PGUDA: Pressure-Guided Unsupervised Domain Adaptation with Cross-Modal Knowledge Distillation for sEMG-Based Gesture Recognition
arXiv:2606.31349v1 Announce Type: cross Abstract: Surface electromyography (sEMG)-based gesture recognition has emerged as a promising technology for natural human-computer interaction. However, its practical deployment remains challenging due to severe performance degradation caused by feature distribution discrepancies across different subjects and recording sessions. Although domain adaptation (DA) techniques are commonly employed to mitigate such discrepancies, conventional methods often struggle to effectively aligning sEMG features, primarily due to their inherent stochasticity and the s
The increasing demand for natural human-computer interaction across various applications is driving the need for more robust gesture recognition technologies that can overcome real-world deployment challenges.
Improving sEMG-based gesture recognition through unsupervised domain adaptation can unlock more natural and reliable control interfaces, impacting fields from prosthetics to consumer electronics.
This research outlines a method to mitigate the performance degradation of sEMG systems when used by different individuals or in varying sessions, paving the way for more universally adaptable devices.
- · Human-computer interaction developers
- · Medical technology (prosthetics)
- · Consumer electronics
- · Robotics
- · Companies relying on calibration-intensive sEMG systems
- · Wearable tech with poor user adaptation
Enhanced reliability and user experience in sEMG-controlled devices and interfaces.
Broader adoption of sEMG technology due to reduced calibration effort and improved performance across diverse users.
Accelerated development of advanced prosthetic limbs and assistive technologies with more intuitive control schemes.
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Read at arXiv cs.AI