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Intelligent Wearable Systems and Computational Techniques

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Dear Colleagues,

Wearable systems have attracted attention in many fields, such as healthcare, sport, and security. Their development requires tight collaborations of different sectors (material science, fashion and functional garment design, electronics, advanced manufacturing, data analysis and medicine, etc.) and involvement of efficient computational tools in order to increase the intelligence capacity of these devices. In this context, we are organizing a Special Issue in Sensors to offer a systematic overview of this emerging research field and provide innovative interdisciplinary approaches. This issue will provide a leading forum for disseminating the latest results of research, development, and applications of wearable systems with a strong involvement of artificial intelligence.

Prof. Dr. Xianyi Zeng
Prof. Dr. Ludovic Koehl
Dr. Xuyuan Tao
Prof. Dr. Wei Chen
Guest Editors

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Keywords

miniature sensors
imbedded systems
device integration
human health and activity
wearable decision support system
human big data mining
human–machine interaction/cooperation

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Research

18 pages, 6533 KiB

Open AccessArticle

Compensation for Electrode Detachment in Electrical Impedance Tomography with Wearable Textile Electrodes

by Chang-Lin Hu, Zong-Yan Lin, Shu-Yun Hu, I-Cheng Cheng, Chih-Hsien Huang, Yu-Hao Li, Chien-Ju Li and Chii-Wann Lin

Sensors 2022, 22(24), 9575; https://doi.org/10.3390/s22249575 - 7 Dec 2022

Cited by 1 | Viewed by 1807

Abstract

Electrical impedance tomography (EIT) is a radiation-free and noninvasive medical image reconstruction technique in which a current is injected and the reflected voltage is received through electrodes. EIT electrodes require good connection with the skin for data acquisition and image reconstruction. However, detached electrodes are a common occurrence and cause measurement errors in EIT clinical applications. To address these issues, in this study, we proposed a method for detecting faulty electrodes using the differential voltage value of the detached electrode in an EIT system. Additionally, we proposed the voltage-replace and voltage-shift methods to compensate for invalid data from the faulty electrodes. In this study, we present the simulation, experimental, and in vivo chest results of our proposed methods to verify and evaluate the feasibility of this approach. Full article

(This article belongs to the Special Issue Intelligent Wearable Systems and Computational Techniques)

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16 pages, 3653 KiB

Open AccessArticle

Preliminary Evaluation of a System with On-Body and Aerial Sensors for Monitoring Working Dogs

by Marc Foster, Tianfu Wu, David L. Roberts and Alper Bozkurt

Sensors 2022, 22(19), 7631; https://doi.org/10.3390/s22197631 - 8 Oct 2022

Cited by 2 | Viewed by 2297

Abstract

This paper presents a system for behavioral, environmental, and physiological monitoring of working dogs using on-body and aerial sensors. The proof of concept study presented here includes two trained dogs performing nine scent detection tasks in an uncontrolled environment encompassing approximately two acres. The dogs were outfitted with a custom designed wearable harness to monitor their heart rate, activity levels and skin temperature. We utilized a commercially available micro-air vehicle to perform aerial sensing by tracking the terrain and movement of the dog in the outdoor space. The dogs were free to explore the space working at maximal speeds to complete a scent-based search-and-retrieval task. Throughout the experiment, the harness data was transferred to a base station via Wi-Fi in real-time. In this work, we also focused on testing the performance of a custom 3D electrode with application specific ergonomic improvements and adaptive filter processing techniques to recover as much electrocardiography data as possible during high intensity motion activity. We were able to recover and use 84% of the collected data where we observed a trend of heart rate generally increasing immediately after successful target localization. For tracking the dogs in the aerial video footage, we applied a state-of-the-art deep learning algorithm designed for online object tracking. Both qualitative and quantitative tracking results are very promising. This study presents an initial effort towards deployment of on-body and aerial sensors to monitor the working dogs and their environments during scent detection and search and rescue tasks in order to ensure their welfare, enable novel dog-machine interfaces, and allow for higher success rate of remote and automated task performance. Full article

(This article belongs to the Special Issue Intelligent Wearable Systems and Computational Techniques)

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