Evaluation of deep learning models in contactless human motion detection system for next generation healthcare

Song, Y., Taylor, W. , Ge, Y., Usman, M., Imran, M. A. and Abbasi, Q. H. (2022) Evaluation of deep learning models in contactless human motion detection system for next generation healthcare. Scientific Reports, 12, 21592. (doi: 10.1038/s41598-022-25403-y) (PMID:36517511) (PMCID:PMC9751145)

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Abstract

Recent decades have witnessed the growing importance of human motion detection systems based on artificial intelligence (AI). The growing interest in human motion detection systems is the advantages of automation in the monitoring of patients remotely and giving warnings to doctors promptly. Currently, wearable devices are frequently used for human motion detection systems. However, such devices have several limitations, such as the elderly not wearing devices due to lack of comfort or forgetfulness and/or battery limitations. To overcome the problems of wearable devices, we propose an AI-driven human motion detection system (deep learning-based system) using channel state information (CSI) extracted from Radio Frequency (RF) signals. The main contribution of this paper is to improve the performance of the deep learning models through techniques, including structure modification and dimension reduction of the original data. In this work, We firstly collected the CSI data with the center frequency 5.32 GHz and implemented the structure of the basic deep learning network in our previous work. After that, we changed the basic deep learning network by increasing the depth, increasing the width, adapting some advanced network structures, and reducing dimensions. After finishing those modifications, we observed the results and analyzed how to further improve the deep learning performance of this contactless AI-enabled human motion detection system. It can be found that reducing the dimension of the original data can work better than modifying the structure of the deep learning model.

Item Type:Articles
Additional Information:This work was supported in part by Engineering and Physical Sciences Research Council (EPSRC) Grants: EP/T021063/1 and EP/T021020/1.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Ge, Yao and Abbasi, Professor Qammer and Imran, Professor Muhammad and Usman, Dr Muhammad and Taylor, William
Authors: Song, Y., Taylor, W., Ge, Y., Usman, M., Imran, M. A., and Abbasi, Q. H.
College/School:College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Journal Name:Scientific Reports
Publisher:Nature Research
ISSN:2045-2322
ISSN (Online):2045-2322
Copyright Holders:Copyright © 2022 The Authors
First Published:First published in Scientific Reports 12: 21592
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
307829Quantum-Inspired Imaging for Remote Monitoring of Health & Disease in Community HealthcareJonathan CooperEngineering and Physical Sciences Research Council (EPSRC)EP/T021020/1ENG - Biomedical Engineering