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Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living
Physical activity is essential for physical and mental health, and its absence is highly associated with severe health conditions and disorders. Therefore, tracking activities of daily living can help promote quality of life. Wearable sensors in this regard can provide a reliable and economical mean...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038216/ https://www.ncbi.nlm.nih.gov/pubmed/32046302 http://dx.doi.org/10.3390/ijerph17031082 |
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author | Rahman, Saifur Irfan, Muhammad Raza, Mohsin Moyeezullah Ghori, Khawaja Yaqoob, Shumayla Awais, Muhammad |
author_facet | Rahman, Saifur Irfan, Muhammad Raza, Mohsin Moyeezullah Ghori, Khawaja Yaqoob, Shumayla Awais, Muhammad |
author_sort | Rahman, Saifur |
collection | PubMed |
description | Physical activity is essential for physical and mental health, and its absence is highly associated with severe health conditions and disorders. Therefore, tracking activities of daily living can help promote quality of life. Wearable sensors in this regard can provide a reliable and economical means of tracking such activities, and such sensors are readily available in smartphones and watches. This study is the first of its kind to develop a wearable sensor-based physical activity classification system using a special class of supervised machine learning approaches called boosting algorithms. The study presents the performance analysis of several boosting algorithms (extreme gradient boosting—XGB, light gradient boosting machine—LGBM, gradient boosting—GB, cat boosting—CB and AdaBoost) in a fair and unbiased performance way using uniform dataset, feature set, feature selection method, performance metric and cross-validation techniques. The study utilizes the Smartphone-based dataset of thirty individuals. The results showed that the proposed method could accurately classify the activities of daily living with very high performance (above 90%). These findings suggest the strength of the proposed system in classifying activity of daily living using only the smartphone sensor’s data and can assist in reducing the physical inactivity patterns to promote a healthier lifestyle and wellbeing. |
format | Online Article Text |
id | pubmed-7038216 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70382162020-03-09 Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living Rahman, Saifur Irfan, Muhammad Raza, Mohsin Moyeezullah Ghori, Khawaja Yaqoob, Shumayla Awais, Muhammad Int J Environ Res Public Health Article Physical activity is essential for physical and mental health, and its absence is highly associated with severe health conditions and disorders. Therefore, tracking activities of daily living can help promote quality of life. Wearable sensors in this regard can provide a reliable and economical means of tracking such activities, and such sensors are readily available in smartphones and watches. This study is the first of its kind to develop a wearable sensor-based physical activity classification system using a special class of supervised machine learning approaches called boosting algorithms. The study presents the performance analysis of several boosting algorithms (extreme gradient boosting—XGB, light gradient boosting machine—LGBM, gradient boosting—GB, cat boosting—CB and AdaBoost) in a fair and unbiased performance way using uniform dataset, feature set, feature selection method, performance metric and cross-validation techniques. The study utilizes the Smartphone-based dataset of thirty individuals. The results showed that the proposed method could accurately classify the activities of daily living with very high performance (above 90%). These findings suggest the strength of the proposed system in classifying activity of daily living using only the smartphone sensor’s data and can assist in reducing the physical inactivity patterns to promote a healthier lifestyle and wellbeing. MDPI 2020-02-08 2020-02 /pmc/articles/PMC7038216/ /pubmed/32046302 http://dx.doi.org/10.3390/ijerph17031082 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Rahman, Saifur Irfan, Muhammad Raza, Mohsin Moyeezullah Ghori, Khawaja Yaqoob, Shumayla Awais, Muhammad Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living |
title | Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living |
title_full | Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living |
title_fullStr | Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living |
title_full_unstemmed | Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living |
title_short | Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living |
title_sort | performance analysis of boosting classifiers in recognizing activities of daily living |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038216/ https://www.ncbi.nlm.nih.gov/pubmed/32046302 http://dx.doi.org/10.3390/ijerph17031082 |
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