From Assessment to Engagement: Inertial Gait Analysis and Remote Exercise for Fall-Risk Prevention

Introduction

Ageing leads to subtle mobility changes that increase fall risk if not identified early. Most existing solutions intervene only after functional decline becomes evident. This work presents an integrated preventive approach combining clinically accurate gait analysis with remotely supervised exercise programmes, aiming to detect early deterioration and promote safer, more autonomous ageing.

Methods

The system includes: (1) a shoe-mounted inertial sensor (Moverics) enabling rapid gait assessment and extraction of validated fall- and frailty-related metrics; and (2) ViviFil, a mobile app delivering daily exercise adapted to the participant’s functional level, with remote monitoring.

Two clinical studies were conducted. First, gait data from 157 individuals (fallers and non-fallers) from the GSTRIDE database were analysed to evaluate the sensor’s discriminative capacity. Second, the GAIT2CARE dataset included 93 older adults (82±6 years) completing two 8-week multicomponent exercise programmes (on-site and app-guided via ViviFil).

Results

Using inertial gait parameters alone, the sensor achieved 78.2% accuracy classifying fallers versus non-fallers (sensitivity 77%, specificity 79%), outperforming conventional clinical tests (gait speed: 76.4%, TUG: 75.6%). Among participants with remarkable adherence (>70%, n=64), the app-guided programme showed functional improvements of 10.2% and gait improvements of 9.0%, with no significant differences compared to on-site training (p>0.05). However, adherence rates differed substantially (app-guided: 45% vs on-site: 96%), highlighting engagement as a critical factor.

Conclusions

This work demonstrates that combining precise inertial gait evaluation with remote structured exercise is a feasible and effective strategy for fall-risk prevention. The sensor’s discriminative performance, using gait parameters alone, matches conventional clinical assessments while offering rapid, objective measurement. The therapeutic equivalence of ViviFil-guided training is confirmed when adherence is maintained. Future work will explore deep learning approaches to further improve classification accuracy, alongside affective feedback strategies to strengthen sustained engagement.