CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
sEMG-Based Torque Estimation for Robot-Assisted Lower Limb Rehabilitation
Peng, Long1; Hou, Zengguang1; Kasabov, Nikola2; Hu, Jin1; Peng, Liang1; Wang, Weiqun1
2015-07
Conference Name2015 International Joint Conference on Neural Networks (IJCNN)
Source PublicationBiomedical applications
Conference DateJuly 12-17, 2015
Conference PlaceIreland
Abstract
    sEMG (surface electromyography) signals have been used as human-machine interface to control robots or prostheses in recent years. sEMG-based torque estimation is a widely research methodology to obtain human motion intention. Most researches focus on improving the accuracy of sEMG-torque models, which often makes them complicated and confined in the laboratory research. However, an accurate estimation of muscle torque could be unnecessary to perform the robot-assisted rehabilitation training.
    This paper proposes a practical method to estimate the net muscle torques of lower limbs using sEMG, which can be used to implement a real-time coordinated active training with iLeg-a horizontal exoskeleton for lower limb rehabilitation developed at our laboratory. Two three-layer back propagation (BP) neural networks are built to estimate the net muscle torques at hip and knee joints respectively. Experimental results show that the well-trained neural networks estimate the user's motion intention in real-time, and can assist the user to perform an active training with iLeg.
KeywordEmg Torque Estimation Rehabilitation Robot
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12805
Collection复杂系统管理与控制国家重点实验室_先进机器人
Corresponding AuthorHou, Zengguang
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.Knowledge Engineering and Discovery Research Institute, Auckland University of Technology
Recommended Citation
GB/T 7714
Peng, Long,Hou, Zengguang,Kasabov, Nikola,et al. sEMG-Based Torque Estimation for Robot-Assisted Lower Limb Rehabilitation[C],2015.
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