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Human-Centric Sensing Technology and Systems

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 20035

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Prof. Dr. Feng Hong

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Guest Editor

Department of Computer Science and Technology, Ocean University of China, Qingdao, China
Interests: smart sensing; ubiquitous computing
Special Issues, Collections and Topics in MDPI journals

Dr. Jiadi Yu

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Guest Editor

Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: mobile computing and sensing; cyber security and privacy; Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals

Dr. Yan Wang

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Guest Editor

Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA
Interests: mobile and pervasive computing and embedded systems; cyber security and privacy; wireless networks and sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human-centric sensing plays a crucially important role in the domains of smart-home and office environments, including safety protection, well-being monitoring/management, healthcare, and smart-appliance interaction. Various physical or physiological sensors are interconnected and designed to sense a broad spectrum of contexts for human beings, laying the foundation of pervasive computing. However, sensor technologies have several limitations relating to deployment cost, usability, and adherence issues, which render them unacceptable for practical use.

Consequently, the pursuit of convenience in human perception necessitates a wireless, sensorless, and contactless sensing paradigm. Recent novel technologies have shown the potential of reusing wireless signals (such as WiFi, mmWave, RFID, LoRa, acoustic, light, and radiofrequency) or environmental infrastructures originally designed for lighting or data transmission to sense human activities. Such studies thereby realize a set of emerging applications, ranging from intrusion detection, daily activity recognition, and gesture recognition to monitoring of vital signs and user identification, involving even finer-grained motion sensing. Relevant human-centric sensing technologies and solutions are still in their early stages.

The Guest Editors encourage submissions of papers addressing physical models, technologies, and applications of human-centric sensing. Original research contributions, tutorials, case studies, and review papers are also welcomed. Manuscripts should provide content that is accessible to general audiences working in the field of sensing systems.

Topics of interest for this Special Issue include (but are not limited to):

Human-centric wireless signal analytic model;
Intrusion detection;
Identity recognition;
Activity recognition;
Gesture detection and recognition;
Vital signs monitoring;
Localization and tracking;
Learning algorithms and models for human behavior perception;
Applications and deployment experiences.

Prof. Dr. Feng Hong
Dr. Jiadi Yu
Dr. Yan Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

smart sensing
wireless sensing
signal processing
passive sensing
activity recognition
gesture recognition
human identification
localization
human counting
respiration monitoring
heartbeat monitoring
signal imaging
vital signs monitoring

Related Special Issue

Human-Centric Sensing Technology and Systems: 2nd Edition in Sensors

Published Papers (9 papers)

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Research

10 pages, 2752 KiB

Open AccessCommunication

Acoustic Forceps Based on Focused Acoustic Vortices with Different Topological Charges

by Libin Du, Gehao Hu, Yantao Hu and Qingdong Wang

Sensors 2023, 23(15), 6874; https://doi.org/10.3390/s23156874 - 3 Aug 2023

Cited by 1 | Viewed by 926

Abstract

For enhanced energy concentration with improved flexibility for object manipulation, a focused acoustic vortex (FAV) is designed using a sector planar piston transducer array and acoustic lens that can produce the effective concentration of the acoustic field to perform the focusing function. Compared to the Gaussian beam, which tends to cause the object to deviate from the axis of acoustic propagation, FAVs can form a central valley region to firmly bind the objects, thus preventing off-target effects. The heat energy in the paraxial region is transferred to the vortex center in the form of heat transfer so that the temperature-sensitive liposomes captured can quickly release drugs, which has a good effect on targeted drug administration. The focused acoustic wave stopped acting on the tissue (gel) for 2 s, the temperature of the vortex center continued to rise, reaching 41.5 °C at the moment of 3.7 s, at which point the liposomes began to release the drug. The FAVs capture the drug and use its thermal effect to achieve accurate and rapid treatment. The simulation results show that the drug release temperature of temperature-sensitive liposomes can be achieved by controlling the action time of the vortices. This study provides a reliable theoretical basis for the clinical application of targeted drugs. Full article

(This article belongs to the Special Issue Human-Centric Sensing Technology and Systems)

