Finger Joint Angle Estimation Based on Motoneuron Discharge Activities
Finger Joint Angle Estimation Based on Motoneuron Discharge Activities
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DOI：
10.1109/jbhi.2019.2926307
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发表时间：
2020-03-01
影响因子：
7.7
通讯作者：
Hu, Xiaogang
中科院分区：
文献类型：
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作者：
Dai, Chenyun;Hu, Xiaogang
关节运动学的估计在直观的人机交互中起着重要的作用。然而，对小的（例如，手指）关节角度仍然是一个挑战。本研究的目的是利用群体运动神经元放电活动连续估计指关节角度。多通道表面肌电图（sEMG）信号从伸趾总肌获得，而受试者进行个人手指振荡伸展运动在两个不同的速度。首先基于肌电信号对个体手指运动进行分类。通过高密度EMG分解提取单个运动单元的放电时间，然后将其合并为复合放电序列。群体运动单位放电事件的放电频率被用来代表运动单位池的下行神经驱动。然后进行二阶多项式回归，以预测所测得的掌指伸展角，使用基于神经元放电的衍生神经驱动。我们的研究结果表明，个人的手指伸展运动可以分类的准确率>96%的基础上多通道肌电图。神经驱动可以连续预测单个手指的伸展角度，R-2值>0.8。基于神经驱动的方法的性能是上级传统的肌电幅度为基础的方法，特别是在快速运动。这些研究结果表明，基于神经驱动的接口是一种很有前途的方法，可靠地预测个人手指运动学。
Estimation of joint kinematics plays an important role in intuitive human-machine interactions. However, continuous and reliable estimation of small (e.g., the finger) joint angles is still a challenge. The objective of this study was to continuously estimate finger joint angles using populational motoneuron firing activities. Multi-channel surface electromyogram (sEMG) signals were obtained from the extensor digitorum communis muscles, while the subjects performed individual finger oscillatory extension movements at two different speeds. The individual finger movement was first classified based on the EMG signals. The discharge timings of individual motor units were extracted through high-density EMG decomposition, and were then pooled as a composite discharge train. The firing frequency of the populational motor unit firing events was used to represent the descending neural drive to the motor unit pool. A second-order polynomial regression was then performed to predict the measured metacarpophalangeal extension angle using the derived neural drive based on the neuronal firings. Our results showed that individual finger extension movement can be classified with >96% accuracy based on multi-channel EMG. The extension angles of individual fingers can be predicted continuously by the derived neural drive with R-2 values >0.8. The performance of the neural-drive-based approach was superior to the conventional EMG-amplitude-based approach, especially during fast movements. These findings indicated that the neural-drive-based interface was a promising approach to reliably predict individual finger kinematics.