sEMG-based continuous estimation of joint angles of human legs by using BP neural network
文献类型：期刊论文
| 作者 | Zhang, Feng1 ; Li, Pengfeng1; Hou, Zeng-Guang1; Lu, Zhen2; Chen, Yixiong1; Li, Qingling1; Tan, Min1
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| 刊名 | NEUROCOMPUTING
 |
| 出版日期 | 2012-02-15 |
| 卷号 | 78期号:1页码:139-148 |
| 关键词 | sEMG Rehabilitation BP SCI |
| 英文摘要 | In this paper, we propose an mth order nonlinear model to describe the relationship between the surface electromyography (sEMG) signals and the joint angles of human legs, in which a simple BP neural network is built for the model estimation. The inputs of the model are sEMG time series that have been processed, and the outputs of the model are the joint angles of hip, knee, and ankle. To validate the effectiveness of the BP neural network, six able-bodied people and four spinal cord injury (SCI) patients participated in the experiment. Two movement modes including the treadmill exercise and the leg extension exercise at different speeds and different loads were respectively conducted by the able-bodied individuals, and only the treadmill exercise was selected for the SCI patients. Seven channels of sEMG from seven human leg muscles were recorded and three joint angles including the hip joint, knee joint and the ankle joint were sampled simultaneously. The results present that this method has a good performance on joint angles estimation by using sEMG for both able-bodied subjects and SCI patients. The average angle estimation root-mean-square (rms) error for leg extension exercise is less than 9 degrees, and the average rms error for treadmill exercise is less than 6 degrees for all the able-bodied subjects. The average angle estimation rms error of the SCI patients is even smaller (less than 5 degrees) than that of the able-bodied people because of a smaller movement range. This method would be used to rehabilitation robot or functional electrical stimulation (FES) for active rehabilitation of SCI patients or stroke patients based on sEMG signals. (C) 2011 Elsevier B.V. All rights reserved. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Computer Science, Artificial Intelligence |
| 研究领域[WOS] | Computer Science |
| 关键词[WOS] | SURFACE EMG ; SIGNAL BANDWIDTH ; MUSCLE ; MODEL ; ROBOT ; CLASSIFICATION ; ARM |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS记录号 | WOS:000298528200017 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3513]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing 100190, Peoples R China 2.China Rehabil Res Ctr, Dept SCI Surg, Beijing 100068, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Feng,Li, Pengfeng,Hou, Zeng-Guang,et al. sEMG-based continuous estimation of joint angles of human legs by using BP neural network[J]. NEUROCOMPUTING,2012,78(1):139-148. |
| APA | Zhang, Feng.,Li, Pengfeng.,Hou, Zeng-Guang.,Lu, Zhen.,Chen, Yixiong.,...&Tan, Min.(2012).sEMG-based continuous estimation of joint angles of human legs by using BP neural network.NEUROCOMPUTING,78(1),139-148. |
| MLA | Zhang, Feng,et al."sEMG-based continuous estimation of joint angles of human legs by using BP neural network".NEUROCOMPUTING 78.1(2012):139-148. |
入库方式： OAI收割
来源：自动化研究所
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