Prediction of EMG Activation Profiles from Gait Kinematics and Kinetics during Multiple Terrains.

Continuous myoelectric prediction of intended limb dynamics has the ability to provide transparent control of a prosthesis by the user. However, the impact on these models of adding a human user into the control loop is less clear. Here, the ability of a User Response Model (URM) to continuously predict EMG activity from gait kinematics and kinetics collected during three mobility tasks (level-ground walking, stair ascent, and stair descent) was examined. Multiple-input, multiple-output NARX-based URMs were developed with two outputs (ankle plantarflexor and dorsiflexor) and variable inputs (ankle kinetics, and shank and/or ankle kinematics). Accuracy in predicting the tibialis anterior and medial gastrocnemius EMG was comparable across URMs regardless of the number of inputs. Stair descent had the lowest accuracy among the mobility tasks. No significant differences in normalized root-mean-square error and cross-correlation were found between URMs with five and nine inputs. A URM that continuously predicts EMG activity from gait kinetics and kinematics could be used to simulate human-in-the-loop myoelectric control of a transtibial prosthesis and examine the stability of the system to changes in the environment or due to control errors.

Continuous Myoelectric Prediction of Future Ankle Angle and Moment Across Ambulation Conditions and Their Transitions.

A hallmark of human locomotion is that it continuously adapts to changes in the environment and predictively adjusts to changes in the terrain, both of which are major challenges to lower limb amputees due to the limitations in prostheses and control algorithms. Here, the ability of a single-network nonlinear autoregressive model to continuously predict future ankle kinematics and kinetics simultaneously across ambulation conditions using lower limb surface electromyography (EMG) signals was examined. Ankle plantarflexor and dorsiflexor EMG from ten healthy young adults were mapped to normal ranges of ankle angle and ankle moment during level overground walking, stair ascent, and stair descent, including transitions between terrains (i.e., transitions to/from staircase). Prediction performance was characterized as a function of the time between current EMG/angle/moment inputs and future angle/moment model predictions (prediction interval), the number of past EMG/angle/moment input values over time (sampling window), and the number of units in the network hidden layer that minimized error between experimentally measured values (targets) and model predictions of ankle angle and moment. Ankle angle and moment predictions were robust across ambulation conditions with root mean squared errors less than 1° and 0.04 Nm/kg, respectively, and cross-correlations (R2) greater than 0.99 for prediction intervals of 58 ms. Model predictions at critical points of trip-related fall risk fell within the variability of the ankle angle and moment targets (Benjamini-Hochberg adjusted p > 0.065). EMG contribution to ankle angle and moment predictions occurred consistently across ambulation conditions and model outputs. EMG signals had the greatest impact on noncyclic regions of gait such as double limb support, transitions between terrains, and around plantarflexion and moment peaks. The use of natural muscle activation patterns to continuously predict variations in normal gait and the model's predictive capabilities to counteract electromechanical inherent delays suggest that this approach could provide robust and intuitive user-driven real-time control of a wide variety of lower limb robotic devices, including active powered ankle-foot prostheses.