Does spontaneous motion lead to intuitive Body-Machine Interfaces? A fitness study of different body segments for wearable telerobotics
This work addresses the design of intuitive human-robot interfaces for telerobotics, but it is incremental as it critiques and suggests improvements to existing data-driven methods.
The study investigated whether using spontaneous motion patterns to select body segments for Body-Machine Interfaces (BoMIs) optimizes performance in telerobotics, finding that this approach can lead to sub-optimal results in a drone teleoperation task.
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.