Cavallaro Ettore, Micera Silvestro, Dario Paolo, Jensen Winnie, Sinkjaer Thomas
ARTS Lab, Scuola Superiore Sant'Anna, 56127 Pisa, Italy.
IEEE Trans Biomed Eng. 2003 Sep;50(9):1063-73. doi: 10.1109/TBME.2003.816075.
In the recent past, many efforts have been carried out in order to evaluate the feasibility of implementing closed-loop controlled neuroprostheses based on the processing of sensory electroneurographic (ENG) signals. The success of these techniques mostly relies on the development of processing algorithms capable of extracting the necessary kinematic information from these signals. Soft-computing algorithms can be very useful when dealing with the complexity of the neuromuscular system because of their generalization ability and model-free structure. In this paper, these techniques were used to extract angular position information from the ENG signals recorded from muscle afferents in animal model using cuff electrodes. Specifically, a genetic algorithm-based dynamic nonsingleton fuzzy logic system (named GA-DNSFLS) was developed and tested on different types of angular trajectories (characterized by small or large angular excursions). In particular, two different Takagi-Sugeno-Kang (TSK)-like structures were used in the consequent part of the neuro-fuzzy model in order to verify which one could improve the generalization abilities (intrasubject and intersubject). The results showed that the GA-DNSFLS was able to reconstruct the trajectories giving interesting results in terms of correlation between the actual and the predicted trajectories for small excursion movements during intrasubject and intersubject tests. Particularly, one of the TSK models showed better results in terms of intersubject generalization. The simulations conducted with the large excursion movements led in some cases to interesting results but further experiments are necessary in order to analyze this point more in deep.
最近，人们进行了许多努力来评估基于感觉神经电图（ENG）信号处理来实现闭环控制神经假体的可行性。这些技术的成功大多依赖于能够从这些信号中提取必要运动学信息的处理算法的开发。由于软计算算法的泛化能力和无模型结构，在处理神经肌肉系统的复杂性时可能非常有用。在本文中，这些技术被用于从使用袖带电极在动物模型中记录的肌肉传入神经的ENG信号中提取角位置信息。具体而言，开发了一种基于遗传算法的动态非单点模糊逻辑系统（名为GA-DNSFLS），并在不同类型的角轨迹（以小角度或大角度偏移为特征）上进行了测试。特别是，在神经模糊模型的后件部分使用了两种不同的类似高木-关野-康（TSK）的结构，以验证哪一种结构可以提高泛化能力（个体内和个体间）。结果表明，GA-DNSFLS能够重建轨迹，在个体内和个体间测试中，对于小偏移运动，实际轨迹与预测轨迹之间的相关性方面给出了有趣的结果。特别是，其中一个TSK模型在个体间泛化方面显示出更好的结果。在大偏移运动下进行的模拟在某些情况下产生了有趣的结果，但需要进一步的实验以便更深入地分析这一点。
