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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, Kadir, Kushairy, Nordin, Muhammad Noor
Format: Conference or Workshop Item
Language:English
English
Published: IEEE IMS 2022
Subjects:
TK7885 Computer engineering
Online Access:http://irep.iium.edu.my/100342/1/100342_Human%20activity%20and%20posture%20classification.pdf
http://irep.iium.edu.my/100342/2/100342_Human%20activity%20and%20posture%20classification_SCOPUS.pdf
http://irep.iium.edu.my/100342/
https://ieeexplore.ieee.org/document/9806551
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spelling my.iium.irep.1003422022-10-04T07:56:51Z http://irep.iium.edu.my/100342/ Human activity and posture classification using smartphone sensors and Matlab mobile Jamian, Syahirah Gunawan, Teddy Surya Kartiwi, Mira Ahmad, Robiah Kadir, Kushairy Nordin, Muhammad Noor TK7885 Computer engineering 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. IEEE IMS 2022-06-30 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/100342/1/100342_Human%20activity%20and%20posture%20classification.pdf application/pdf en http://irep.iium.edu.my/100342/2/100342_Human%20activity%20and%20posture%20classification_SCOPUS.pdf Jamian, Syahirah and Gunawan, Teddy Surya and Kartiwi, Mira and Ahmad, Robiah and Kadir, Kushairy 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. https://ieeexplore.ieee.org/document/9806551 10.1109/I2MTC48687.2022.9806551
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic TK7885 Computer engineering
spellingShingle TK7885 Computer engineering
Jamian, Syahirah
Gunawan, Teddy Surya
Kartiwi, Mira
Ahmad, Robiah
Kadir, Kushairy
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
Kadir, Kushairy
Nordin, Muhammad Noor
author_facet Jamian, Syahirah
Gunawan, Teddy Surya
Kartiwi, Mira
Ahmad, Robiah
Kadir, Kushairy
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
publisher IEEE IMS
publishDate 2022
url http://irep.iium.edu.my/100342/1/100342_Human%20activity%20and%20posture%20classification.pdf
http://irep.iium.edu.my/100342/2/100342_Human%20activity%20and%20posture%20classification_SCOPUS.pdf
http://irep.iium.edu.my/100342/
https://ieeexplore.ieee.org/document/9806551
_version_ 1746210126537162752
score 13.211869

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