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Physical Activity Classification for Elderly People in Free-Living Conditions

Awais, Muhammad; Chiari, Lorenzo; Ihlen, Espen Alexander F.; Helbostad, Jorunn L.; Palmerini, Luca

Authors

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

Lorenzo Chiari

Espen Alexander F. Ihlen

Jorunn L. Helbostad

Luca Palmerini



Abstract

Physical activity is strongly linked with mental and physical health in the elderly population and accurate monitoring of activities of daily living (ADLs) can help improve quality of life and well-being. This study presents and validates an inertial sensors-based physical activity classification system developed with older adults as the target population. The dataset was collected in free-living conditions without placing constraints on the way and order of performing ADLs. Four sensor locations (chest, lower back, wrist, and thigh) were explored to obtain the optimal number and combination of sensors by finding the best tradeoff between the system's performance and wearability. Several feature selection techniques were implemented on the feature set obtained from acceleration and angular velocity signals to classify four major ADLs (sitting, standing, walking, and lying). A support vector machine was used for the classification of the ADLs. The findings show the potential of different solutions (single sensor or multisensor) to correctly classify the ADLs of older people in free-living conditions. Considering a minimal set-up of a single sensor, the sensor worn at the L5 achieved the best performance. A two-sensor solution (L5 + thigh) achieved a better performance with respect to a single-sensor solution. By contrast, considering more than two sensors did not provide further improvements. Finally, we evaluated the computational cost of different solutions and it was shown that a feature selection step can reduce the computational cost of the system and increase the system performance in most cases. This can be helpful for real-time applications.

Journal Article Type Article
Publication Date 2019-01
Journal IEEE Journal of Biomedical and Health Informatics
Print ISSN 2168-2194
Electronic ISSN 2168-2208
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 23
Issue 1
Pages 197-207
APA6 Citation Awais, M., Chiari, L., Ihlen, E. A. F., Helbostad, J. L., & Palmerini, L. (2019). Physical Activity Classification for Elderly People in Free-Living Conditions. IEEE Journal of Biomedical and Health Informatics, 23(1), 197-207. https://doi.org/10.1109/jbhi.2018.2820179
DOI https://doi.org/10.1109/jbhi.2018.2820179
Keywords Biotechnology; Electrical and Electronic Engineering; Health Information Management; Computer Science Applications
Publisher URL https://ieeexplore.ieee.org/document/8327491
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