Research Article
Monitoring Discrete Activities of Daily Living of Young and Older Adults Using 5.8 GHz Frequency Modulated Continuous Wave Radar and ResNet Algorithm
@INPROCEEDINGS{10.1007/978-3-030-95593-9_3, author={Umer Saeed and Fehaid Alqahtani and Fatmah Baothman and Syed Yaseen Shah and Syed Ikram Shah and Syed Salman Badshah and Muhammad Ali Imran and Qammer H. Abbasi and Syed Aziz Shah}, title={Monitoring Discrete Activities of Daily Living of Young and Older Adults Using 5.8 GHz Frequency Modulated Continuous Wave Radar and ResNet Algorithm}, proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health Management. 16th EAI International Conference, BODYNETS 2021, Virtual Event, October 25-26, 2021, Proceedings}, proceedings_a={BODYNETS}, year={2022}, month={2}, keywords={Radar sensor Non-invasive healthcare Human activities identification Deep learning ResNet}, doi={10.1007/978-3-030-95593-9_3} }- Umer Saeed
Fehaid Alqahtani
Fatmah Baothman
Syed Yaseen Shah
Syed Ikram Shah
Syed Salman Badshah
Muhammad Ali Imran
Qammer H. Abbasi
Syed Aziz Shah
Year: 2022
Monitoring Discrete Activities of Daily Living of Young and Older Adults Using 5.8 GHz Frequency Modulated Continuous Wave Radar and ResNet Algorithm
BODYNETS
Springer
DOI: 10.1007/978-3-030-95593-9_3
Abstract
With numerous applications in distinct domains, especially healthcare, human activity detection is of utmost significance. The objective of this study is to monitor activities of daily living using the publicly available dataset recorded in nine different geometrical locations for ninety-nine volunteers including young and older adults (65+) using 5.8 GHz Frequency Modulated Continuous Wave (FMCW) radar. In this work, we experimented with discrete human activities, for instance, walking, sitting, standing, bending, and drinking, recorded for 10 s and 5 s. To detect the list of activities mentioned above, we obtained the Micro-Doppler signatures through Short-time Fourier transform using MATLAB tool and procured the spectrograms as images. The acquired data of the spectrograms are trained, validated, and tested exploiting a state-of-the-art deep learning approach known as Residual Neural Network (ResNet). Moreover, the confusion matrix, model loss, and classification accuracy are used as performance evaluation metrics for the trained ResNet model. The unique skip connection technique of ResNet minimises the overfitting and underfitting issue, consequently resulting accuracy rate up to(91\%).
