On Intuitive Control of Ankle-Foot Prostheses: A Sensor Fusion-Based Algorithm for Real-Time Prediction of Transitions to Compliant Surfaces

Substantial research and development on the design and control of robotic ankle-foot prostheses have aimed to restore normal function and movement capacity for people with gait impairments and lower limb amputations. However, prostheses controllers usually fail to incorporate information pertaining to the properties of the walking terrain, such as ground stiffness. There is therefore a need for a framework that adjusts the prostheses parameters according to the user's intent to transition to a variable impedance terrain. To achieve this, we need to incorporate the human wearer in the control loop of the prosthesis. This work proposes an advanced, high-level controller framework for powered ankle-foot prostheses that combines subject-specific pattern recognition (PR) and classification strategies to predict whether the next step will be on a rigid or compliant surface. Comparing the Support Vector Machine (SVM) and k-Nearest Neighbors (k-NN) classification algorithms for this task, we conclude that by combining a k-NN implementation with a Pattern Recognition Neural Network (PR NN), our method can accurately forecast upcoming surface stiffness transitions in time to allow for prompt adaptation to the new walking terrain. We also show that the sensor fusion of kinematic and surface electromyographic (EMG) data outperforms single-source inputs producing the best prediction results for all subjects with an accuracy of up to 87.5%.