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Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living

Rahman, Saifur; Irfan, Muhammad; Raza, Mohsin; Moyeezullah Ghori, Khawaja; Yaqoob, Shumayla; Awais, Muhammad

Authors

Saifur Rahman

Muhammad Irfan

Mohsin Raza

Khawaja Moyeezullah Ghori

Shumayla Yaqoob

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Dr Muhammad Awais M.Awais@hull.ac.uk
Post Doctoral Research Fellow in Data Analytics and AI



Abstract

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.

Journal Article Type Article
Publication Date Feb 1, 2020
Journal International Journal of Environmental Research and Public Health
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 17
Issue 3
Article Number 1082
APA6 Citation Rahman, S., Irfan, M., Raza, M., Moyeezullah Ghori, K., Yaqoob, S., & Awais, M. (2020). Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living. International Journal of Environmental Research and Public Health, 17(3), https://doi.org/10.3390/ijerph17031082
DOI https://doi.org/10.3390/ijerph17031082
Keywords Activities of daily living; Boosting classifiers; Machine learning; Performance; Physical activity classification
Publisher URL https://www.mdpi.com/1660-4601/17/3/1082

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Copyright Statement
© 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/).





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