Knee Exoskeleton Reduces Muscle Effort and Improves Balance During Sit-to-Stand Transitions After Stroke: A Case Study.

After a stroke, the weight-bearing asymmetry often forces stroke survivors to compensate with overuse of the unaffected side muscles to stand up. Powered exoskeletons can address this problem by assisting the affected limb during sit-tostand transitions. However, there is currently no experimental evidence demonstrating the efficacy of this intervention with the target population. This study explores controlling a powered knee exoskeleton with EMG signals to assist a stroke patient during sit-to-stand transitions. Our results show decreased peak knee torques by 6.24% and 11.9% on their unaffected and affected sides, respectively, while wearing the exoskeleton. Additionally, the peak value of the EMG signal decreased by 29.3% and 21.9%, and the integrated EMG signal value decreased by 46.7% and 36.1% on their affected vastus medialis and lateralis while wearing the exoskeleton, respectively. Finally, our results indicate improved medial-lateral balance by 61.2%, 81.6%, and 70.0% based on the degree of asymmetry (DOA), the center of pressure (COP), and the center of mass (COM), respectively. These results support the efficacy of using powered exoskeletons for high-torque tasks such as sit-to-stand transitions with stroke survivors.

Robust Torque Predictions From Electromyography Across Multiple Levels of Active Exoskeleton Assistance Despite Non-linear Reorganization of Locomotor Output.

Robotic exoskeletons can assist humans with walking by providing supplemental torque in proportion to the user's joint torque. Electromyographic (EMG) control algorithms can estimate a user's joint torque directly using real-time EMG recordings from the muscles that generate the torque. However, EMG signals change as a result of supplemental torque from an exoskeleton, resulting in unreliable estimates of the user's joint torque during active exoskeleton assistance. Here, we present an EMG control framework for robotic exoskeletons that provides consistent joint torque predictions across varying levels of assistance. Experiments with three healthy human participants showed that using diverse training data (from different levels of assistance) enables robust torque predictions, and that a convolutional neural network (CNN), but not a Kalman filter (KF), can capture the non-linear transformations in EMG due to exoskeleton assistance. With diverse training, the CNN could reliably predict joint torque from EMG during zero, low, medium, and high levels of exoskeleton assistance [root mean squared error (RMSE) below 0.096 N-m/kg]. In contrast, without diverse training, RMSE of the CNN ranged from 0.106 to 0.144 N-m/kg. RMSE of the KF ranged from 0.137 to 0.182 N-m/kg without diverse training, and did not improve with diverse training. When participant time is limited, training data should emphasize the highest levels of assistance first and utilize at least 35 full gait cycles for the CNN. The results presented here constitute an important step toward adaptive and robust human augmentation via robotic exoskeletons. This work also highlights the non-linear reorganization of locomotor output when using assistive exoskeletons; significant reductions in EMG activity were observed for the soleus and gastrocnemius, and a significant increase in EMG activity was observed for the erector spinae. Control algorithms that can accommodate spatiotemporal changes in muscle activity have broad implications for exoskeleton-based assistance and rehabilitation following neuromuscular injury.