Evaluating a Human/Machine Interface with Redundant Motor Modalities for Trajectory-Tracking

Abstract

In human/machine interfaces (HMI), humans can interact with dynamic machines through a variety of sensory and motor modalities. Redundant motor modalities are known to have advantages in both human sensorimotor control and human-computer interaction: motor redundancy in sensorimotor control provides abundant solutions to achieve tasks; and incorporating diverse features from different modalities has improved the performance of gesture-, movement-, and brain-controlled computer interfaces. Our objective is to investigate whether redundant motor modalities enhance performance for a continuous trajectory-tracking task. We designed a multimodal human/machine interface with combined manual (joystick) and muscle (surface electromyography, sEMG) inputs and evaluated its closed-loop performance for tracking trajectories through second-order machine dynamics. In a human subjects experiment with 15 participants, we found that the multimodal interface outperformed the manual-only interface while performing comparably to the muscle-only interface; and that the multimodal interface enabled users to coordinate sensorimotor noise in individual modalities to improve performance. Multimodal human/machine interfaces could be beneficial in systems that require stability and robustness against perturbations such as motor rehabilitation and robotic manipulation.