Estimation of Joint Kinematics and Fingertip Forces using Motoneuron Firing Activities: A Preliminary Report
Estimation of Joint Kinematics and Fingertip Forces using Motoneuron Firing Activities: A Preliminary Report
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DOI：
10.1109/ner49283.2021.9441433
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发表时间：
2021-05
期刊：
影响因子：
--
通讯作者：
Feng Xu;Yang Zheng;Xiaogang Hu
中科院分区：
文献类型：
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作者：
Feng Xu;Yang Zheng;Xiaogang Hu
失去个性化的手指运动影响日常活动的关键方面。有必要开发神经-机器接口技术，可以连续解码单个手指的运动。在这项初步研究中，我们评估了一种新的解码方法，该方法使用手指特定的运动神经元发射频率来估计关节运动学和指尖力。在食指或中指产生动态屈曲运动或等长屈曲力时获得高密度肌电图（EMG）信号。采用源分离方法提取单次试验的运动单元（MU）放电活动。一项单独的验证试验仅用于保留与特定手指相关的MUs。在第三次试验中，使用回归方法，使用手指特异性MU发射活动来估计单个手指关节角度和等距力。我们的研究结果表明，与传统的基于肌电振幅的方法相比，基于MU发射的方法对关节角度和力的预测误差更小。研究结果可以帮助开发直观的神经-机器接口技术，使机器人手的连续单指控制成为可能。此外，将先前获得的MU分离信息直接应用于新数据，因此可以在线提取MU发射活动以进行实时神经-机器交互。
A loss of individuated finger movement affects critical aspects of daily activities. There is a need to develop neural-machine interface techniques that can continuously decode single finger movements. In this preliminary study, we evaluated a novel decoding method that used finger-specific motoneuron firing frequency to estimate joint kinematics and fingertip forces. High-density electromyogram (EMG) signals were obtained during which index or middle fingers produced either dynamic flexion movements or isometric flexion forces. A source separation method was used to extract motor unit (MU) firing activities from a single trial. A separate validation trial was used to only retain the MUs associated with a particular finger. The finger-specific MU firing activities were then used to estimate individual finger joint angles and isometric forces in a third trial using a regression method. Our results showed that the MU firing based approach led to smaller prediction errors for both joint angles and forces compared with the conventional EMG amplitude based method. The outcomes can help develop intuitive neural-machine interface techniques that allow continuous single-finger level control of robotic hands. In addition, the previously obtained MU separation information was applied directly to new data, and it is therefore possible to enable online extraction of MU firing activities for real-time neural-machine interactions.