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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.
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A Robotic Assistance Personalization Control Approach of Hip Exoskeletons for Gait Symmetry Improvement
Healthy human locomotion functions with good gait symmetry depend on rhythmic coordination of the left and right legs, which can be deteriorated by neurological disorders like stroke and spinal cord injury. Powered exoskeletons are promising devices to improve impaired people's locomotion functions, like gait symmetry. However, given higher uncertainties and the time-varying nature of human-robot interaction, providing personalized robotic assistance from exoskeletons to achieve the best gait symmetry is challenging, especially for people with neurological disorders. In this paper, we propose a hierarchical control framework for a bilateral hip exoskeleton to provide the adaptive optimal hip joint assistance with a control objective of imposing the desired gait symmetry during walking. Three control levels are included in the hierarchical framework, including the high-level control to tune three control parameters based on a policy iteration reinforcement learning approach, the middle-level control to define the desired assistive torque profile based on a delayed output feedback control method, and the low-level control to achieve a good torque trajectory tracking performance. To evaluate the feasibility of the proposed control framework, five healthy young participants are recruited for treadmill walking experiments, where an artificial gait asymmetry is imitated as the hemiparesis post-stroke, and only the ‘paretic’ hip joint is controlled with the proposed framework. The pilot experimental studies demonstrate that the hierarchical control framework for the hip exoskeleton successfully (asymmetry index from 8.8% to − 0.5%) and efficiently (less than 4 minutes) achieved the desired gait symmetry by providing adaptive optimal assistance on the ‘paretic’ hip joint.
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- PAR ID:
- 10482750
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- ISSN:
- 2153-0866
- ISBN:
- 978-1-6654-9190-7
- Page Range / eLocation ID:
- 6125 to 6132
- Format(s):
- Medium: X
- Location:
- Detroit, MI, USA
- Sponsoring Org:
- National Science Foundation
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Robot-assisted gait training is becoming increasingly common to support recovery of walking function after neurological injury. How to formulate controllers capable of promoting desired features in gait, i.e. goals, is complicated by the limited understanding of the human response to robotic input. A possible method to formulate controllers for goal-oriented gait training is based on the analysis of the joint torques applied by healthy subjects to modulate such goals. The objective of this work is to understand how sagittal plane joint torque is affected by two important gait parameters: gait speed (GS) and stride length (SL). We here present the results obtained from healthy subjects walking on a treadmill at different speeds, and asked to modulate stride length via visual feedback. Via principal component analysis, we extracted the global effects of the two factors on the peak-to-peak amplitude of joint torques. Next, we used a torque pulse approximation analysis to determine optimal timing and amplitude of torque pulses that approximate the SL-specific difference in joint torque profiles measured at different values of GS. Our results show a strong effect of GS on the torque profiles in all joints considered. In contrast, SL mostly affects the torque produced at the knee joint at early and late stance, with smaller effects on the hip and ankle joints. Our analysis generated a set of torque assistance profiles that will be experimentally tested using gait training robots.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/IROS55552.2023.10341440
