Integrating wearable technology and machine learning for early detection and intervention in age-related mobility decline
| dc.contributor.author | Tedesco, Salvatore | en |
| dc.contributor.author | O'Flynn, Brendan | en |
| dc.contributor.funder | European Cooperation in Science and Technology | en |
| dc.contributor.funder | European Commission | en |
| dc.date.accessioned | 2025-07-03T11:57:13Z | |
| dc.date.available | 2025-07-03T11:57:13Z | |
| dc.date.issued | 2025 | en |
| dc.description.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. | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Accepted Version | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.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 . | en |
| dc.identifier.uri | https://hdl.handle.net/10468/17666 | |
| dc.language.iso | en | en |
| dc.publisher | EGRAPA | en |
| dc.relation.uri | https://www.egrapa.org/ | en |
| dc.rights | © 2025, the Authors. | en |
| dc.subject | Wearable technology | en |
| dc.subject | Mobility assessment | en |
| dc.subject | Frailty detection | en |
| dc.subject | Neuromuscular control analysis | en |
| dc.title | Integrating wearable technology and machine learning for early detection and intervention in age-related mobility decline | en |
| dc.type | Conference item | en |
