Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotion

TL;DR

This paper introduces a wearable-informed generative framework that predicts task-specific post-stroke locomotion, enabling personalized rehabilitation strategies by reconstructing patient-specific movements in new environments.

Contribution

It presents a novel hybrid data-physics approach that personalizes digital avatars from wearable sensors to predict post-stroke gait in different scenarios.

Findings

Predicted postures achieved over 80% similarity for slopes and stairs.

Patients using scenario-specific predictions showed greater improvements in motor scores.

The framework outperformed physics-only baselines in accuracy.

Abstract

Dynamic prediction of locomotor capacity after stroke could enable more individualized rehabilitation, yet current assessments largely provide static impairment scores and do not indicate whether patients can perform specific tasks such as slope walking or stair climbing. Here, we present a wearable-informed data-physics hybrid generative framework that reconstructs a stroke survivor's locomotor control from wearable inertial sensing and predicts task-conditioned post-stroke locomotion in new environments. From a single 20 m level-ground walking trial recorded by five IMUs, the framework personalizes a physics-based digital avatar using a healthy-motion prior and hybrid imitation learning, generating dynamically feasible, patient-specific movements for inclined walking and stair negotiation. Across 11 stroke inpatients, predicted postures reached 82.2% similarity for slopes and 69.9% for stairs, substantially exceeding a physics-only baseline. In a multicentre pilot randomized study (n = 21; 28 days), access to scenario-specific locomotion predictions to support task selection and difficulty titration was associated with larger gains in Fugl-Meyer lower-extremity scores than standard care (mean change 6.0 vs 3.7 points; $p < 0.05$). These results suggest that wearable-informed generative digital avatars may augment individualized gait rehabilitation planning and provide a pathway toward dynamically personalized post-stroke motor recovery strategies.