A Task-Agnostic Approach to Unified Multi-Activity Gait Phase Estimation via Bilateral Sensing

Abstract

Estimating the gait phase is a key aspect for controlling many lower-limb rehabilitation robots, including transfemoral prostheses. Current control approaches often rely on high-level activity classification to then employ a taskspecific phase algorithm, which can limit adaptability across tasks and introduce risks associated with misclassification. This study proposes a novel unified phase variable framework with two approaches, one using activity classification and one being entirely task-agnostic. The framework uses predicted gait event information to continuously define a unified phase variable across level walking, ramp ascent/descent, and stair ascent/descent at various inclines and speeds. The classification approach senses the unilateral thigh angle, whereas the taskagnostic approach expands sensing to include the contralateral thigh angle. Simulated evaluations using an able-bodied dataset demonstrate average phase root-mean-square error of 6.8% with classification and 6.3% in the task-agnostic mode. The bilateral task-agnostic approach notably performed the same or better than the unilateral classification-based approach, showing improved consistency across subjects and tasks, particularly during stair ascent. These results highlight the feasibility of task-agnostic gait phase estimation for prosthesis control, demonstrating performance comparable to task-specific models while removing reliance on activity classification.