The purpose of this study was to explore the effectiveness of a neural controller for a single-joint bilateral hip exoskeleton. The device provides mechanical torque in the sagittal plane and uses series elastic actuators for feedback control. The system consists of three control layers: (1) a high-level controller that estimates the current gait phase, (2) a mid-level controller that converts the electromyography (EMG) signals to desired exoskeleton torques, and (3) a low-level controller that ensures the output torque matches the commanded torque. To evaluate the effectiveness of the proportional EMG controller, one able-body subject walked with the exoskeleton under 3 assistance conditions: (1) a baseline proportional gain condition (× G), (2) a double proportional gain condition (× 2G) for faster scaling, and (3) an on/off set value torque assistance (SV). The third condition provides the same net mechanical power as the baseline (× G) condition to compare whether proportional scaling of the hip torque was significant. The subject’s hip-joint kinematics, metabolic rate, and muscle activities were collected as outcome measurements. In summary, the EMG controller could generate seamless torque to the user with a response time of 80 ms. The × 2G condition resulted in a 23.3% EMG activity reduction while SV condition reduced the metabolic rate by 8.1%. Interestingly, the largest EMG reduction condition (× 2G) did not result in largest metabolic reduction (SV). Our preliminary findings suggest that the proportional scaling of the hip torque may not be the most important parameter to minimize metabolic cost.
- Dynamic Systems and Control Division
Design and Evaluation of a Proportional Myoelectric Controller for Hip Exoskeletons During Walking
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Hsu, H, Kang, I, & Young, AJ. "Design and Evaluation of a Proportional Myoelectric Controller for Hip Exoskeletons During Walking." Proceedings of the ASME 2018 Dynamic Systems and Control Conference. Volume 1: Advances in Control Design Methods; Advances in Nonlinear Control; Advances in Robotics; Assistive and Rehabilitation Robotics; Automotive Dynamics and Emerging Powertrain Technologies; Automotive Systems; Bio Engineering Applications; Bio-Mechatronics and Physical Human Robot Interaction; Biomedical and Neural Systems; Biomedical and Neural Systems Modeling, Diagnostics, and Healthcare. Atlanta, Georgia, USA. September 30–October 3, 2018. V001T13A005. ASME. https://doi.org/10.1115/DSCC2018-9226
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