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Human activity and posture classification using smartphone sensors and matlab mobile

Human Activity Recognition (HAR) is significant, especially in the medical field. Activity recognition has been used in various ways as technology has advanced, particularly using a smartphone-based approach. This work aims to evaluate the accuracy of the triaxial accelerometer in the Matlab Mobile...

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Main Authors:	Jamian, Syahirah, Gunawan, Teddy Surya, Kartiwi, Mira, Ahmad, Robiah, Abdul Kadir, Kushsairy, Nordin, Muhammad Noor
Format:	Conference or Workshop Item
Published:	2022
Subjects:	T Technology (General)
Online Access:	http://eprints.utm.my/id/eprint/98854/ http://dx.doi.org/10.1109/I2MTC48687.2022.9806551
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id	my.utm.98854
record_format	eprints
spelling	my.utm.988542023-02-02T09:44:27Z http://eprints.utm.my/id/eprint/98854/ Human activity and posture classification using smartphone sensors and matlab mobile Jamian, Syahirah Gunawan, Teddy Surya Kartiwi, Mira Ahmad, Robiah Abdul Kadir, Kushsairy Nordin, Muhammad Noor T Technology (General) Human Activity Recognition (HAR) is significant, especially in the medical field. Activity recognition has been used in various ways as technology has advanced, particularly using a smartphone-based approach. This work aims to evaluate the accuracy of the triaxial accelerometer in the Matlab Mobile and examine the development and performance of the algorithms in identifying human motions on individuals of similar ages and physical appearances. Motion signals from three subjects are measured, data is preprocessed using a filtering technique, features are extracted, feature normalization is used to reduce bias in data measurement, and activities are classified. Confusion matrix, precision, recall, accuracy, F1-score, and Kappa score are performance indicators used to determine this classification approach. As a result, this research discovered that the Quadratic Support Vector Machine (SVM) produces the best results, with a 99.22 % accuracy rate, proving the efficacy of its activity identification method. 2022 Conference or Workshop Item PeerReviewed Jamian, Syahirah and Gunawan, Teddy Surya and Kartiwi, Mira and Ahmad, Robiah and Abdul Kadir, Kushsairy and Nordin, Muhammad Noor (2022) Human activity and posture classification using smartphone sensors and matlab mobile. In: 2022 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2022, 16 - 19 May 2022, Ottawa, Canada. http://dx.doi.org/10.1109/I2MTC48687.2022.9806551
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	T Technology (General)
spellingShingle	T Technology (General) Jamian, Syahirah Gunawan, Teddy Surya Kartiwi, Mira Ahmad, Robiah Abdul Kadir, Kushsairy Nordin, Muhammad Noor Human activity and posture classification using smartphone sensors and matlab mobile
description	Human Activity Recognition (HAR) is significant, especially in the medical field. Activity recognition has been used in various ways as technology has advanced, particularly using a smartphone-based approach. This work aims to evaluate the accuracy of the triaxial accelerometer in the Matlab Mobile and examine the development and performance of the algorithms in identifying human motions on individuals of similar ages and physical appearances. Motion signals from three subjects are measured, data is preprocessed using a filtering technique, features are extracted, feature normalization is used to reduce bias in data measurement, and activities are classified. Confusion matrix, precision, recall, accuracy, F1-score, and Kappa score are performance indicators used to determine this classification approach. As a result, this research discovered that the Quadratic Support Vector Machine (SVM) produces the best results, with a 99.22 % accuracy rate, proving the efficacy of its activity identification method.
format	Conference or Workshop Item
author	Jamian, Syahirah Gunawan, Teddy Surya Kartiwi, Mira Ahmad, Robiah Abdul Kadir, Kushsairy Nordin, Muhammad Noor
author_facet	Jamian, Syahirah Gunawan, Teddy Surya Kartiwi, Mira Ahmad, Robiah Abdul Kadir, Kushsairy Nordin, Muhammad Noor
author_sort	Jamian, Syahirah
title	Human activity and posture classification using smartphone sensors and matlab mobile
title_short	Human activity and posture classification using smartphone sensors and matlab mobile
title_full	Human activity and posture classification using smartphone sensors and matlab mobile
title_fullStr	Human activity and posture classification using smartphone sensors and matlab mobile
title_full_unstemmed	Human activity and posture classification using smartphone sensors and matlab mobile
title_sort	human activity and posture classification using smartphone sensors and matlab mobile
publishDate	2022
url	http://eprints.utm.my/id/eprint/98854/ http://dx.doi.org/10.1109/I2MTC48687.2022.9806551
_version_	1758578028713082880
score	13.211869

Human activity and posture classification using smartphone sensors and matlab mobile

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