A long short-term memory neural network model for knee joint acceleration estimation using mechanomyography signals
文献类型：期刊论文
| 作者 | Xie, Chenlei1,2,4; Wang, Daqing2; Wu, Haifeng3 ; Gao, Lifu2
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| 刊名 | INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
 |
| 出版日期 | 2020-11-01 |
| 卷号 | 17 |
| 关键词 | Knee joint MMG PCA LSTM acceleration estimation |
| ISSN号 | 1729-8814 |
| DOI | 10.1177/1729881420968702 |
| 通讯作者 | Gao, Lifu(lifugao@iim.ac.cn) |
| 英文摘要 | With the growth of the number of elderly and disabled with motor dysfunction, the demand for assisted exercise is increasing. Wearable power assistance robots are developed to provide athletic ability of limbs for the elderly or the disabled who have weakened limbs to better self-care ability. Existing wearable power-assisted robots generally use surface electromyography (sEMG) to obtain effective human motion intentions. Due to the characteristics of sEMG signals, it is limited in many applications. To solve the above problems, we design a long short-term memory (LSTM) neural network model based on human mechanomyography (MMG) signals to estimate the motion acceleration of knee joint. The acceleration can be further calculated by the torque required for movement control of the wearable power assistance robots for the lower limb. We detect MMG signals on the clothed thigh, extract features of the MMG signals, and then, use principal component analysis to reduce the features' dimensions. Finally, the dimension-reduced features are inputted into the LSTM neural network model in time series for estimating the acceleration. The experimental results show that the average correlation coefficient (R) is 94.48 +/- 1.91% for the estimation of acceleration in the process of continuously performing under approximately pi/4 rad/s. This approach can be applied in the practical applications of wearable field. |
| WOS关键词 | RECOGNITION |
| 资助项目 | Strategic Priority Research Program of the Chinese Academy of Sciences[XDA22040303] ; Natural Science Research Project of Anhui Jianzhu University[JZ192012] ; State Key Laboratory of Transducer Technology |
| WOS研究方向 | Robotics |
| 语种 | 英语 |
| WOS记录号 | WOS:000591340000001 |
| 出版者 | SAGE PUBLICATIONS INC |
| 资助机构 | Strategic Priority Research Program of the Chinese Academy of Sciences ; Natural Science Research Project of Anhui Jianzhu University ; State Key Laboratory of Transducer Technology |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/105317]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Gao, Lifu |
| 作者单位 | 1.Univ Sci & Technol China, Dept Sci Isl, Hefei, Peoples R China 2.Chinese Acad Sci, Inst Intelligent Machines, Hefei Inst Phys Sci, Hefei 23031, Peoples R China 3.Chinese Acad Sci, Hefei Inst Phys Sci, High Field Magnet Lab, Hefei, Peoples R China 4.Anhui Jianzhu Univ, Anhui Prov Key Lab Intelligent Bldg & Bldg Energy, Hefei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xie, Chenlei,Wang, Daqing,Wu, Haifeng,et al. A long short-term memory neural network model for knee joint acceleration estimation using mechanomyography signals[J]. INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,2020,17. |
| APA | Xie, Chenlei,Wang, Daqing,Wu, Haifeng,&Gao, Lifu.(2020).A long short-term memory neural network model for knee joint acceleration estimation using mechanomyography signals.INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS,17. |
| MLA | Xie, Chenlei,et al."A long short-term memory neural network model for knee joint acceleration estimation using mechanomyography signals".INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 17(2020). |
入库方式： OAI收割
来源：合肥物质科学研究院
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