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This paper introduces a montage-agnostic encoder for surface-EMG gesture decoding that maintains recognition accuracy across recording sessions without recalibration, and shows that feature-statistic alignment at test time improves adaptation on NinaPro DB6.
Introduces a montage-agnostic encoder for calibration-light cross-user gesture recognition from surface EMG, using shared weights and electrode coordinates to handle variable channel counts and reduce per-user calibration. It outperforms per-user baselines on some datasets and analyzes factors affecting cross-user transfer.
This paper introduces a graph neural network model for real-time hand gesture recognition using surface electromyography (sEMG) signals from the forearm. The method achieves 99% classification accuracy with an average processing time of 48ms on an M1 Pro CPU, outperforming existing state-of-the-art techniques.
Demonstrates how to use Claude Code and workflow to automatically generate a cyber-immortal cultivation style web mini-game that uses camera-based gesture recognition from a single prompt.