Abstract
The amount of research on developing exoskeletons for human gait assistance has been growing in the recent years. However, the control design of exoskeletons for assisting human walking remains unclear. This paper presents a novel bio-inspired reflex-based control for assisting human walking. In this approach, the leg force is used as a feedback signal to adjust hip compliance. The effects of modulating hip compliance on walking gait is investigated through joint kinematics, leg muscle activations and overall metabolic costs for eight healthy young subjects. Reduction in the average metabolic cost and muscle activation are achieved with fixed hip compliance. Compared to the fixed hip compliance, improved assistance as reflected in more consistent reduction in muscle activities and more natural kinematic behaviour are obtained using the leg force feedback. Furthermore, smoother motor torques and less peak power are two additional advantages obtained by compliance modulation. The results show that the proposed control method which is inspired by human posture control can not only facilitate the human gait, but also reduce the exoskeleton power consumption. This demonstrates that the proposed bio-inspired controller allows a synergistic interaction between human and robot.
Highlights
H UMAN locomotor systems comprise complex but intensively coupled mechanics and control to achieveManuscript received April 12, 2019; revised June 14, 2019; accepted July 15, 2019
The results demonstrate the effects of these two control methods on metabolic costs, muscle activation and kinematic behavior
Fl l rh sin φ + r p sin(φ + γ ) − rh cos φ − r p cos(φ + γ where τh is hip torque, F is leg force, l is leg length, φ is hip angle, r p is the distance from center of mas (CoM) to virtual pivot point (VPP), and rh is the distance from CoM to hip joint. γ denotes the angle between trunk axis and the vector from CoM to VPP
Summary
H UMAN locomotor systems comprise complex but intensively coupled mechanics and control to achieveManuscript received April 12, 2019; revised June 14, 2019; accepted July 15, 2019. M. Ahmad Sharbafi is with the Lauflabor Locomotion Laboratory, Centre for Cognitive Science, Institute of Sport Science, Technische Universität Darmstadt, 64289 Darmstadt, Germany, and with the Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran 14395/515, Iran. M. Vlutters and E. van Asseldonk are with the Department of Biomechanical Engineering, University of Twente, 7500 Enschede, The Netherlands
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