Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network
文献类型：期刊论文
| 作者 | Wang, Shurun1; Tang, Hao1; Gao, Lifu2 ; Tan, Qi1
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| 刊名 | IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
 |
| 出版日期 | 2022-11-01 |
| 卷号 | 26 |
| 关键词 | Attention mechanism motion estimation surface electromyography temporal convolutional network |
| ISSN号 | 2168-2194 |
| DOI | 10.1109/JBHI.2022.3198640 |
| 通讯作者 | Tang, Hao(htang@hfut.edu.cn) |
| 英文摘要 | Intention recognition based on surface electromyography (sEMG) signals is pivotal in human-machine interaction (HMI), where continuous motion estimation with high accuracy has been the challenge. The convolutional neural network (CNN) possesses excellent feature extraction capability. Still, it is difficult for ordinary CNN to explore the dependencies of time-series data, so most researchers adopt the recurrent neural network or its variants (e.g., LSTM) for motion estimation tasks. This paper proposes a multi-feature temporal convolutional attention-based network (MFTCAN) to recognize joint angles continuously. First, we recruited ten subjects to accomplish the signal acquisition experiments in different motion patterns. Then, we developed a joint training mechanism that integrates MFTCAN with commonly used statistical algorithms, and the integrated architectures were named MFTCAN-KNR, MFTCAN-SVR and MFTCAN-LR. Last, we utilized two performance indicators (RMSE and R-2) to evaluate the effect of different methods. Moreover, we further validated the performance of the proposed method on the open dataset (Ninapro DB2). When evaluating on the original dataset, the average RMSE of the estimations obtained by MFTCAN-KNR is 0.14, which is significantly less than the results obtained by LSTM (0.20) and BP (0.21). The average R-2 of the estimations obtained by MFTCAN-KNR is 0.87, indicating the anti-disturbance ability of the architecture. Moreover, MFTCAN-KNR also achieves high performance when evaluating on the open dataset. The proposed methods can effectively accomplish the task of motion estimation, allowing further implementations in the human-exoskeleton interaction systems. |
| WOS关键词 | MOVEMENTS |
| 资助项目 | National Key R&D Program of China[2017YFE0129700] |
| WOS研究方向 | Computer Science ; Mathematical & Computational Biology ; Medical Informatics |
| 语种 | 英语 |
| WOS记录号 | WOS:000882005700023 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key R&D Program of China |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/130343]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Tang, Hao |
| 作者单位 | 1.Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Shurun,Tang, Hao,Gao, Lifu,et al. Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network[J]. IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,2022,26. |
| APA | Wang, Shurun,Tang, Hao,Gao, Lifu,&Tan, Qi.(2022).Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network.IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS,26. |
| MLA | Wang, Shurun,et al."Continuous Estimation of Human Joint Angles From sEMG Using a Multi-Feature Temporal Convolutional Attention-Based Network".IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 26(2022). |
入库方式： OAI收割
来源：合肥物质科学研究院
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