TY - JOUR
T1 - Human activity recognition based on wrist PPG via the ensemble method
AU - Almanifi, Omair Rashed Abdulwareth
AU - Mohd Khairuddin, Ismail
AU - Mohd Razman, Mohd Azraai
AU - Musa, Rabiu Muazu
AU - P.P. Abdul Majeed, Anwar
N1 - Funding Information:
The authors would like to give a special thanks to the providers of the dataset; Wrist PPG During Exercise [7].
Publisher Copyright:
© 2022 The Author(s)
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Classification
KW - ECG
KW - Ensemble
KW - Exercise
KW - HAR
KW - Machine learning
KW - PPG
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85127321203&partnerID=8YFLogxK
U2 - 10.1016/j.icte.2022.03.006
DO - 10.1016/j.icte.2022.03.006
M3 - Article
AN - SCOPUS:85127321203
SN - 2405-9595
VL - 8
SP - 513
EP - 517
JO - ICT Express
JF - ICT Express
IS - 4
ER -