基于肌肉生理信号的操纵舒适性评价

从操纵者人机交互过程中肌肉生理信号角度出发，针对目前操纵舒适性评价方法的不足，提出一种基于肌肉生理信号特征参数，运用正则化RBF网络，对人机交互操纵舒适性进行评价的方法。以典型人机交互过程驾驶操作系统为例，进行实验与数据统计，通过正则化RBF网络对实验测得的操纵者肌肉生理信号特征参数和主观舒适度评分构成的样本进行学习和训练，建立操纵舒适性评价模型。实验结果验证了该方法的可行性和合理性。

Human-machine Interaction Manipulation Comfort Evaluations Based on Muscle Physiological Signals

Aiming at drawbacks of handling comfort evaluation methods, a method of man-machine interaction to evaluate handling comfort was put forward herein, which was based on characteristic parameters of muscle physiological signals. Using driving system as a typical example of man-machine interactions, experimental and data statistics were carried out. Through the regularization RBF network, the experimental samples of subjective comfort ratings and characteristic parameters of manipulator muscle physiological signals measured were studied and trained. Finally evaluation model of the handling comfort was established. Computational and experimental results demonstrate feasibility and applicability of the method.