Discrete human activity recognition and fall detection by combining FMCW RADAR data of heterogeneous environments for independent assistive living

Saeed, U., Shah, S. Y., Shah, S. A. , Ahmad, J., Alotaibi, A. A., Althobaiti, T., Ramzan, N., Alomainy, A. and Abbasi, Q. H. (2021) Discrete human activity recognition and fall detection by combining FMCW RADAR data of heterogeneous environments for independent assistive living. Electronics, 10(18), 2237. (doi: 10.3390/electronics10182237)

Abstract

Human activity monitoring is essential for a variety of applications in many fields, particularly healthcare. The goal of this research work is to develop a system that can effectively detect fall/collapse and classify other discrete daily living activities such as sitting, standing, walking, drinking, and bending. For this paper, a publicly accessible dataset is employed, which is captured at various geographical locations using a 5.8 GHz Frequency-Modulated Continuous-Wave (FMCW) RADAR. A total of ninety-nine participants, including young and elderly individuals, took part in the experimental campaign. During data acquisition, each aforementioned activity was recorded for 5–10 s. Through the obtained data, we generated the micro-doppler signatures using short-time Fourier transform by exploiting MATLAB tools. Subsequently, the micro-doppler signatures are validated, trained, and tested using a state-of-the-art deep learning algorithm called Residual Neural Network or ResNet. The ResNet classifier is developed in Python, which is utilised to classify six distinct human activities in this study. Furthermore, the metrics used to analyse the trained model’s performance are precision, recall, F1-score, classification accuracy, and confusion matrix. To test the resilience of the proposed method, two separate experiments are carried out. The trained ResNet models are put to the test by subject-independent scenarios and unseen data of the above-mentioned human activities at diverse geographical spaces. The experimental results showed that ResNet detected the falling and rest of the daily living human activities with decent accuracy.

• Item Type: Articles
• Additional Information: This work was supported in part by the Taif University, Taif, Saudi Arabia, through the Taif University Research Grant under Project TURSP-2020/277.
• Keywords: Radio-frequency, FMCW RADAR, next generation healthcare, contactless monitoring, fall detection, deep learning, ResNet.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Abbasi, Professor Qammer and Shah, Mr Syed

Creator Roles

Abbasi, Q. H.: Conceptualization, Formal analysis, Investigation
Shah, S. A.: Conceptualization, Formal analysis, Funding acquisition, Investigation, Project administration, Supervision

• Authors: Saeed, U., Shah, S. Y., Shah, S. A., Ahmad, J., Alotaibi, A. A., Althobaiti, T., Ramzan, N., Alomainy, A., and Abbasi, Q. H.
• 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: Electronics
• Publisher: MDPI
• ISSN: 2079-9292
• ISSN (Online): 2079-9292
• Published Online: 12 September 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Electronics 10(18): 2237
• Publisher Policy: Reproduced under a Creative Commons License