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2017 | OriginalPaper | Chapter

Multi-class SVM Based Real-Time Recognition of Sit-to-Stand and Stand-to-Sit Transitions for a Bionic Knee Exoskeleton in Transparent Mode

Authors : Xiuhua Liu, Zhihao Zhou, Jingeng Mai, Qining Wang

Published in: Intelligent Robotics and Applications

Publisher: Springer International Publishing

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Abstract

Real-time locomotion intent recognition is a challenge in lower-limb exoskeletons. In this paper, we present a multi-sensor based locomotion intent prediction system for sit-to-stand and stand-to-sit transition recognition the subject wears a knee exoskeleton in transparent mode. The desired reference torque for movement control is obtained from a direct torque control loop. The feedback torque is estimated by an inner current control loop. Five able-bodied subjects were recruited in experiments. The classifier is based on multi-class Support Vector Machine. Four kinds of modes and four kinds of transitions are tested in this study. Recognition accuracy during steady periods is 99.68% ± 0.07% for five able-bodied subjects. And during transition periods, all the transitions are correctly detected and no missed detections was observed for all the trials of the five subjects. Preliminary experimental results show that the proposed method is capable of performing real-time intent recognition and consequently reduces the interaction force between human body and the exoskeleton.

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Metadata
Title
Multi-class SVM Based Real-Time Recognition of Sit-to-Stand and Stand-to-Sit Transitions for a Bionic Knee Exoskeleton in Transparent Mode
Authors
Xiuhua Liu
Zhihao Zhou
Jingeng Mai
Qining Wang
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-65289-4_25

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