Integrating Wearable Technology and Machine Learning for Early Detection and Intervention in Age-Related Mobility Decline

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.