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17 pages, 5044 KiB

Open AccessArticle

Efficient Fine Tuning for Fashion Object Detection

by Benjiang Ma and Wenjin Xu

Sensors 2023, 23(13), 6083; https://doi.org/10.3390/s23136083 - 1 Jul 2023

Viewed by 3596

Abstract

Pre-trained models have achieved success in object detection. However, challenges remain due to dataset noise and lack of domain-specific data, resulting in weaker zero-shot capabilities in specialized fields such as fashion imaging. We addressed this by constructing a novel clothing object detection benchmark, Garment40K, which includes more than 140,000 human images with bounding boxes and over 40,000 clothing images. Each clothing item within this dataset is accompanied by its corresponding category and textual description. The dataset covers 2 major categories, pants and tops, which are further divided into 15 fine-grained subclasses, providing a rich and high-quality clothing resource. Leveraging this dataset, we propose an efficient fine-tuning method based on the Grounding DINO framework to tackle the issue of missed and false detections of clothing targets. This method incorporates additional similarity loss constraints and adapter modules, leading to a significantly enhanced model named Improved Grounding DINO. By fine-tuning only a small number of additional adapter module parameters, we considerably reduced computational costs while achieving performance comparable to full parameter fine tuning. This allows our model to be conveniently deployed on a variety of low-cost visual sensors. Our Improved Grounding DINO demonstrates considerable performance improvements in computer vision applications in the clothing domain. Full article

(This article belongs to the Special Issue Human-Centric Sensing Technology and Systems)

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25 pages, 21355 KiB

Open AccessArticle

Echo-ID: Smartphone Placement Region Identification for Context-Aware Computing

by Xueting Jiang, Zhongning Zhao, Zhiyuan Li and Feng Hong

Sensors 2023, 23(9), 4302; https://doi.org/10.3390/s23094302 - 26 Apr 2023

Viewed by 1368

Abstract

Region-function combinations are essential for smartphones to be intelligent and context-aware. The prerequisite for providing intelligent services is that the device can recognize the contextual region in which it resides. The existing region recognition schemes are mainly based on indoor positioning, which require pre-installed infrastructures or tedious calibration efforts or memory burden of precise locations. In addition, location classification recognition methods are limited by either their recognition granularity being too large (room-level) or too small (centimeter-level, requiring training data collection at multiple positions within the region), which constrains the applications of providing contextual awareness services based on region function combinations. In this paper, we propose a novel mobile system, called Echo-ID, that enables a phone to identify the region in which it resides without requiring any additional sensors or pre-installed infrastructure. Echo-ID applies Frequency Modulated Continuous Wave (FMCW) acoustic signals as its sensing medium which is transmitted and received by the speaker and microphones already available in common smartphones. The spatial relationships among the surrounding objects and the smartphone are extracted with a signal processing procedure. We further design a deep learning model to achieve accurate region identification, which calculate finer features inside the spatial relations, robust to phone placement uncertainty and environmental variation. Echo-ID requires users only to put their phone at two orthogonal angles for 8.5 s each inside a target region before use. We implement Echo-ID on the Android platform and evaluate it with Xiaomi 12 Pro and Honor-10 smartphones. Our experiments demonstrate that Echo-ID achieves an average accuracy of 94.6% for identifying five typical regions, with an improvement of 35.5% compared to EchoTag. The results confirm Echo-ID’s robustness and effectiveness for region identification. Full article

(This article belongs to the Special Issue Human-Centric Sensing Technology and Systems)

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10 pages, 1843 KiB

Open AccessArticle

Design Principle and Proofing of a New Smart Textile Material That Acts as a Sensor for Immobility in Severe Bed-Confined Patients

by Bogdan Florin Iliescu, Vlad Niki Mancasi, Ionut Dumitru Ilie, Iulian Mancasi, Bogdan Costachescu and Daniel Ilie Rotariu

Sensors 2023, 23(5), 2573; https://doi.org/10.3390/s23052573 - 25 Feb 2023

Cited by 2 | Viewed by 1419

Abstract

The immobility of patients confined to continuous bed rest continues to raise a couple of very serious challenges for modern medicine. In particular, the overlooking of sudden onset immobility (as in acute stroke) and the delay in addressing the underlying conditions are of utmost importance for the patient and, in the long term, for the medical and social systems. This paper describes the design principles and concrete implementation of a new smart textile material that can form the substrate of intensive care bedding, that acts as a mobility/immobility sensor in itself. The textile sheet acts as a multi-point pressure-sensitive surface that sends continuous capacitance readings through a connector box to a computer running a dedicated software. The design of the capacitance circuit ensures enough individual points to provide an accurate description of the overlying shape and weight. We describe the textile composition and circuit design as well as the preliminary data collected during testing to demonstrate the validity of the complete solution. These results suggest that the smart textile sheet is a very sensitive pressure sensor and can provide continuous discriminatory information to allow for the very sensitive, real-time detection of immobility. Full article

