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 R2 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.

中文翻译：

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基于动子素放电活动的手指关节角度估计

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，连续且可靠地估计小（例如，手指）关节角度仍然是一个挑战。这项研究的目的是连续使用人口运动神经元射击活动来估计手指关节角度。多指表面肌电图（sEMG）信号是从指趾伸肌得到的，而受试者则以两种不同的速度进行了单独的手指振动伸展运动。首先根据EMG信号对手指的单个运动进行分类。通过高密度EMG分解提取单个电机的放电正时，然后将其合并为一个复合放电列。总体运动单位触发事件的触发频率用于表示对运动单位池的递减神经驱动。然后执行二阶多项式回归，以使用基于神经元放电的派生神经驱动来预测测得的掌指伸角度。我们的结果表明，基于多通道肌电图，可以将单个手指伸展运动分类为> 96％的精度。单个手指的伸直角度可以通过派生的R2值> 0.8的神经驱动来连续预测。基于神经驱动的方法的性能优于基于常规EMG幅度的方法，尤其是在快速运动期间。这些发现表明，基于神经驱动的界面是可靠预测单个手指运动学的一种有前途的方法。