ASTRA-BF: A human-in-the-loop algorithm for predicting surface transitions in robotic lower limb prosthetics using biomechanical features

In the realm of lower-limb prosthetics and wearable devices, the significance of walking on compliant surfaces for individuals with lower-limb amputations (LLA) cannot be overstated. The interaction between humans and compliant surfaces presents a unique challenge, particularly for those relying on prosthetic interventions. Ensuring the safety, stability, and fluidity of movement on these surfaces is paramount for preventing falls in this population. This work delves into the critical importance of addressing this challenge, outlining the complexities involved in walking on compliant surfaces, and exploring high-level control strategy interventions aimed at mitigating the inherent difficulties faced by individuals with LLA. We specifically expand on an individualized pattern recognition (PR) and classification approach utilizing kinematic, kinetic, and surface electromyography (EMG) data to discern user intent for transitioning from rigid to compliant surfaces of variable stiffness. This work proposes an efficient Algorithm for Surface TRAnsition prediction in robotic lower limb prosthetics using Biomechanical Features, called ASTRA-BF. Integrating a k-Nearest Neighbors (kNN) methodology alongside a Naive Bayes classification, our strategy can accurately forecast impending transitions in four different levels of surface stiffness in real time. This capability enables swift parameter control of prostheses or wearable devices, facilitating adaptation to diverse terrains. Post-implementation of the ASTRA-BF, classification results attain a prediction accuracy of up to 82%, demonstrating the feasibility and efficiency of real-time prediction for transitions to various compliant surfaces in both healthy and clinical populations. The proposed framework has the potential to propel the field of robotics toward novel solutions that not only enhance mobility but also significantly improve the quality of life for those navigating the intricate terrain of compliant surfaces with prosthetic limbs.