目的分解单通道表面肌电信号(surface electromyography,sEMG),获得组成肌电信号的运动单元动作电位(motor unit action potential,MUAP)的波形及发放频率。方法首先对单通道的sEMG信号进行候选MUAP信号段检测,然后使用基于模糊K-均值...目的分解单通道表面肌电信号(surface electromyography,sEMG),获得组成肌电信号的运动单元动作电位(motor unit action potential,MUAP)的波形及发放频率。方法首先对单通道的sEMG信号进行候选MUAP信号段检测,然后使用基于模糊K-均值聚类的Gap Statistic聚类分析处理获取参与肌肉活动的运动单元发放的模板MUAP,最后使用广义互相关求时延的方法完成对低收缩水平的表面肌电信号的分解,并获得所有模板MUAP的发放频率。结果对模拟的单通道表面肌电信号进行分解,MUAP的准确识别率可达80%以上;并对真实采集的低收缩水平的等长收缩表面肌电信号进行分解,得到了MUAP波形和发放信息。结论本文提出的方法能够有效的对单通道表面肌电信号进行分解,具有较好的分解效果。展开更多
Grasping force estimation using surface Electromyography (sEMG) has been actively investigated as it can increase the manipulability and dexterity of prosthetic hands and robotic hands. Most of the current studies in ...Grasping force estimation using surface Electromyography (sEMG) has been actively investigated as it can increase the manipulability and dexterity of prosthetic hands and robotic hands. Most of the current studies in this area only focus on finding the relationship between sEMG signals and the grasping force without considering the arm posture. Therefore, regression models are not suitable to predict grasping force in various arm postures. In this paper, a method to predict the grasping force from sEMG signals and various grasping postures is developed. The proposed algorithm uses a tensor algebra to train a multi-factor model relevant to sEMG signals corresponding to various grasping forces and postures of the wrist and forearm in multiple dimensions. The multi-factor model is then decomposed into four independent factor spaces of the grasping force, sEMG signals, wrist posture, and forearm posture. Moreover, when a participant executes a new posture, new factors for the wrist and forearm are interpolated in the factor spaces. Thus, the grasping force with various postures can be predicted by combining these factors. The effectiveness of the proposed method is verified through experiments with ten healthy subjects, demonstrating the higher performance of proposed grasping force prediction method than the previous algorithm.展开更多
文摘Grasping force estimation using surface Electromyography (sEMG) has been actively investigated as it can increase the manipulability and dexterity of prosthetic hands and robotic hands. Most of the current studies in this area only focus on finding the relationship between sEMG signals and the grasping force without considering the arm posture. Therefore, regression models are not suitable to predict grasping force in various arm postures. In this paper, a method to predict the grasping force from sEMG signals and various grasping postures is developed. The proposed algorithm uses a tensor algebra to train a multi-factor model relevant to sEMG signals corresponding to various grasping forces and postures of the wrist and forearm in multiple dimensions. The multi-factor model is then decomposed into four independent factor spaces of the grasping force, sEMG signals, wrist posture, and forearm posture. Moreover, when a participant executes a new posture, new factors for the wrist and forearm are interpolated in the factor spaces. Thus, the grasping force with various postures can be predicted by combining these factors. The effectiveness of the proposed method is verified through experiments with ten healthy subjects, demonstrating the higher performance of proposed grasping force prediction method than the previous algorithm.
基金吉林省科技发展计划项目(20090350)吉林大学"985工程"工程仿生科技创新平台项目+5 种基金吉林大学博士研究生交叉学科科研资助计划项目(2011J009)资助高等院校博士专项科研基金(20100061110029)Supported by the Key Project of Science and Technology Development Plan for Jilin Province(20090350)the Jilin University "985 Project" Engineering Bionic Science and Technology Innovation PlatformDoctoral Interdisciplinary Scientific Research Projects Fund of Jilin University(2011J009)Chinese College Doctor Special Scientific Research Fund(20100061110029)
