Discrete Human Activity Recognition and Fall Detection by Combining FMCW RADAR Data of Heterogeneous Environments for Independent Assistive Living

Electronics

Abdullah Alhumaidi Alotaibi¹  Turke Althobaiti²  Qammer H. Abbasi³  Syed Aziz Shah⁴  Umer Saeed⁴  Jawad Ahmad⁵  Syed Yaseen Shah⁶  Naeem Ramzan⁷  Akram Alomainy⁸ 

[1] Department of Science and Technology, College of Ranyah, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia;James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK;Research Centre for Intelligent Healthcare, Coventry University, Coventry CV1 5FB, UK;School of Computing, Edinburgh Napier University, Edinburgh EH10 5DT, UK;School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK;School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisely PA1 2BE, UK;School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS, UK;

关键词: radio-frequency;    FMCW RADAR;    next generation healthcare;    contactless monitoring;    fall detection;    deep learning;   

DOI  :  10.3390/electronics10182237

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown