Physical activity monitoring and classification using machine learning techniques

Alsareii, Saeed Ali, Awais, Muhammad ORCID: https://orcid.org/0000-0001-6421-9245, Alamri, Abdulrahman Manaa, Alasmari, Mansour, Irfan, Muhammad, Aslam, Nauman and Raza, Mohsin (2022) Physical activity monitoring and classification using machine learning techniques. Life, 12 (8). ISSN 2075-1729

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Abstract

Physical activity plays an important role in controlling obesity and maintaining healthy living. It becomes increasingly important during a pandemic due to restrictions on outdoor activities. Tracking physical activities using miniature wearable sensors and state-of-the-art machine learning techniques can encourage healthy living and control obesity. This work focuses on introducing novel techniques to identify and log physical activities using machine learning techniques and wearable sensors. Physical activities performed in daily life are often unstructured and unplanned, and one activity or set of activities (sitting, standing) might be more frequent than others (walking, stairs up, stairs down). None of the existing activities classification systems have explored the impact of such class imbalance on the performance of machine learning classifiers. Therefore, the main aim of the study is to investigate the impact of class imbalance on the performance of machine learning classifiers and also to observe which classifier or set of classifiers is more sensitive to class imbalance than others. The study utilizes motion sensors’ data of 30 participants, recorded while performing a variety of daily life activities. Different training splits are used to introduce class imbalance which reveals the performance of the selected state-of-the-art algorithms with various degrees of imbalance. The findings suggest that the class imbalance plays a significant role in the performance of the system, and the underrepresentation of physical activity during the training stage significantly impacts the performance of machine learning classifiers.

Item Type:	Article
Additional Information:	Funding information: The authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education, Kingdom of Saudi Arabia, for this research through a grant (NU/IFC/ENT/01/020) under the institutional Funding Committee at Najran University, Kingdom of Saudi Arabia.
Uncontrolled Keywords:	sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI
Related URLs:	http://dx.doi.org/10.3390/life12081103
Depositing User:	LivePure Connector
Date Deposited:	17 Oct 2023 00:44
Last Modified:	10 Dec 2024 01:42
URI:	https://ueaeprints.uea.ac.uk/id/eprint/93298
DOI:	10.3390/life12081103

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