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EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm

In Human activity recognition (HAR) research, it is a common practice to use wearable sensors to acquire the signals for human daily activities. In this study, an experimental data from electromyography (EMG) wireless sensors is analysed for six different activities recognition. This paper aims to c...

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Main Authors: Nurhanim, K., Elamvazuthi, I., Izhar, L.I., Capi, G., Su, S.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Online Access:http://scholars.utp.edu.my/id/eprint/33451/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127011513&doi=10.1109%2fNICS54270.2021.9701461&partnerID=40&md5=b1b92908d84d8a27f1e9b6112940fadd
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spelling oai:scholars.utp.edu.my:334512022-12-28T08:22:02Z http://scholars.utp.edu.my/id/eprint/33451/ EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm Nurhanim, K. Elamvazuthi, I. Izhar, L.I. Capi, G. Su, S. In Human activity recognition (HAR) research, it is a common practice to use wearable sensors to acquire the signals for human daily activities. In this study, an experimental data from electromyography (EMG) wireless sensors is analysed for six different activities recognition. This paper aims to compare EMG signals of left and right of upper leg muscles by using Random Forest (RF) Machine Learning Classifier. The HAR processing comprises of data filtering and segmentation, data feature extraction, feature selection of the data, and classification. Model evaluation of holdout method is implemented for classification assessment. The performance of all human daily activities is evaluated according to the comparison of precision and recall for each activity. The results show combined muscles obtained the highest precision and recall on running activity with 89.2 and 88.3. The highest overall accuracy of classification was 82.08 on the bicep femoris left and right (BF-Left Right). © 2021 IEEE. Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item NonPeerReviewed Nurhanim, K. and Elamvazuthi, I. and Izhar, L.I. and Capi, G. and Su, S. (2021) EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm. In: UNSPECIFIED. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127011513&doi=10.1109%2fNICS54270.2021.9701461&partnerID=40&md5=b1b92908d84d8a27f1e9b6112940fadd
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description In Human activity recognition (HAR) research, it is a common practice to use wearable sensors to acquire the signals for human daily activities. In this study, an experimental data from electromyography (EMG) wireless sensors is analysed for six different activities recognition. This paper aims to compare EMG signals of left and right of upper leg muscles by using Random Forest (RF) Machine Learning Classifier. The HAR processing comprises of data filtering and segmentation, data feature extraction, feature selection of the data, and classification. Model evaluation of holdout method is implemented for classification assessment. The performance of all human daily activities is evaluated according to the comparison of precision and recall for each activity. The results show combined muscles obtained the highest precision and recall on running activity with 89.2 and 88.3. The highest overall accuracy of classification was 82.08 on the bicep femoris left and right (BF-Left Right). © 2021 IEEE.
format Conference or Workshop Item
author Nurhanim, K.
Elamvazuthi, I.
Izhar, L.I.
Capi, G.
Su, S.
spellingShingle Nurhanim, K.
Elamvazuthi, I.
Izhar, L.I.
Capi, G.
Su, S.
EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm
author_facet Nurhanim, K.
Elamvazuthi, I.
Izhar, L.I.
Capi, G.
Su, S.
author_sort Nurhanim, K.
title EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm
title_short EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm
title_full EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm
title_fullStr EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm
title_full_unstemmed EMG Signals Classification on Human Activity Recognition using Machine Learning Algorithm
title_sort emg signals classification on human activity recognition using machine learning algorithm
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2021
url http://scholars.utp.edu.my/id/eprint/33451/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85127011513&doi=10.1109%2fNICS54270.2021.9701461&partnerID=40&md5=b1b92908d84d8a27f1e9b6112940fadd
_version_ 1753790779357134848
score 13.160551

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