Adaptive biofeedback improves ankle angle in ankle instability patients
Adaptive Gait Biofeedback With Participant-Held-Out Modeling and Participant-Specific Updating in Chronic Ankle Instability
Artificial Intelligence
Summary
People with chronic ankle instability can have trouble walking properly. The authors tested a computer model that looks at ankle movements and gives feedback to help improve walking. They found their model could tell good and bad steps apart well and that personalized updates helped it work better over time. After several sessions, patients who used this adaptive feedback showed better ankle angles than those who did not. However, the authors do not claim this feedback system alone can diagnose or cure ankle problems.
What this means in practice
- •For physical therapists: Use adaptive gait biofeedback systems to provide personalized walking practice for patients with chronic ankle instability, improving ankle movement angles.
- •For rehabilitation technology developers: Develop wearable gait monitoring devices that update models based on user performance to enhance biofeedback accuracy during rehabilitation.$Commercial implications: Enables development of adaptive gait feedback products targeting patient-specific improvements for rehabilitation markets.
Authors
Jaeyoon, Kim, Veronika Lebisova, Jeniya Sultana, Jaeyoung Cho, Jaeho Jang
Abstract
Adaptive gait biofeedback may support repeated practice in chronic ankle instability, but its evaluation must address model performance and human response. We evaluated a temporal convolutional classifier on protocol-defined, angle-derived GOOD/BAD gait-cycle labels using participant-held-out leave-one-subject-out (LOSO) cross-validation in 20 participants. Seven participants in the adaptive-intervention group completed nine sessions over three weeks, with one motion-capture recording analyzed per session. Models updated after failed sessions were compared offline with their parent models on the same-session validation subset used for candidate selection and the first subsequent adaptive-session recording. Frontal-plane ankle angle was compared between the adaptive group and 10 sequentially enrolled controls at Baseline, Post, and 7-day Retention. Across 20 held-out folds, mean fold-level area under the receiver operating characteristic curve (AUROC) was 0.948, sensitivity for angle-threshold-exceeding BAD cycles was 0.941, and specificity for angle-threshold-meeting GOOD cycles was 0.366. Mean BAD-class F1 was higher in candidate models by 0.187 on the same-session subset and 0.118 on the first subsequent recording. At Post, the adaptive group had a baseline-adjusted frontal-plane ankle angle 5.168 degrees lower than controls (95% confidence interval, 1.766-8.569 degrees lower); the Retention contrast was uncertain. These findings characterize population-model discrimination and offline participant-specific updating during repeated biofeedback use, alongside a nonrandomized Post frontal-plane ankle angle association. They do not establish independent clinical gait classification or a causal benefit of updating.