(This article belongs to the Special Issue Human-Centric Sensing Technology and Systems)

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21 pages, 6588 KiB

Open AccessArticle

Rope Jumping Strength Monitoring on Smart Devices via Passive Acoustic Sensing

by Xiaowen Hou and Chao Liu

Sensors 2022, 22(24), 9739; https://doi.org/10.3390/s22249739 - 12 Dec 2022

Cited by 1 | Viewed by 2126

Abstract

Rope jumping, as a fitness exercise recommended by many sports medicine practitioners, can improve cardiorespiratory capacity and physical coordination. Existing rope jump monitoring systems have limitations in terms of convenience, comfort, and exercise intensity evaluation. This paper presents a rope jump monitoring system using passive acoustic sensing. Our system exploits the off-the-shelf smartphone and headphones to capture the user’s rope-jumping sound and breathing sound after exercise. Given the captured acoustic data, the system uses a short-time energy-based approach and the high correlation between rope jumping cycles to detect the rope-jumping sound frames, then applies a dual-threshold endpoint detection algorithm to calculate the number of rope jumps. Finally, our system performs regression predictions of exercise intensity based on features extracted from the jumping speed and the mel spectrograms of the user’s breathing sound. The significant advantage of the system lies in the solution of the problem of poorly characterized mel spectrograms. We employ an attentive mechanism-based GAN to generate optimized breathing sound mel spectrograms and apply domain adversarial adaptive in the network to improve the migration capability of the system. Through extensive experiments, our system achieves (on average) 0.32 and 2.3% error rates for the rope jumping count and exercise intensity evaluation, respectively. Full article

(This article belongs to the Special Issue Human-Centric Sensing Technology and Systems)

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18 pages, 2328 KiB

Open AccessArticle

Achieving Adaptive Visual Multi-Object Tracking with Unscented Kalman Filter

by Guowei Zhang, Jiyao Yin, Peng Deng, Yanlong Sun, Lin Zhou and Kuiyuan Zhang

Sensors 2022, 22(23), 9106; https://doi.org/10.3390/s22239106 - 23 Nov 2022

Cited by 9 | Viewed by 2578

Abstract

As an essential part of intelligent monitoring, behavior recognition, automatic driving, and others, the challenge of multi-object tracking is still to ensure tracking accuracy and robustness, especially in complex occlusion environments. Aiming at the issues of the occlusion, background noise, and motion state violent change for multi-object in a complex scene, an improved DeepSORT algorithm based on YOLOv5 is proposed for multi-object tracking to enhance the speed and accuracy of tracking. Firstly, a general object motion model is devised, which is similar to the variable acceleration motion model, and a multi-object tracking framework with the general motion model is established. Then, the latest YOLOv5 algorithm, which has satisfactory detection accuracy, is utilized to obtain the object information as the input of multi-object tracking. An unscented Kalman filter (UKF) is proposed to estimate the motion state of multi-object to solve nonlinear errors. In addition, the adaptive factor is introduced to evaluate observation noise and detect abnormal observations so as to adaptively adjust the innovation covariance matrix. Finally, an improved DeepSORT algorithm for multi-object tracking is formed to promote robustness and accuracy. Extensive experiments are carried out on the MOT16 data set, and we compare the proposed algorithm with the DeepSORT algorithm. The results indicate that the speed and precision of the improved DeepSORT are increased by 4.75% and 2.30%, respectively. Especially in the MOT16 of the dynamic camera, the improved DeepSORT shows better performance. Full article

(This article belongs to the Special Issue Human-Centric Sensing Technology and Systems)

