Taylor, W., Dashtipour, K., Shah, S. A. , Hussain, A., Abbasi, Q. H. and Imran, M. A. (2021) Radar sensing for activity classification in elderly people exploiting micro-doppler signatures using machine learning. Sensors, 21(11), 3881. (doi: 10.3390/s21113881)
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Abstract
The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in activities of daily living (ADLs). Detecting ADLs provides possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper presents fall detection in elderly people based on radar image classification by examining their daily routine activities, using radar data that were previously collected for 99 volunteers. Machine learning techniques are used classify six human activities, namely walking, sitting, standing, picking up objects, drinking water and fall events. Different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long short-term memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification. To obtain optimum results, we applied data processing techniques, such as principal component analysis and data augmentation, to the available radar images. The aim of this paper is to improve upon the results achieved using a publicly available dataset to further improve upon research of fall detection systems. It was found out that the best results were obtained using the CNN algorithm with principal component analysis and data augmentation together to obtain a result of 95.30% accuracy. The results also demonstrated that principal component analysis was most beneficial when the training data were expanded by augmentation of the available data. The results of our proposed approach, in comparison to the state of the art, have shown the highest accuracy.
| Item Type: | Articles |
|---|---|
| Additional Information: | William Taylor’s studentship is funded by CENSIS UK through the Scottish Funding Council in collaboration with British Telecom. This work is supported in part by EPSRC EP/T021020/1 and EP/T021063/1. |
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Dashtipour, Dr Kia and Abbasi, Professor Qammer and Imran, Professor Muhammad and Taylor, William and Shah, Mr Syed |
| Creator Roles: | Taylor, W.Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – review and editing Dashtipour, K.Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing – review and editing, Resources Shah, S.Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Writing – review and editing Abbasi, Q.Conceptualization, Resources, Writing – review and editing, Funding acquisition Imran, M.Resources, Writing – review and editing, Conceptualization, Funding acquisition |
| Authors: | Taylor, W., Dashtipour, K., Shah, S. A., Hussain, 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 |
| Copyright Holders: | Copyright © 2021 The Authors |
| First Published: | First published in Sensors 21(11):3881 |
| Publisher Policy: | Reproduced under a Creative Commons License |
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