用于肌电假肢手控制的表面肌电双线性模型分析  

Analysis of the sEMG bilinear model for the control of hand prothesis

作　　者：王涛[1] 侯文生[1,2] 吴小鹰[1,2] 万小萍[1,2] 郑小林[1,2] 

机构地区：[1]重庆大学生物流变科学与技术教育部重点实验室,重庆400044 [2]重庆市医疗电子工程技术研究中心,重庆400044

出　　处：《仪器仪表学报》2014年第8期1907-1913,共7页Chinese Journal of Scientific Instrument

基　　金：国家科技支撑计划(2012BAI16B02;2011BAI14B04);国家自然科学基金项目(30970758;3127106);重庆市科技(CSTC2010BB5071)资助项目

摘　　要：表面肌电信号(sEMG)被广泛应用于假肢控制,但是由于解剖组织、生理状态等因素使肌电信号表现出较大的个体差异,传统的参数归一化方法需要大样本的训练构建适合用户的肌电控制模型,新近发展起来的一种基于动作因素和个体因素的双线性模型为肌电假肢控制提供了新的思路。利用指总伸肌的综合强度和肌电活动的空间分布特征构建双线性模型,将指总伸肌肌电活动从神经-肌肉生理机制的角度分解为用户相关的个人因素矩阵和与动作相关的动作模式矩阵。为了验证该模型,设计了食指、中指、无名指在20%MVC、40%MVC、60%MVC 3个力量水平的单指按压实验,然后利用32通道柔性阵列电极所采集的6名志愿者的指总伸肌sEMG完成双线性模型的构建,在较少的样本训练下实现了对力量水平和手指模式的较好识别。结果表明双线性模型可用于简化肌电假肢接口的训练过程,对肌电假肢手力量和手指动作控制有较大的应用前景。Surface electromyography （sEMG）has been widely used as the control source in hand prosthesis;however,some factors, such as the individual anatomy property and physiological state can cause different sEMGs even in the same motor task.Traditional pa-rameter normalization is usually employed to construct a general sEMG model among individuals,which requires extensive sample train-ing.To cope with this problem,the bilinear model representing the activities with motion factors and individual factors provides a novel approach for myoelectric prosthetic control.The bilinear model is introduced to extract the neuromuscular characters of the extensor digi-torum’s activities.Firstly,the activities of the muscle are described with the multi-dimensional feature vectors including integrated sMEG amplitude and spatial distribution.Secondly,the vectors are decomposed into user-dependent matrices and motion-dependent ma-trices.To test the model,6 subjects were recruited for finger force-tracking tasks.The finger experiments on the index,middle and ring fingers with the force levels of 20%MVC,40%MVC and 60%MVC were designed.At the same time,the sEMGs were collected from the extensor digitorum with a 32-channel flexible electrode array.Then,the bilinear model was built to recognize the finger pattern and force levels under less sample training condition.The experiment results show that the proposed bilinear model can classify the finger pattern and force levels through only a few interactions,which suggests that the bilinear model can simplify the training procedures of my-oelectric prosthetic interface,and has good application prospect in the control of the finger motions and force levels of myoelectric pros-thetic hand.

关 键 词：表面肌电信号(sEMG) 双线性模型 手指动作识别 肌电假肢手 

分 类 号：TP911.7[自动化与计算机技术] TP241