Umer Saeed
Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living
Saeed, Umer; Shah, Syed Yaseen; Shah, Syed Aziz; Ahmad, Jawad; Alotaibi, Abdullah Alhumaidi; Althobaiti, Turke; Ramzan, Naeem; Alomainy, Akram; Abbasi, Qammer H.
Authors
Syed Yaseen Shah
Syed Aziz Shah
Dr Jawad Ahmad J.Ahmad@napier.ac.uk
Lecturer
Abdullah Alhumaidi Alotaibi
Turke Althobaiti
Naeem Ramzan
Akram Alomainy
Qammer H. Abbasi
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.
Citation
Saeed, U., Shah, S. Y., Shah, S. A., Ahmad, J., Alotaibi, A. A., Althobaiti, T., Ramzan, N., Alomainy, A., & 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), Article 2237. https://doi.org/10.3390/electronics10182237
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 10, 2021 |
Online Publication Date | Sep 12, 2021 |
Publication Date | 2021 |
Deposit Date | Oct 12, 2021 |
Publicly Available Date | Oct 12, 2021 |
Journal | Electronics |
Publisher | MDPI |
Peer Reviewed | Peer Reviewed |
Volume | 10 |
Issue | 18 |
Article Number | 2237 |
DOI | https://doi.org/10.3390/electronics10182237 |
Keywords | radio-frequency; FMCW RADAR; next generation healthcare; contactless monitoring; fall detection; deep learning; ResNet |
Public URL | http://researchrepository.napier.ac.uk/Output/2811673 |
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Discrete Human Activity Recognition And Fall Detection By Combining FMCW RADAR Data Of Heterogeneous Environments For Independent Assistive Living
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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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