Closed-Loop Future Prediction of Continuous Ankle Kinematics and Kinetics Using Residual Muscle Signals of Transtibial Amputees

Abstract Background Despite performance improvements in active lower limb prostheses, there remains a need for control techniques that incorporate direct user intent (e.g., myoelectric control) to limit the physical and cognitive demands and provide continuous, natural gait across terrains. Methods The ability of a nonlinear autoregressive neural network with exogenous inputs (NARX) to continuously predict future (up to 142 ms ahead of time) ankle angle and moment of three transtibial amputees was examined across ambulation conditions (level overground walking, stair ascent, and stair descent) and terrain transitions. Within-socket residual EMG of the prosthetic side, in conjunction with sound-limb shank velocity, were used as inputs to the single-network NARX model to predict sound-limb ankle dynamics. By overlaying the ankle dynamics of the sound limb onto the prosthesis, the approach is a step forward to establish a more normal gait by creating symmetric gait patterns. The NARX model was trained and tested as a closed-loop network (model predictions fed back as recurrent inputs, rather than error-free targets) to ensure accuracy and stability when implemented in a feedback control system. Results Ankle angle and moment predictions of amputee models were accurate across ambulation conditions and terrain transitions with root-mean-square errors (RMSE) less than 3.7 degrees and 0.22 Nm/kg, respectively, and cross-correlations (R2) greater than 0.89 and 0.93, respectively, for predictions 58 ms ahead of time. The closed-loop NARX model had similar performance when characterizing normal ranges of ankle dynamics across able-bodied participants (n = 6; RMSEθ < 2.7°, R2θ > 0.95, RMSEM < 0.11 Nm/kg, R2M > 0.98 for predictions 58 ms ahead of time). Model performance was stable across a range of different EMG profiles, leveraging both EMG and shank velocity inputs for the prediction of ankle dynamics across ambulation conditions. Conclusions The use of natural, yet altered in amputees, muscle activity with information about limb state, coupled with the closed-loop predictive design, could provide intuitive user-driven and robust control by counteracting delays and proactively modifying gait in response to observed changes in terrain. The model takes an important step toward continuous real-time feedback control of active ankle-foot prostheses and robotic devices.