arXiv:2601.04181v2 Announce Type: replace Abstract: Reliable long-term decoding of gestures from surface electromyography (EMG) is hindered by signal drift caused by electrode displacement, muscle fatigue, and/or posture changes. Although modern models achieve high intra-session accuracy, their performance often degrades substantially across recording sessions. Existing approaches to mitigate this problem typically rely on large training datasets or computationally intensive pipelines that are unsuitable for energy-efficient wearable devices. We propose a lightweight test-time adaptation frame
The proliferation of wearable AI devices and the increasing demand for seamless human-computer interaction necessitate more robust and adaptive sensor technologies that can perform reliably outside of controlled lab environments.
This breakthrough addresses a critical bottleneck in the real-world application of EMG-based gesture recognition, enabling more reliable and energy-efficient control interfaces for everyday use.
The ability to achieve reliable, long-term EMG decoding with lightweight, test-time adaptation lowers the barrier for widespread adoption of gesture control in resource-constrained edge devices.
- · Wearable device manufacturers
- · Human-computer interface developers
- · AI hardware companies
- · Healthcare technology developers
- · Companies reliant solely on large, static training datasets
- · Developers of computationally heavy adaptation models
- · Users experiencing frequent recalibration of EMG devices
More accurate and persistent gesture control becomes feasible for a wider range of applications and users.
This improved reliability could accelerate the development and commercialization of advanced prosthetic limbs and assistive technologies.
The reduced computational demands for adaptation could lead to longer battery life and smaller form factors for devices, expanding the market drastically.
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Read at arXiv cs.LG