CASIA OpenIR  > 智能制造技术与系统研究中心  > 先进制造与自动化
Gait Pattern Identification and Phase Estimation in Continuous Multilocomotion Mode Based on Inertial Measurement Units
Zhang, Xingxuan1,2; Zhang, Haojian1; Hu, Jianhua1; Zheng, Jun1; Wang, Xinbo1; Deng, Jieren1,2; Wan, Zihao1,2; Wang, Haotian1,2; Wang, Yunkuan1
Source PublicationIEEE SENSORS JOURNAL
ISSN1530-437X
2022-09-01
Volume22Issue:17Pages:16952-16962
Corresponding AuthorWang, Yunkuan(yunkuan.wang@ia.ac.cn)
AbstractIn the field of lower limb exoskeletons, it is essential to accurately estimate the gait phase of humans. Many methods have been proposed to estimate the gait phase, but only a few studies have considered the multi-locomotion mode. This paper proposes a novel inertial measurement unit(IMU)-based method to estimate the gait phase of a pilot in continuous multi-locomotion mode. The method includes gait pattern recognition based on long short-term memory (LSTM), continuous phase estimation based on a dual adaptive frequency oscillator(DAFO), threshold-based toe-off event detection and a rule-based gait phase synchronization module. First, we used the LSTM-based network to identify four gait patterns including standing, level ground walking, upstairs and downstairs. Next, the DAFO was used to obtain the continuous gait phase of the pilot. Then, we detected the gait events in different gait modes. Finally, the continuous gait phase was synchronized according to the gait events. The experimental result shows that the gait pattern classification accuracy using 5 IMUs is 98.58% and the F-1 score reaches 0.9875. The proposed DAFO model can maintain good stability when multiple gait modes are frequently switched, significantly improving the problem of slow convergence and the poor robustness of single adaptive frequency oscillator(SAFO) models. Toe-off gait events of 492 steps are all detected and the average error at the detected gait events in different gait modes is 15.34 +/- 40.58 ms.
KeywordGait pattern identification phase estimate adaptive oscillators gait event detector wearable robots
DOI10.1109/JSEN.2022.3175823
WOS KeywordEXTREMITY EXOSKELETON ROBOT ; REAL-TIME ESTIMATE ; INTENT RECOGNITION ; EVENT DETECTION ; OSCILLATOR
Indexed BySCI
Language英语
Funding ProjectIntelligent Manufacturing Comprehensive Standardization and New Model Application Project of the Ministry of Industry and Information Technology of the People's Republic of China[Y8G1041CB1]
Funding OrganizationIntelligent Manufacturing Comprehensive Standardization and New Model Application Project of the Ministry of Industry and Information Technology of the People's Republic of China
WOS Research AreaEngineering ; Instruments & Instrumentation ; Physics
WOS SubjectEngineering, Electrical & Electronic ; Instruments & Instrumentation ; Physics, Applied
WOS IDWOS:000849268700034
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/50081
Collection智能制造技术与系统研究中心_先进制造与自动化
Corresponding AuthorWang, Yunkuan
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Xingxuan,Zhang, Haojian,Hu, Jianhua,et al. Gait Pattern Identification and Phase Estimation in Continuous Multilocomotion Mode Based on Inertial Measurement Units[J]. IEEE SENSORS JOURNAL,2022,22(17):16952-16962.
APA Zhang, Xingxuan.,Zhang, Haojian.,Hu, Jianhua.,Zheng, Jun.,Wang, Xinbo.,...&Wang, Yunkuan.(2022).Gait Pattern Identification and Phase Estimation in Continuous Multilocomotion Mode Based on Inertial Measurement Units.IEEE SENSORS JOURNAL,22(17),16952-16962.
MLA Zhang, Xingxuan,et al."Gait Pattern Identification and Phase Estimation in Continuous Multilocomotion Mode Based on Inertial Measurement Units".IEEE SENSORS JOURNAL 22.17(2022):16952-16962.
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