Gait Pattern Identification and Phase Estimation in Continuous Multilocomotion Mode Based on Inertial Measurement Units
文献类型：期刊论文
| 作者 | Zhang, Xingxuan1,2 ; Zhang, Haojian2; Hu, Jianhua2; Zheng, Jun2; Wang, Xinbo2; Deng, Jieren1,2; Wan, Zihao1,2; Wang, Haotian1,2; Wang, Yunkuan2
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| 刊名 | IEEE SENSORS JOURNAL
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| 出版日期 | 2022-09-01 |
| 卷号 | 22期号:17页码:16952-16962 |
| ISSN号 | 1530-437X |
| 关键词 | Gait pattern identification phase estimate adaptive oscillators gait event detector wearable robots |
| DOI | 10.1109/JSEN.2022.3175823 |
| 通讯作者 | Wang, Yunkuan(yunkuan.wang@ia.ac.cn) |
| 英文摘要 | In 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. |
| WOS关键词 | EXTREMITY EXOSKELETON ROBOT ; REAL-TIME ESTIMATE ; INTENT RECOGNITION ; EVENT DETECTION ; OSCILLATOR |
| 资助项目 | Intelligent Manufacturing Comprehensive Standardization and New Model Application Project of the Ministry of Industry and Information Technology of the People's Republic of China[Y8G1041CB1] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation ; Physics |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000849268700034 |
| 资助机构 | Intelligent Manufacturing Comprehensive Standardization and New Model Application Project of the Ministry of Industry and Information Technology of the People's Republic of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/50080]  |
| 专题 | 智能制造技术与系统研究中心_先进制造与自动化 |
| 通讯作者 | Wang, Yunkuan |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：自动化研究所
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