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Enable Fully Customized Assistance: A Novel IMU-based Motor Intent Decoding Scheme

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journal contribution
posted on 01.12.2021, 09:45 authored by C Yi, S Zhang, F Jiang, J Liu, Z Ding, C Yang, Huiyu Zhou
Assisting human locomotion is essentially related to the assistive force profile, which can be determined from four aspects: timing, magnitude, shape and duration. Most current methods of decoding human motor intent enable the customized determination of the assistive force profile by providing information of different subsets of the four aspects. Trustworthy human-exoskeleton interaction essentially relates to determining the assistive force profile. Current methods of decoding human motor intent enable the customized determination of the assistive force profile by providing limited information of human kinetics. In this paper, we propose and validate a novel motor intent decoding scheme that can enable a fully customized assistive force profile, where only inertial measurement units (IMUs) are used. First, we improve the robustness of the IMU-based kinematic estimation by sampling IMU measurements that well meet the hinge-joint assumption, and by online calibrating axes’ direction in order to avoid the post-hoc analysis of joint axes’ directions during the determination of the body-fixed coordinate frame. Second, using the calculated kinematics as input, we develop a computationally efficient dynamic model, through which kinetics of users can be calculated in real-time. Finally, we leverage a cable-driven ankle exoskeleton method to validate the assistive performance of our motor intent decoding scheme. We perform experiments on ten healthy subjects to evaluate the accuracy of our algorithm, and the change of metabolic rate and muscle efforts under the exoskeleton’s assistance. The results show the improvement from determining the assistive force profile by nominal curves and the feasibility of our algorithm.

History

Citation

IEEE Transactions on Cognitive and Developmental Systems, 2021, https://doi.org/10.1109/TCDS.2021.3126001

Author affiliation

School of Informatics

Version

AM (Accepted Manuscript)

Published in

IEEE Transactions on Cognitive and Developmental Systems

Publisher

Institute of Electrical and Electronics Engineers

issn

2379-8920

Acceptance date

01/11/2021

Copyright date

2021

Available date

01/12/2021

Language

en