An intelligent non-invasive real time human activity recognition system for next-generation healthcare

Taylor, W., Shah, S. A. , Dashtipour, K., Zahid, A., Abbasi, Q. H. and Imran, M. A. (2020) An intelligent non-invasive real time human activity recognition system for next-generation healthcare. Sensors, 20(9), 2653. (doi: 10.3390/s20092653)

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Abstract

Human motion detection is getting considerable attention in the field of Artificial Intelligence (AI) driven healthcare systems. Human motion can be used to provide remote healthcare solutions for vulnerable people by identifying particular movements such as falls, gait and breathing disorders. This can allow people to live more independent lifestyles and still have the safety of being monitored if more direct care is needed. At present wearable devices can provide real-time monitoring by deploying equipment on a person’s body. However, putting devices on a person’s body all the time makes it uncomfortable and the elderly tend to forget to wear them, in addition to the insecurity of being tracked all the time. This paper demonstrates how human motions can be detected in a quasi-real-time scenario using a non-invasive method. Patterns in the wireless signals present particular human body motions as each movement induces a unique change in the wireless medium. These changes can be used to identify particular body motions. This work produces a dataset that contains patterns of radio wave signals obtained using software-defined radios (SDRs) to establish if a subject is standing up or sitting down as a test case. The dataset was used to create a machine learning model, which was used in a developed application to provide a quasi-real-time classification of standing or sitting state. The machine-learning model was able to achieve 96.70% accuracy using the Random Forest algorithm using 10 fold cross-validation. A benchmark dataset of wearable devices was compared to the proposed dataset and results showed the proposed dataset to have similar accuracy of nearly 90%. The machine-learning models developed in this paper are tested for two activities but the developed system is designed and applicable for detecting and differentiating x number of activities.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Abbasi, Dr Qammer and Imran, Professor Muhammad and Taylor, William and Zahid, Mr Adnan and Shah, Mr Syed and Dashtipour, Dr Kia
Creator Roles:
Taylor, W.Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – review and editing
Shah, S. A.Conceptualization, Methodology, Software, Validation, Investigation, Resources, Writing – review and editing
Dashtipour, K.Conceptualization, Methodology, Software, Validation, Investigation, Resources, Writing – review and editing
Zahid, A.Conceptualization, Resources, Writing – review and editing, Funding acquisition
Abbasi, Q. H.Conceptualization, Resources, Writing – review and editing, Funding acquisition
Imran, M. A.Conceptualization, Resources, Writing – review and editing, Funding acquisition
Authors: Taylor, W., Shah, S. A., Dashtipour, K., Zahid, A., Abbasi, Q. H., and Imran, M. A.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Sensors
Publisher:MDPI
ISSN:1424-8220
ISSN (Online):1424-8220
Published Online:06 May 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Sensors 20(9): 2653
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
172865EPSRC DTP 16/17 and 17/18Tania GalabovaEngineering and Physical Sciences Research Council (EPSRC)EP/N509668/1Research and Innovation Services