Arm-based control of a lower limb exoskeleton

Proof of concept of a novel approach based on the shoulder kinematics

Master thesis (2019)

Authors

F. Izzi Mechanical Engineering

Contributors

H. van der Kooij Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (supervisor 1)
H. Vallery Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (coach)
L. Peternel Human-Robot Interaction - Mechanical, Maritime and Materials Engineering (coach)
Faculty
Mechanical Engineering
Copyright: © 2019 Fabio Izzi
Interlimb coordination Lower limb exoskeleton Arm-based control Wearable robotics control
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Published Date

10-05-2019

Language

English

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Faculty

Mechanical Engineering

Abstract

Recognising the user’s locomotive intentions is crucial for the correct functionality of exoskeletons and active orthoses. For gait applications, extrapolating control inputs from the arm swing may be worthwhile, since arm oscillations naturally occur during human locomotion. A similar method would be unaffected by severe impairments of the lower limbs, and there is evidence suggesting enhanced results of gait rehabilitation when arms and legs exercise together. In this thesis, we propose a control algorithm to drive online a lower limb exoskeleton through the arm swing. Contrary to a previous EMG-based approach by La Scaleia et al. (2014), our algorithm exploits shoulder kinematic data to mimic “single swinging”, a natural mode of human interlimb coordination which is characterised by each arm swinging in-phase with the contralateral leg. Our proposed control architecture relies on two major modules: an Arm Observer and a Gait Generator. The Arm Observer consists of an adaptive frequency oscillator which extrapolates the frequency and phase of the arm swing by receiving online measurements of the angular shoulder position in the sagittal plane. This data is used by the Gait Generator to compute lower limb trajectories, based on regression models from a previous study by Koopman et al. (2014). We validated our controller through human-subject experiments, involving three participants walking on a treadmill with and without a lower limb exoskeleton, the Lopes II. When feed by data associated with natural walking, our adaptive frequency oscillator could very precisely replicate the arm swing frequency, stride cadence and timing of shoulder flexion peaks when walking faster than 0.5 m/s. When wearing the exoskeleton, our algorithm allowed the participants to cope with constant and variable treadmill velocities in the range of 0.5-1.25 m/s. As such, the results of this thesis show that our proposed approach can extend the applicability of arm-based control to walking speeds suitable for gait rehabilitation and assistance.