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Simultaneous and Proportional Estimation of Finger Joint Angles from Surface EMG Signals during Mirrored Bilateral Movements

机译:镜像的双边运动过程中根据表面肌电信号同时和按比例估计手指关节角度

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This paper studies the continuous estimation of finger angles with state-space model to realize the simultaneous and proportional control of finger joint motions, where the test model of mirrored bilateral movements is first applied to decode the fingers' continuous motion. First a linear method was proposed for simultaneous and proportional estimation of 10 degrees of freedom (DOFs) finger joint angle with surface eletromyography (EMG). The state-space model was adopted to estimate one finger joint angle from 8 channel surface EMG signals recorded from contralateral limb mirrored bilateral movements. The average estimation performance of 8 DOFs of the hand except little finger can achieve 0.824(index R~2), while the index R~2 can achieve as high as 0.924 with the estimation thumb metacarpophalangeal joint (MCP). Although the estimation performance of the little finger was not good enough, the results demonstrate that state-space model is a feasible method to estimate the finger joint angles. Because the proposed method requires only the contralateral finger joint angles as the model identification reference, there is a great potential application for unilateral amputees in control of dexterous myoelectric prosthesis with multiple DOFs.
机译:本文利用状态空间模型研究了手指角度的连续估计,以实现手指关节动作的同步和比例控制,其中首先应用镜像的双边动作测试模型对手指的连续动作进行解码。首先提出了一种线性方法,用于通过表面电肌电图(EMG)同时按比例估计10个自由度(DOF)手指关节角度。采用状态空间模型从对侧肢体镜像的双侧运动记录的8个通道表面肌电信号中估计一个手指的关节角度。用拇指掌指关节(MCP)估计,除小指外,手的8个自由度的平均估计性能可以达到0.824(指标R〜2),而指标R〜2可以达到0.924。尽管小指的估计性能不够好,但结果表明状态空间模型是估计手指关节角度的可行方法。由于所提出的方法仅需要将对侧手指关节角作为模型识别参考,因此单侧截肢者在控制具有多个自由度的灵巧肌电假体中具有巨大的潜在应用。

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