Integrating wearable technology and machine learning for early detection and intervention in age-related mobility decline

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Date
2025
Authors
Tedesco, Salvatore
O'Flynn, Brendan
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Publisher
EGRAPA
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Abstract
Aging is associated with declines in neuromuscular control, mobility, and overall physical function, increasing the risk of frailty and reduced quality of life. Early detection and targeted interventions are critical in mitigating these effects. At Tyndall National Institute, we have explored evidence-based approaches to assessing and improving physical activity (PA) in older adults, leveraging wearables, machine learning (ML), and neuromuscular control analysis to optimize clinical interventions.
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Keywords
Wearable technology , Mobility assessment , Frailty detection , Neuromuscular control analysis
Citation
Tedesco, S. and O'Flynn, B. (2025) ‘Integrating wearable technology and machine learning for early detection and intervention in age-related mobility decline’, EGRAPA Conference, Trabzon, Turkey, 7-9 May .
Link to publisher’s version
https://www.egrapa.org/
URI
https://hdl.handle.net/10468/17666
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Tyndall National Institute - Conference Items
Copyright
© 2025, the Authors.