Wearable Human Drone Interface: Gesture-Based Drone Control and Vibrotactile Feedback

This paper presents a human-drone interaction system that integrates a hand-gesture controller with a wearable vibrotactile feedback device. The gesture controller uses an inertial measurement unit (IMU) and a gated recurrent unit (GRU) neural network classifier with an error-correcting output code (ECOC) scheme to recognize user commands with 95.65% accuracy. Mapped gestures correspond to the drone's movements. For vibrotactile feedback, we developed a belt with a 3 × 12 array of coin-type vibration motors that encodes the drone's relative azimuth, altitude, and distance via activation position and vibration intensity. In a user study with 12 participants, participants achieved accuracy of 82.75% in identifying the indicated drone position using the tactile device, with a mean response time of 3.1 seconds. The results demonstrate that combining intuitive gesture inputs with vibrotactile feedback can significantly enhance teleoperation and situational awareness in unmanned aerial vehicle control.