FRL Discrete Gestures: Hand Gesture Recognition from Surface Electromyography

This dataset comprises wrist-based surface electromyography (sEMG) recordings from 100 participants performing nine discrete hand gestures (four thumb swipes, four finger-to-thumb pinches, and one thumb tap) for gesture-based human-computer interaction. Recorded at 2000 Hz using a 16-channel dry electrode wristband, the dataset enables development and evaluation of generic machine learning models for real-time gesture classification without user calibration. The data supports research in neuromotor interfaces for AR/VR applications, accessibility, and alternative input modalities.