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Human-Robot Interfaces (HRIs) represent a crucial component in teleroboticsystems. Body-Machine Interfaces (BoMIs) based on body motion can feel moreintuitive than standard HRIs for naive users as they leverage humans' naturalcontrol capability over their movements. Among the different methods used tomap human gestures into robot commands, data-driven approaches select a set ofbody segments and transform their motion into commands for the robot based onthe users' spontaneous motion patterns. Despite being a versatile and genericmethod, there is no scientific evidence that implementing an interface based onspontaneous motion maximizes its effectiveness. In this study, we compare a setof BoMIs based on different body segments to investigate this aspect. Weevaluate the interfaces in a teleoperation task of a fixed-wing drone andobserve users' performance and feedback. To this aim, we use a framework thatallows a user to control the drone with a single Inertial Measurement Unit(IMU) and without prior instructions. We show through a user study thatselecting the body segment for a BoMI based on spontaneous motion can lead tosub-optimal performance. Based on our findings, we suggest additional metricsbased on biomechanical and behavioral factors that might improve data-drivenmethods for the design of HRIs.

Does spontaneous motion lead to intuitive Body-Machine Interfaces? A fitness study of different body segments for wearable telerobotics