Physical activity classification using body-worn inertial sensors in a multi-sensor setup

Awais, Muhammad ORCID: https://orcid.org/0000-0001-6421-9245, Palmerini, Luca and Chiari, Lorenzo (2016) Physical activity classification using body-worn inertial sensors in a multi-sensor setup. In: 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI). The Institute of Electrical and Electronics Engineers (IEEE). ISBN 9781509011315

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Abstract

Physical inactivity significantly impacts personal health, reduces quality of life, and often leads to mobility disorders, diabetes, and cardiovascular disease. Monitoring daily life activities by means of wearable inertial sensors can provide valuable feedback necessary to improve the quality of daily life and prevent the development of mobility disorders caused by physical inactivity. In this study, a physical activity classification (PAC) algorithm was developed and tested using an inertial sensor-based dataset. The dataset was acquired from multiple inertial sensors, each mounted at a different body location, and consists of various Activities of Daily Living (ADL). Data from nineteen healthy young subjects were analyzed. Time- and frequency-domain features from raw 3D accelerometer and 3D gyroscope signals were computed by performing windowing of the time series data. The K-nearest neighbors (KNN) pattern recognition algorithm was used to classify thirteen different ADLs and was evaluated by a 10-fold cross-validation. The proposed PAC algorithm outperformed the existing algorithm validated using the same dataset, with an overall mean classification rate (sensitivity) of 97.38%. This paper discusses the limitations of this study and proposes ways to overcome said limitations in order to make the PAC algorithm more effective in real-life conditions.

Item Type:	Book Section
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
Depositing User:	LivePure Connector
Date Deposited:	17 Oct 2023 00:49
Last Modified:	10 Dec 2024 01:13
URI:	https://ueaeprints.uea.ac.uk/id/eprint/93336
DOI:	10.1109/rtsi.2016.7740565

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