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Awais, Muhammad 
ORCID: https://orcid.org/0000-0001-6421-9245, Chiari, Lorenzo, Ihlen, Espen A. F., Helbostad, Jorunn L. and Palmerini, Luca
(2021)
Classical machine learning versus deep learning for the older adults free-living activity classification.
Sensors, 21 (14).
ISSN 1424-8220
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Abstract
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.
| Item Type: | Article |
|---|---|
| Additional Information: | Data Availability Statement: The script from the ADAPT validation data set used in this study is available on request to Jorunn L. Helbostad. Funding information: This study was partially funded by the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 820820 (Mobilise-D). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This study was also partially funded by the Norwegian Research Council (FRIMEDBIO, Contract No 230435). |
| Uncontrolled Keywords: | classical machine learning,deep learning,free living,older adults,physical activity classification,wearable sensors,analytical chemistry,information systems,atomic and molecular physics, and optics,biochemistry,instrumentation,electrical and electronic engineering ,/dk/atira/pure/subjectarea/asjc/1600/1602 |
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| 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/93303 |
| DOI: | 10.3390/s21144669 |
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