Concurrent Estimation of Finger Flexion and Extension Forces Using Motoneuron Discharge Information
Concurrent Estimation of Finger Flexion and Extension Forces Using Motoneuron Discharge Information
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DOI：
10.1109/tbme.2021.3056930
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发表时间：
2021-02
影响因子：
4.6
通讯作者：
Yang Zheng;Xiaogang Hu
中科院分区：
文献类型：
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作者：
Yang Zheng;Xiaogang Hu
目的：一个可靠的神经机器接口提供了控制先进的机器人手与高灵巧性的可能性。本研究的目的是开发一种解码方法来估计弯曲和伸展的力量，同时个人手指。研究方法：首先，通过表面肌电图（EMG）分解识别运动单元（MU）放电信息，并通过细化程序将MU进一步分类为不同的池，用于单个手指的屈曲和伸展。计算群体水平的MU击发率，然后通过二元线性回归模型（神经驱动法）估计个体手指力。传统的肌电幅值为基础的方法被用作比较。结果如下：我们的研究结果表明，神经驱动方法有一个显着更好的性能（较低的估计误差和较高的相关性）相比，传统的方法。结论：我们的方法提供了一个可靠的神经解码灵巧手指运动的方法。意义：我们的方法的进一步探索可以潜在地提供一个强大的神经机器接口的机器人手的直观控制。
Objective: A reliable neural-machine interface offers the possibility of controlling advanced robotic hands with high dexterity. The objective of this study was to develop a decoding method to estimate flexion and extension forces of individual fingers concurrently. Methods: First, motor unit (MU) firing information was identified through surface electromyogram (EMG) decomposition, and the MUs were further categorized into different pools for the flexion and extension of individual fingers via a refinement procedure. MU firing rate at the populational level was calculated, and the individual finger forces were then estimated via a bivariate linear regression model (neural-drive method). Conventional EMG amplitude-based method was used as a comparison. Results: Our results showed that the neural-drive method had a significantly better performance (lower estimation error and higher correlation) compared with the conventional method. Conclusion: Our approach provides a reliable neural decoding method for dexterous finger movements. Significance: Further exploration of our method can potentially provide a robust neural-machine interface for intuitive control of robotic hands.