Assistive Powered Hip Exoskeleton Improves Self-Selected Walking Speed in One Individual with Hemiparesis: A Case Study

Most individuals suffering a stroke have permanent weakness on one side of the body (hemiparesis) that reduces their ability to ambulate. Autonomous powered exoskeletons have been proposed as a possible solution to this problem. Studies with healthy subjects show that assistive powered exoskeletons have the potential to improve gait, for example, by reducing the metabolic cost of walking. However, only a handful of studies have been conducted with individuals with hemiparesis. Thus, the ability of autonomous exoskeletons to improve gait in this population remains largely unknown. In this study, we assess self-selected walking speed with and without an autonomous powered hip exoskeleton in one individual with hemiparesis walking on level ground. Results show that the proposed exoskeleton improves self-selected walking speed by ~30%. The biomechanical analysis suggest that the increased walking speed is the result of the powered hip exoskeleton enabling the subject to take longer strides on the hemiparetic side. This case study provides important information to inform future exoskeleton development and clinical study design.

Powered Hip Exoskeleton Reduces Residual Hip Effort without Affecting Kinematics and Balance in Individuals with Above-Knee Amputations During Walking

OBJECTIVE

A unilateral, lightweight powered hip exoskeleton has been shown to improve walking economy in individuals with above-knee amputations. However, the mechanism responsible for this improvement is unknown. In this study we assess the biomechanics of individuals with above-knee amputations walking with and without a unilateral, lightweight powered hip exoskeleton. We hypothesize that assisting the residual limb will reduce the net residual hip energy.

METHODS

Eight individuals with above-knee amputations walked on a treadmill at 1 m/s with and without a unilateral powered hip exoskeleton. Flexion/extension assistance was provided to the residual hip. Motion capture and inverse dynamic analysis were performed to assess gait kinematics, kinetics, center of mass, and center of pressure.

RESULTS

The net energy at the residual hip decreased from 0.05±0.04 J/kg without the exoskeleton to -0.01±0.05 J/kg with the exoskeleton (p = 0.026). The cumulative positive energy of the residual hip decreased on average by 18.2% with 95% confidence intervals (CI) (0.20 J/kg, 0.24 J/kg) and (0.16 J/kg, 0.20 J/kg) without and with the exoskeleton, respectively. During stance, the hip extension torque of the residual limb decreased on average by 37.5%, 95% CI (0.28 Nm/kg, 0.36 Nm/kg), (0.17 Nm/kg, 0.23 Nm/kg) without and with the exoskeleton, respectively.

CONCLUSION

Powered hip exoskeleton assistance significantly reduced the net residual hip energy, with concentric energy being the main contributor to this change. We believe that the reduction in residual hip extension torque during early stance is the main contributor to this reduction.

SIGNIFICANCE

This analysis shows that by assisting the residual hip, the exoskeleton significantly decreased the net hip energy produced by the residual limb, which may explain the improvements in walking economy previously observed.

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.