Selective Assist Strategy by Using Lightweight Carbon Frame Exoskeleton Robot

Exoskeleton robots need to always actively assist the user's movements otherwise robot just becomes a heavy load for the user. However, estimating diversified movement intentions in a user's daily life is not easy and no algorithm so far has achieved that level of estimation. In this study...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 7; no. 2; pp. 3890 - 3897
Main Authors: Furukawa, Jun-ichiro, Okajima, Shotaro, An, Qi, Nakamura, Yuichi, Morimoto, Jun
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Actuators
Algorithms
Artificial muscles
Carbon
Carbon fibers
Classification
Classifiers
Estimation
Exoskeletons
intention recognition
Lightweight
Muscles
optimization and optimal control
Pneumatic systems
Prosthetics and exoskeletons
Robot sensing systems
Robots
Torque
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:Exoskeleton robots need to always actively assist the user's movements otherwise robot just becomes a heavy load for the user. However, estimating diversified movement intentions in a user's daily life is not easy and no algorithm so far has achieved that level of estimation. In this study, we rather focus on estimating and assisting a limited number of selected movements by using an EMG-based movement classification and a newly developed lightweight exoskeleton robot. Our lightweight knee exoskeleton is composed of a carbon fiber frame and highly backdrivable joint driven by a pneumatic artificial muscle. Thus, our robot does not interfere with the user's motions even when the actuator is not activated. As the classification method, we adopted a positive-unlabeled (PU) classifier. Since precisely labeling all the selected data from large-scale daily movements is not practical, we assumed that only part of the selected data was labeled and used a PU classifier that can handle the unlabeled data. To validate our approach, we conducted experiments with five healthy subjects to selectively assist sit-to-stand movements from four possible daily motions. We compared our approach with two classification methods that assume fully labeled data. The results showed that all subject's movements were properly assisted.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3148799