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Article

Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification

1
Department of Computer Science, Edge Hill University, Ormskirk L39 4QP, UK
2
Department of Electrical, Electronic, and Information Engineering Guglielmo Marconi, University of Bologna, 40126 Bologna, Italy
3
Health Sciences and Technologies Interdepartmental Center for Industrial Research, University of Bologna, 40126 Bologna, Italy
4
Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, N-7493 Trondheim, Norway
*
Author to whom correspondence should be addressed.
Academic Editor: Mario Munoz-Organero
Sensors 2021, 21(14), 4669; https://doi.org/10.3390/s21144669
Received: 29 May 2021 / Revised: 29 June 2021 / Accepted: 5 July 2021 / Published: 7 July 2021
Physical activity has a strong influence on mental and physical health and is essential in healthy ageing and wellbeing for the ever-growing elderly population. Wearable sensors can provide a reliable and economical measure of activities of daily living (ADLs) by capturing movements through, e.g., accelerometers and gyroscopes. This study explores the potential of using classical machine learning and deep learning approaches to classify the most common ADLs: walking, sitting, standing, and lying. We validate the results on the ADAPT dataset, the most detailed dataset to date of inertial sensor data, synchronised with high frame-rate video labelled data recorded in a free-living environment from older adults living independently. The findings suggest that both approaches can accurately classify ADLs, showing high potential in profiling ADL patterns of the elderly population in free-living conditions. In particular, both long short-term memory (LSTM) networks and Support Vector Machines combined with ReliefF feature selection performed equally well, achieving around 97% F-score in profiling ADLs. View Full-Text
Keywords: physical activity classification; older adults; classical machine learning; deep learning; free living; wearable sensors physical activity classification; older adults; classical machine learning; deep learning; free living; wearable sensors
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MDPI and ACS Style

Awais, M.; Chiari, L.; Ihlen, E.A.F.; Helbostad, J.L.; Palmerini, L. Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification. Sensors 2021, 21, 4669. https://doi.org/10.3390/s21144669

AMA Style

Awais M, Chiari L, Ihlen EAF, Helbostad JL, Palmerini L. Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification. Sensors. 2021; 21(14):4669. https://doi.org/10.3390/s21144669

Chicago/Turabian Style

Awais, Muhammad, Lorenzo Chiari, Espen A. F. Ihlen, Jorunn L. Helbostad, and Luca Palmerini. 2021. "Classical Machine Learning Versus Deep Learning for the Older Adults Free-Living Activity Classification" Sensors 21, no. 14: 4669. https://doi.org/10.3390/s21144669

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