On Predicting Transitions to Compliant Surfaces in Adults with Transtibial Amputation: A Real-Time Classification Approach.

Abstract

Walking on compliant surfaces, such as carpets, grass, and soil, presents a unique challenge, particularly for those relying on prosthetic interventions. Ensuring the safety, stability, and fluidity of movement on these surfaces is paramount to prevent falls and related balance issues in this population. This study presents the first attempt to classify and predict surface compliance in individuals with transtibial lower-limb amputations. By integrating electromyographic (EMG), kinematic, and kinetic data, our system effectively distinguishes user intent across varying surface stiffnesses representing diverse real-world terrains. As we demonstrate the algorithm's success within a clinical population, we achieve up to 83% prediction accuracy, attaining comparable results as in previously tested healthy populations. The suggested framework is a critical component for high-level controllers for advanced prostheses and it holds potential for real-time integration, enabling adaptive adjustments to the prosthetic device in response to both user intent and environmental stimuli.