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Open AccessArticle

Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors

by Du-Xin Liu 1,2,3, Xinyu Wu 1,2,4,*, Wenbin Du 1,2,3, Can Wang 1,2 and Tiantian Xu 1,2
Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Chinese Academy of Sciences (CAS) Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen 518055, China
University of Chinese Academy of Sciences, Beijing 100049, China
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Hong Kong, China
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Sensors 2016, 16(10), 1579;
Received: 28 June 2016 / Revised: 18 September 2016 / Accepted: 20 September 2016 / Published: 27 September 2016
(This article belongs to the Special Issue Wearable Biomedical Sensors)
Gait phase is widely used for gait trajectory generation, gait control and gait evaluation on lower-limb exoskeletons. So far, a variety of methods have been developed to identify the gait phase for lower-limb exoskeletons. Angular sensors on lower-limb exoskeletons are essential for joint closed-loop controlling; however, other types of sensors, such as plantar pressure, attitude or inertial measurement unit, are not indispensable.Therefore, to make full use of existing sensors, we propose a novel gait phase recognition method for lower-limb exoskeletons using only joint angular sensors. The method consists of two procedures. Firstly, the gait deviation distances during walking are calculated and classified by Fisher’s linear discriminant method, and one gait cycle is divided into eight gait phases. The validity of the classification results is also verified based on large gait samples. Secondly, we build a gait phase recognition model based on multilayer perceptron and train it with the phase-labeled gait data. The experimental result of cross-validation shows that the model has a 94.45% average correct rate of set (CRS) and an 87.22% average correct rate of phase (CRP) on the testing set, and it can predict the gait phase accurately. The novel method avoids installing additional sensors on the exoskeleton or human body and simplifies the sensory system of the lower-limb exoskeleton. View Full-Text
Keywords: gait phase classification; gait phase recognition; lower-limb exoskeleton gait phase classification; gait phase recognition; lower-limb exoskeleton
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Liu, D.-X.; Wu, X.; Du, W.; Wang, C.; Xu, T. Gait Phase Recognition for Lower-Limb Exoskeleton with Only Joint Angular Sensors. Sensors 2016, 16, 1579.

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