Pattern recognition and characterization of upper limb neuromuscular dynamics during driver-vehicle interactions

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.