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14 pages, 4370 KiB

Open AccessArticle

A Novel Deep Neural Network Method for HAR-Based Team Training Using Body-Worn Inertial Sensors

by Yun-Chieh Fan, Yu-Hsuan Tseng and Chih-Yu Wen

Sensors 2022, 22(21), 8507; https://doi.org/10.3390/s22218507 - 4 Nov 2022

Cited by 5 | Viewed by 2504

Abstract

Human activity recognition (HAR) became a challenging issue in recent years. In this paper, we propose a novel approach to tackle indistinguishable activity recognition based on human wearable sensors. Generally speaking, vision-based solutions struggle with low illumination environments and partial occlusion problems. In contrast, wearable inertial sensors can tackle this problem and avoid revealing personal privacy. We address the issue by building a multistage deep neural network framework that interprets accelerometer, gyroscope, and magnetometer data that provide useful information of human activities. Initially, the stage of variational autoencoders (VAE) can extract the crucial information from raw data of inertial measurement units (IMUs). Furthermore, the stage of generative adversarial networks (GANs) can generate more realistic human activities. Finally, the transfer learning method is applied to enhance the performance of the target domain, which builds a robust and effective model to recognize human activities. Full article

(This article belongs to the Special Issue Human-Centric Sensing Technology and Systems)

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24 pages, 7174 KiB

Open AccessArticle

DFTrans: Dual Frequency Temporal Attention Mechanism-Based Transportation Mode Detection

by Pu Wang and Yongguo Jiang

Sensors 2022, 22(21), 8499; https://doi.org/10.3390/s22218499 - 4 Nov 2022

Viewed by 1516

Abstract

In recent years, with the diversification of people’s modes of transportation, a large amount of traffic data is generated when people travel every day, and this data can help transportation mode detection to be of great use in a variety of applications. Although transportation mode detection has been investigated, there are still challenges in terms of accuracy and robustness. This paper presents a novel transportation mode detection algorithm, DFTrans, which is based on Temporal Block and Attention Block. Low- and high-frequency components of traffic sequences are obtained using discrete wavelet transforms. A two-channel encoder is carefully designed to accurately capture the temporal and spatial correlation between low- and high-frequency components in both long- and short-term patterns. With the Temporal Block, the inductive bias of the CNN is introduced at high frequencies to improve generalization performance. At the same time, the network is generated with the same length as the input, ensuring a long effective history. Low frequencies are passed through Attention Block, which has fewer parameters to capture the global focus and solves the problem that RNNs cannot be computed in parallel. After fusing the output of the feature by Temporal Block and Attention Block, the classification results are output by MLP. Extensive experimental results show that the DFTrans algorithm achieves macro F1 scores of 86.34% on the real-world SHL dataset and 87.64% on the HTC dataset. Our model can better identify eight modes of transportation, including stationary, walking, running, cycling, bus, car, underground, and train, and has better performance in transportation mode detection than other baseline algorithms. Full article

(This article belongs to the Special Issue Human-Centric Sensing Technology and Systems)

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16 pages, 2764 KiB

Open AccessArticle

Automatic Detection of the Cyclic Alternating Pattern of Sleep and Diagnosis of Sleep-Related Pathologies Based on Cardiopulmonary Resonance Indices

by Jiajia Cui, Zhipei Huang and Jiankang Wu

Sensors 2022, 22(6), 2225; https://doi.org/10.3390/s22062225 - 14 Mar 2022

Cited by 3 | Viewed by 2497

Abstract

The cyclic alternating pattern is the periodic electroencephalogram activity occurring during non-rapid eye movement sleep. It is a marker of sleep instability and is correlated with several sleep-related pathologies. Considering the connection between the human heart and brain, our study explores the feasibility of using cardiopulmonary features to automatically detect the cyclic alternating pattern of sleep and hence diagnose sleep-related pathologies. By statistically analyzing and comparing the cardiopulmonary characteristics of a healthy group and groups with sleep-related diseases, an automatic recognition scheme of the cyclic alternating pattern is proposed based on the cardiopulmonary resonance indices. Using the Hidden Markov and Random Forest, the scheme combines the variation and stability of measurements of the coupling state of the cardiopulmonary system during sleep. In this research, the F1 score of the sleep-wake classification reaches 92.0%. In terms of the cyclic alternating pattern, the average recognition rate of A-phase reaches 84.7% on the CAP Sleep Database of 108 cases of people. The F1 score of disease diagnosis is 87.8% for insomnia and 90.0% for narcolepsy. Full article

(This article belongs to the Special Issue Human-Centric Sensing Technology and Systems)

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