2020/11/15 by Matteo Macchini, Macchini, Matteo, Jan Frogg +5
Computer Science · Engineering · Psychology · #FOS: Computer and information sciences #Gaze Tracking and Assistive Technology #Human-Automation Interaction and Safety #Human-Computer Interaction (cs.HC) #Muscle activation and electromyography studies #Robotics (cs.RO) #cs.HC #cs.RO
paper · pdf · doi:10.48550/arxiv.2011.07591
arxiv created 2020/11/15 · openalex publication_date 2020/11/15 · arxiv updated 2020/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Human-Robot Interfaces (HRIs) represent a crucial component in telerobotic systems. Body-Machine Interfaces (BoMIs) based on body motion can feel more intuitive than standard HRIs for naive users as they leverage humans' natural control capability over their movements. Among the different methods used to map human gestures into robot commands, data-driven approaches select a set of body segments and transform their motion into commands for the robot based on the users' spontaneous motion patterns. Despite being a versatile and generic method, there is no scientific evidence that implementing an interface based on spontaneous motion maximizes its effectiveness. In this study, we compare a set of BoMIs based on different body segments to investigate this aspect. We evaluate the interfaces in a teleoperation task of a fixed-wing drone and observe users' performance and feedback. To this aim, we use a framework that allows a user to control the drone with a single Inertial Measurement Unit (IMU) and without prior instructions. We show through a user study that selecting the body segment for a BoMI based on spontaneous motion can lead to sub-optimal performance. Based on our findings, we suggest additional metrics based on biomechanical and behavioral factors that might improve data-driven methods for the design of HRIs.