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)

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, Dr 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

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
307829		Quantum-Inspired Imaging for Remote Monitoring of Health & Disease in Community Healthcare	Jonathan Cooper	Engineering and Physical Sciences Research Council (EPSRC)	EP/T021020/1	ENG - Biomedical Engineering