Human activity recognition based on wrist PPG via the ensemble method

Almanifi, Omair Rashed Abdulwareth and Ismail, Mohd Khairuddin and Mohd Azraai, Mohd Razman and Musa, Rabiu Muazu and Anwar, P. P. Abdul Majeed (2022) Human activity recognition based on wrist PPG via the ensemble method. ICT Express. pp. 1-5. ISSN 2405-9595. (In Press / Online First) (In Press / Online First)

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DOI/Official URL: https://doi.org/10.1016/j.icte.2022.03.006

Abstract

Human activity recognition via Electrocardiography (ECG) and Photoplethysmography (PPG) is extensively researched. While ECG requires less filtering and is less prone to disturbance and artifacts, nonetheless, PPG is cheaper and widely available in smart devices, making it a desired alternative. In this study, we explore the employment of the ensemble method with several pre-trained machine learning models namely Resnet50V2, MobileNetV2, and Xception for the classification of wrist PPG data of human activity, in comparison to its ECG counterpart. The study produced promising results with a test classification accuracy of 88.91% and 94.28% for PPG and ECG, respectively.

Item Type:	Article
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Classification; ECG; Ensemble; Exercise; HAR; Machine learning; PPG; Transfer learning
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TS Manufactures
Faculty/Division:	Institute of Postgraduate Studies Faculty of Manufacturing and Mechatronic Engineering Technology
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	27 Oct 2022 00:55
Last Modified:	27 Oct 2022 00:55
URI:	http://umpir.ump.edu.my/id/eprint/35111
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