Glove2UAV: A Wearable IMU-Based Glove for Intuitive Control of UAV

2026-01-22

AI Summary

This work proposes a wearable glove system based on inertial measurement units (IMUs) to address the limitations of conventional drone remote control, which lacks intuitiveness, struggles to support fine-grained gesture interaction, and offers no real-time warning during high-speed flight. The system maps hand gestures to flight control primitives through lightweight gesture recognition and integrates vibrotactile feedback to provide timely overspeed alerts. Median filtering is employed to suppress sensor noise, while the Madgwick algorithm enables real-time orientation estimation, ensuring both control stability and responsiveness. Experimental results demonstrate that the approach achieves low-latency gesture response, high-precision command execution, and prompt tactile warnings in both simulated and real-world flight scenarios, significantly enhancing the naturalness, safety, and efficiency of drone interaction.

Technology Category

• Intelligent Robots: State Estimation
• Computer Vision: Biometrics, Face, Gesture & Pose
• Application Domains: Mobility, Driving & Flight

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

This paper presents Glove2UAV, a wearable IMU-glove interface for intuitive UAV control through hand and finger gestures, augmented with vibrotactile warnings for exceeding predefined speed thresholds. To promote safer and more predictable interaction in dynamic flight, Glove2UAV is designed as a lightweight and easily deployable wearable interface intended for real-time operation. Glove2UAV streams inertial measurements in real time and estimates palm and finger orientations using a compact processing pipeline that combines median-based outlier suppression with Madgwick-based orientation estimation. The resulting motion estimations are mapped to a small set of control primitives for directional flight (forward/backward and lateral motion) and, when supported by the platform, to object-interaction commands. Vibrotactile feedback is triggered when flight speed exceeds predefined threshold values, providing an additional alert channel during operation. We validate real-time feasibility by synchronizing glove signals with UAV telemetry in both simulation and real-world flights. The results show fast gesture-based command execution, stable coupling between gesture dynamics and platform motion, correct operation of the core command set in our trials, and timely delivery of vibratile warning cues.

Problem

• UAV control
• wearable interface
• gesture recognition
• vibrotactile feedback
• real-time interaction

Innovation

• wearable IMU glove
• intuitive UAV control
• vibrotactile feedback
• real-time orientation estimation
• gesture-to-flight mapping