Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network

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 inte...

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Main Author: Mekruksavanich S.
Other Authors: Mahidol University
Format: Conference or Workshop Item
Published: 2023
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Online Access:https://repository.li.mahidol.ac.th/handle/123456789/84340
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spelling th-mahidol.843402023-06-19T00:03:01Z Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network Mekruksavanich S. Mahidol University Computer Science 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. 2023-06-18T17:03:01Z 2023-06-18T17:03:01Z 2022-01-01 Conference Paper Proceedings - 2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022 (2022) , 130-134 10.1109/RI2C56397.2022.9910287 2-s2.0-85141802784 https://repository.li.mahidol.ac.th/handle/123456789/84340 SCOPUS
institution Mahidol University
building Mahidol University Library
continent Asia
country Thailand
Thailand
content_provider Mahidol University Library
collection Mahidol University Institutional Repository
topic Computer Science
spellingShingle Computer Science
Mekruksavanich S.
Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network
description 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.
author2 Mahidol University
author_facet Mahidol University
Mekruksavanich S.
format Conference or Workshop Item
author Mekruksavanich S.
author_sort Mekruksavanich S.
title Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network
title_short Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network
title_full Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network
title_fullStr Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network
title_full_unstemmed Classification of Physical Exercise Activity from ECG, PPG and IMU Sensors using Deep Residual Network
title_sort classification of physical exercise activity from ecg, ppg and imu sensors using deep residual network
publishDate 2023
url https://repository.li.mahidol.ac.th/handle/123456789/84340
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