Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network
Issued Date
2022-01-01
Resource Type
Scopus ID
2-s2.0-85141802784
Journal Title
Proceedings - 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022
Start Page
130
End Page
134
Rights Holder(s)
SCOPUS
Bibliographic Citation
Proceedings - 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022 (2022) , 130-134
Suggested Citation
Mekruksavanich S., Jantawong P., Hnoohom N., Jitpattanakul A. Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network. Proceedings - 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022 (2022) , 130-134. 134. doi:10.1109/RI2C56397.2022.9910287 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/84340
Title
Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network
Author's Affiliation
Other Contributor(s)
Abstract
Human activity recognition (HAR) plays an increasingly vital role in several industrial applications, including medical services and rehabilitation surveillance. With the fast growth of information and communications technology, wearable technologies have recently triggered a new human-computer interaction. Wearable inertial sensors (IMUs) are commonly used in the area of HAR because this data source provides the most insightful motion signal data. Lately, HAR studies have examined the enhancement of activity recognition using bio-signals like Electrocardiogram (ECG) and Photoplethysmography (PPG). Nevertheless, current HAR research was constrained by machine learning techniques that relied on human-crafted feature extraction. This research proposed a deep learning technique to effectively identify physical activity behaviors using ECG, PPG, and IMU sensor data. ResNet-SE is a deep residual network that incorporates convolutional processes, shortcut connections, and squeeze-and-excitement. We trained and evaluated baseline deep learning models to assess the suggested network, including the proposed model, using the public HAR dataset called Wrist_PPG dataset. According to experimental findings, the suggested method earned the most fantastic accuracy of F1-score. In addition, our results indicate that the PPG data can be utilized to classify physical workouts.