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Phase Variable Based Recognition of Human Locomotor Activities Across Diverse Gait Patterns
Tan XW(谈晓伟)1,2,3; Zhang B(张弼)1,2; Liu GJ(刘光军)4; Zhao XG(赵新刚)1,2; Zhao YW(赵忆文)1,2
Department机器人学研究室
Source PublicationIEEE Transactions on Human-Machine Systems
ISSN2168-2291
2021
Volume51Issue:6Pages:684-695
Indexed BySCI ; EI
EI Accession number20213810925503
WOS IDWOS:000719561500016
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China under Grant U1813214, Grant 61821005, and Grant 61773369 ; China Postdoctoral Science Foundation under Grant 2019M661157
KeywordActivity recognition classification algorithms gait recognition patient monitoring phase
Abstract

Human locomotor activity (LA) recognition is important in the control of exoskeletons and prostheses and in patient monitoring. This article presents a practical recognition approach that can classify level walking, stair ascent, and stair descent activities across different subjects and diverse gait patterns. The thigh angle is measured and utilized in this method to construct a phase curve in an activity-specific coordinate frame during a stride. The LA is recognized by matching the curvature of its phase curve to the expected one. The factors affecting the adaptability of the proposed method to gait variations are analyzed and compensated for. The proposed method is evaluated with eight subjects who are asked to perform the three types of activity at two different cadences: 70 stepsmin and 110 stepsmin. Experimental results show that the proposed classifier outperforms an existing phase variable based classifier in all validation experiments and a ${\bm{k}}$-nearest neighbor classifier when using nonsubject-specific training data, indicating that the proposed method has superior adaptability to changes in human and in strides. Moreover, the feature used in the proposed method has demonstrated the potential in quantitatively indicating the extent of neuromotor impairments of patients.

Language英语
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS Research AreaComputer Science
Funding ProjectNational Natural Science Foundation of China[U1813214] ; National Natural Science Foundation of China[61821005] ; National Natural Science Foundation of China[61773369] ; China Postdoctoral Science Foundation[2019M661157]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/29673
Collection机器人学研究室
Corresponding AuthorZhang B(张弼); Zhao XG(赵新刚)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016 China
2.Institute for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169 China
3.University of Chinese Academy of Sciences, Beijing 100049 China
4.Department of Aerospace Engineering, Ryerson University, Toronto, ON M5B 2K3 Canada
Recommended Citation
GB/T 7714
Tan XW,Zhang B,Liu GJ,et al. Phase Variable Based Recognition of Human Locomotor Activities Across Diverse Gait Patterns[J]. IEEE Transactions on Human-Machine Systems,2021,51(6):684-695.
APA Tan XW,Zhang B,Liu GJ,Zhao XG,&Zhao YW.(2021).Phase Variable Based Recognition of Human Locomotor Activities Across Diverse Gait Patterns.IEEE Transactions on Human-Machine Systems,51(6),684-695.
MLA Tan XW,et al."Phase Variable Based Recognition of Human Locomotor Activities Across Diverse Gait Patterns".IEEE Transactions on Human-Machine Systems 51.6(2021):684-695.
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