and environmental impacts, a lot of critical sensing needs remain unmet by commercial technologies. In the present review, we show across 20 water monitoring analytes and 90 references that carbon nanotube-based electrochemical sensors, chemistors and field-effect transistors (chemFET) can meet these needs. A set of 126 additional references provide context and supporting information. After introducing water quality monitoring challenges, the general operation and fabrication principles of CNT water quality sensors are summarized. They are sorted by target analytes (pH, micronutrients and metal ions, nitrogen, hardness, dissolved oxygen, disinfectants, sulfur and miscellaneous) and compared in terms of performances (limit of detection, sensitivity and detection range) and functionalization strategies. For each analyte, the references with best performances are discussed. Overall, the most frequently investigated analytes are H⁺ (pH) and lead (with 18% of references each), then cadmium (14%) and nitrite (11%). Micronutrients and toxic metals cover 40% of all references. Electrochemical sensors (73%) have been more investigated than chemistors (14%) or FETs (12%). Limits of detection in the ppt range have been reached, for instance Cu(II) detection with a liquid-gated chemFET using SWCNT functionalized with peptide-enhanced polyaniline or Pb(II) detection with stripping voltammetry using MWCNT functionalized with ionic liquid-dithizone based bucky-gel. The large majority of reports address functionalized CNTs (82%) instead of pristine or carboxyl-functionalized CNTs. For analytes where comparison is possible, FET-based and electrochemical transduction yield better performances than chemistors (Cu(II), Hg(II), Ca(II), H₂O₂); non-functionalized CNTs may yield better performances than functionalized ones (Zn(II), pH and chlorine). Full article

(This article belongs to the Special Issue Micro- and Nanostructures for Sensing Applications)

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15 pages, 4756 KiB

Open AccessEditor’s ChoiceArticle

Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

by Zahoor Ahmad, Tuan-Khai Nguyen, Sajjad Ahmad, Cong Dai Nguyen and Jong-Myon Kim

Sensors 2022, 22(1), 179; https://doi.org/10.3390/s22010179 - 28 Dec 2021

Cited by 25 | Viewed by 3216

Abstract

This study proposes a fault diagnosis method (FD) for multistage centrifugal pumps (MCP) using informative ratio principal component analysis (Ir-PCA). To overcome the interference and background noise in the vibration signatures (VS) of the centrifugal pump, the fault diagnosis method selects the fault-specific [...] Read more.

This study proposes a fault diagnosis method (FD) for multistage centrifugal pumps (MCP) using informative ratio principal component analysis (Ir-PCA). To overcome the interference and background noise in the vibration signatures (VS) of the centrifugal pump, the fault diagnosis method selects the fault-specific frequency band (FSFB) in the first step. Statistical features in time, frequency, and wavelet domains were extracted from the fault-specific frequency band. In the second step, all of the extracted features were combined into a single feature vector called a multi-domain feature pool (MDFP). The multi-domain feature pool results in a larger dimension; furthermore, not all of the features are best for representing the centrifugal pump condition and can affect the condition classification accuracy of the classifier. To obtain discriminant features with low dimensions, this paper introduces a novel informative ratio principal component analysis in the third step. The technique first assesses the feature informativeness towards the fault by calculating the informative ratio between the feature within the class scatteredness and between-class distance. To obtain a discriminant set of features with reduced dimensions, principal component analysis was applied to the features with a high informative ratio. The combination of informative ratio-based feature assessment and principal component analysis forms the novel informative ratio principal component analysis. The new set of discriminant features obtained from the novel technique are then provided to the K-nearest neighbor (K-NN) condition classifier for multistage centrifugal pump condition classification. The proposed method outperformed existing state-of-the-art methods in terms of fault classification accuracy. Full article

(This article belongs to the Special Issue Sensing Technologies for Fault Diagnostics and Prognosis)

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32 pages, 14081 KiB

Open AccessEditor’s ChoiceReview

Recent Advances in Self-Powered Piezoelectric and Triboelectric Sensors: From Material and Structure Design to Frontier Applications of Artificial Intelligence

by Zetian Yang, Zhongtai Zhu, Zixuan Chen, Mingjia Liu, Binbin Zhao, Yansong Liu, Zefei Cheng, Shuo Wang, Weidong Yang and Tao Yu

Sensors 2021, 21(24), 8422; https://doi.org/10.3390/s21248422 - 17 Dec 2021

Cited by 21 | Viewed by 6464

Abstract

The development of artificial intelligence and the Internet of things has motivated extensive research on self-powered flexible sensors. The conventional sensor must be powered by a battery device, while innovative self-powered sensors can provide power for the sensing device. Self-powered flexible sensors can [...] Read more.

