Data-driven body-machine interface for the accurate control of drones.

The accurate teleoperation of robotic devices requires simple, yet intuitive and reliable control interfaces. However, current human-machine interfaces (HMIs) often fail to fulfill these characteristics, leading to systems requiring an intensive practice to reach a sufficient operation expertise. Here, we present a systematic methodology to identify the spontaneous gesture-based interaction strategies of naive individuals with a distant device, and to exploit this information to develop a data-driven body-machine interface (BoMI) to efficiently control this device.