In this work, pattern recognition and characterization of the neuromuscular dynamics of driver upper limb during naturalistic driving were studied. During the human-in-the-loop experiments, two steering tasks, namely, the passive and active steering tasks, were instructed to be completed by the subjects. Furthermore, subjects manipulated the steering wheel with two distinct postures and six different hand positions. The neuromuscular dynamics of subjects' upper limb were measured using electromyogram signals, and the behavioral data, including the steering torque and steering angle, were also collected. Based on the experimental data, patterns of muscle activities during naturalistic driving were investigated. The correlations, amplitudes, and responsiveness of the electromyogram signals, as well as the smoothness and regularity of the steering torque were discussed. The results reveal the mechanisms of neuromuscular dynamics of driver upper limb and provide a theoretical foundation for the design of the future human-machine interface for automated vehicles.

在这项工作中，研究了自然驾驶过程中驾驶员上肢神经肌肉动力学的模式识别和表征。在人体在环实验中，受试者指示两个转向任务，即被动和主动转向任务完成。此外，受试者以两种不同的姿势和六个不同的手部位置操纵了方向盘。使用肌电图信号测量受试者上肢的神经肌肉动力学，并收集包括转向扭矩和转向角在内的行为数据。基于实验数据，研究了自然驾驶过程中的肌肉活动模式。肌电信号的相关性，幅度和响应度，讨论了转向转矩的平稳性和规律性。结果揭示了驾驶员上肢神经肌肉动力学的机制，并为未来的自动车辆人机界面设计提供了理论基础。