The development of artificial intelligence and the Internet of things has motivated extensive research on self-powered flexible sensors. The conventional sensor must be powered by a battery device, while innovative self-powered sensors can provide power for the sensing device. Self-powered flexible sensors can have higher mobility, wider distribution, and even wireless operation, while solving the problem of the limited life of the battery so that it can be continuously operated and widely utilized. In recent years, the studies on piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs) have mainly concentrated on self-powered flexible sensors. Self-powered flexible sensors based on PENGs and TENGs have been reported as sensing devices in many application fields, such as human health monitoring, environmental monitoring, wearable devices, electronic skin, human–machine interfaces, robots, and intelligent transportation and cities. This review summarizes the development process of the sensor in terms of material design and structural optimization, as well as introduces its frontier applications in related fields. We also look forward to the development prospects and future of self-powered flexible sensors. Full article

(This article belongs to the Special Issue Frontiers in Flexible Electronics and Sensors)

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34 pages, 2959 KiB

Open AccessEditor’s ChoiceReview

Sensors and Measurements for UAV Safety: An Overview

by Eulalia Balestrieri, Pasquale Daponte, Luca De Vito, Francesco Picariello and Ioan Tudosa

Sensors 2021, 21(24), 8253; https://doi.org/10.3390/s21248253 - 10 Dec 2021

Cited by 42 | Viewed by 7565

Abstract

Unmanned aerial vehicles’ (UAVs) safety has gained great research interest due to the increase in the number of UAVs in circulation and their applications, which has inevitably also led to an increase in the number of accidents in which these vehicles are involved. [...] Read more.

Unmanned aerial vehicles’ (UAVs) safety has gained great research interest due to the increase in the number of UAVs in circulation and their applications, which has inevitably also led to an increase in the number of accidents in which these vehicles are involved. The paper presents a classification of UAV safety solutions that can be found in the scientific literature, putting in evidence the fundamental and critical role of sensors and measurements in the field. Proposals from research on each proposed class concerning flight test procedures, in-flight solutions including soft propeller use, fault and damage detection, collision avoidance and safe landing, as well as ground solution including testing and injury and damage quantification measurements are discussed. Full article

(This article belongs to the Special Issue Advanced UAV-Based Sensor Technologies)

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14 pages, 1179 KiB

Open AccessEditor’s ChoiceArticle

Toward Modular and Flexible Open RAN Implementations in 6G Networks: Traffic Steering Use Case and O-RAN xApps

by Marcin Dryjański, Łukasz Kułacz and Adrian Kliks

Sensors 2021, 21(24), 8173; https://doi.org/10.3390/s21248173 - 7 Dec 2021

Cited by 58 | Viewed by 7929

Abstract

The development of cellular wireless systems has entered the phase when 5G networks are being deployed and the foundations of 6G solutions are being identified. However, in parallel to this, another technological breakthrough is observed, as the concept of open radio access networks [...] Read more.

The development of cellular wireless systems has entered the phase when 5G networks are being deployed and the foundations of 6G solutions are being identified. However, in parallel to this, another technological breakthrough is observed, as the concept of open radio access networks is coming into play. Together with advancing network virtualization and programmability, this may reshape the way the functionalities and services related to radio access are designed, leading to modular and flexible implementations. This paper overviews the idea of open radio access networks and presents ongoing O-RAN Alliance standardization activities in this context. The whole analysis is supported by a study of the traffic steering use case implemented in a modular way, following the open networking approach. Full article

(This article belongs to the Special Issue Next Generation Radio Communication Technologies)

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20 pages, 11035 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Recent Progress in Devices Based on Magnetoelectric Composite Thin Films

by Deepak Rajaram Patil, Ajeet Kumar and Jungho Ryu

Sensors 2021, 21(23), 8012; https://doi.org/10.3390/s21238012 - 30 Nov 2021

Cited by 17 | Viewed by 3842

Abstract

The strain-driven interfacial coupling between the ferromagnetic and ferroelectric constituents of magnetoelectric (ME) composites makes them potential candidates for novel multifunctional devices. ME composites in the form of thin-film heterostructures show promising applications in miniaturized ME devices. This article reports the recent advancement [...] Read more.

