Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm
文献类型：期刊论文
| 作者 | Li, Zebin1,2,3; Gao, Lifu3 ; Lu, Wei2,3; Wang, Daqing3; Xie, Chenlei3; Cao, Huibin3
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| 刊名 | ELECTRONICS
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| 出版日期 | 2021-12-01 |
| 卷号 | 10 |
| 关键词 | mechanomyography knee joint extension force improved gray wolf algorithm support vector machine |
| DOI | 10.3390/electronics10232972 |
| 通讯作者 | Li, Zebin(robotzebinli@foxmail.com) ; Cao, Huibin(hbcao@iim.ac.cn) |
| 英文摘要 | Muscle force is an important physiological parameter of the human body. Accurate estimation of the muscle force can improve the stability and flexibility of lower limb joint auxiliary equipment. Nevertheless, the existing force estimation methods can neither satisfy the accuracy requirement nor ensure the validity of estimation results. It is a very challenging task that needs to be solved. Among many optimization algorithms, gray wolf optimization (GWO) is widely used to find the optimal parameters of the regression model because of its superior optimization ability. Due to the traditional GWO being prone to fall into local optimum, a new nonlinear convergence factor and a new position update strategy are employed to balance local and global search capability. In this paper, an improved gray wolf optimization (IGWO) algorithm to optimize the support vector regression (SVR) is developed to estimate knee joint extension force accurately and timely. Firstly, mechanomyography (MMG) of the lower limb is measured by acceleration sensors during leg isometric muscle contractions extension training. Secondly, root mean square (RMS), mean absolute value (MAV), zero crossing (ZC), mean power frequency (MPF), and sample entropy (SE) of the MMG are extracted to construct feature sets as candidate data sets for regression analysis. Lastly, the features are fed into IGWO-SVR for further training. Experiments demonstrate that the IGWO-SVR provides the best performance indexes in the estimation of knee joint extension force in terms of RMSE, MAPE, and R compared with the other state-of-art models. These results are expected to become the most effective as guidance for rehabilitation training, muscle disease diagnosis, and health evaluation. |
| WOS关键词 | WOLF OPTIMIZATION |
| WOS研究方向 | Computer Science ; Engineering ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:000735160100001 |
| 出版者 | MDPI |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/127189]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Li, Zebin; Cao, Huibin |
| 作者单位 | 1.West Anhui Univ, Sch Elect & Photoelect Engn, Luan 237012, Peoples R China 2.Univ Sci & Technol China, Dept Sci Isl, Hefei 230026, Peoples R China 3.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, Hefei 230031, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Zebin,Gao, Lifu,Lu, Wei,et al. Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm[J]. ELECTRONICS,2021,10. |
| APA | Li, Zebin,Gao, Lifu,Lu, Wei,Wang, Daqing,Xie, Chenlei,&Cao, Huibin.(2021).Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm.ELECTRONICS,10. |
| MLA | Li, Zebin,et al."Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm".ELECTRONICS 10(2021). |
入库方式： OAI收割
来源：合肥物质科学研究院
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