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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Vydané v:IEEE robotics and automation letters Ročník 7; číslo 2; s. 3890 - 3897
Hlavní autori: Furukawa, Jun-ichiro, Okajima, Shotaro, An, Qi, Nakamura, Yuichi, Morimoto, Jun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3148799