Evaluating a Human/Machine Interface with Redundant Motor Modalities for Trajectory-Tracking. Issue 41 (2022)

• Record Type: Journal Article
• Authors: Chou, Amber H.Y.; Yamagami, Momona; Burden, Samuel A.
• Is Part Of: IFAC-PapersOnLine. Volume 55:Issue 41(2022)
• Page Start: 125
• Page End: 130
• Publication Date: 2022
• Subjects: electromyography -- human/machine interface -- human subject experiment -- motor redundancy -- sensorimotor control; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2023.01.114
• Languages: English
• ISSNs: 2405-8963

Abstract

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 movement-, gesture-, 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 individual modalities to attenuate noise. Multimodal human/machine interfaces could be beneficial in systems that require stability and robustness against perturbations such as motor rehabilitation and robotic manipulation.