The strain-driven interfacial coupling between the ferromagnetic and ferroelectric constituents of magnetoelectric (ME) composites makes them potential candidates for novel multifunctional devices. ME composites in the form of thin-film heterostructures show promising applications in miniaturized ME devices. This article reports the recent advancement in ME thin-film devices, such as highly sensitive magnetic field sensors, ME antennas, integrated tunable ME inductors, and ME band-pass filters, is discussed. (Pb₁_−xZr_x)TiO₃ (PZT), Pb(Mg_1/3Nb_2/3)O₃-PbTiO₃ (PMN-PT), Aluminium nitride (AlN), and Al₁_−xSc_xN are the most commonly used piezoelectric constituents, whereas FeGa, FeGaB, FeCo, FeCoB, and Metglas (FeCoSiB alloy) are the most commonly used magnetostrictive constituents in the thin film ME devices. The ME field sensors offer a limit of detection in the fT/Hz^1/2 range at the mechanical resonance frequency. However, below resonance, different frequency conversion techniques with AC magnetic or electric fields or the delta-E effect are used. Noise floors of 1–100 pT/Hz^1/2 at 1 Hz were obtained. Acoustically actuated nanomechanical ME antennas operating at a very-high frequency as well as ultra-high frequency (0.1–3 GHz) range, were introduced. The ME antennas were successfully miniaturized by a few orders smaller in size compared to the state-of-the-art conventional antennas. The designed antennas exhibit potential application in biomedical devices and wearable antennas. Integrated tunable inductors and band-pass filters tuned by electric and magnetic field with a wide operating frequency range are also discussed along with miniaturized ME energy harvesters. Full article

(This article belongs to the Special Issue Magnetoelectric Thin-Film Based Devices)

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24 pages, 4032 KiB

Open AccessEditor’s ChoiceArticle

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

by Xiangbing Zhan, Huiyun Long, Fangfang Gou, Xun Duan, Guangqian Kong and Jia Wu

Sensors 2021, 21(23), 7996; https://doi.org/10.3390/s21237996 - 30 Nov 2021

Cited by 27 | Viewed by 3178

Abstract

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional [...] Read more.

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients’ medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians. Full article

(This article belongs to the Collection Medical Applications of Sensor Systems and Devices)

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20 pages, 3312 KiB

Open AccessEditor’s ChoiceArticle

Robust Data Association Using Fusion of Data-Driven and Engineered Features for Real-Time Pedestrian Tracking in Thermal Images

by Mircea Paul Muresan, Sergiu Nedevschi and Radu Danescu

Sensors 2021, 21(23), 8005; https://doi.org/10.3390/s21238005 - 30 Nov 2021

Cited by 22 | Viewed by 2799

Abstract

Object tracking is an essential problem in computer vision that has been extensively researched for decades. Tracking objects in thermal images is particularly difficult because of the lack of color information, low image resolution, or high similarity between objects of the same class. [...] Read more.

Object tracking is an essential problem in computer vision that has been extensively researched for decades. Tracking objects in thermal images is particularly difficult because of the lack of color information, low image resolution, or high similarity between objects of the same class. One of the main challenges in multi-object tracking, also referred to as the data association problem, is finding the correct correspondences between measurements and tracks and adapting the object appearance changes over time. We addressed this challenge of data association for thermal images by proposing three contributions. The first contribution consisted of the creation of a data-driven appearance score using five Siamese Networks, which operate on the image detection and on parts of it. Secondly, we engineered an original edge-based descriptor that improves the data association process. Lastly, we proposed a dataset consisting of pedestrian instances that were recorded in different scenarios and are used for training the Siamese Networks. The data-driven part of the data association score offers robustness, while feature engineering offers adaptability to unknown scenarios and their combination leads to a more powerful tracking solution. Our approach had a running time of 25 ms and achieved an average precision of 86.2% on publicly available benchmarks, containing real-world scenarios, as shown in the evaluation section. Full article

(This article belongs to the Special Issue Thermal Imaging Sensors and Their Applications)

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22 pages, 5463 KiB

Open AccessEditor’s ChoiceArticle

Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications

by Li-Yu Lo, Chi Hao Yiu, Yu Tang, An-Shik Yang, Boyang Li and Chih-Yung Wen

Sensors 2021, 21(23), 7888; https://doi.org/10.3390/s21237888 - 27 Nov 2021

Cited by 45 | Viewed by 7660

Abstract

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, a UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving [...] Read more.

The ever-burgeoning growth of autonomous unmanned aerial vehicles (UAVs) has demonstrated a promising platform for utilization in real-world applications. In particular, a UAV equipped with a vision system could be leveraged for surveillance applications. This paper proposes a learning-based UAV system for achieving autonomous surveillance, in which the UAV can be of assistance in autonomously detecting, tracking, and following a target object without human intervention. Specifically, we adopted the YOLOv4-Tiny algorithm for semantic object detection and then consolidated it with a 3D object pose estimation method and Kalman filter to enhance the perception performance. In addition, UAV path planning for a surveillance maneuver is integrated to complete the fully autonomous system. The perception module is assessed on a quadrotor UAV, while the whole system is validated through flight experiments. The experiment results verified the robustness, effectiveness, and reliability of the autonomous object tracking UAV system in performing surveillance tasks. The source code is released to the research community for future reference. Full article

(This article belongs to the Special Issue Stereo Vision-Based Perception, Navigation and Control for Intelligent Autonomous Systems)

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