Lightweight Test-Time Adaptation for EMG-Based Gesture Recognition

TL;DR

This paper introduces a lightweight, real-time test-time adaptation framework for EMG-based gesture recognition that improves long-term accuracy and robustness with minimal computational overhead, suitable for wearable devices.

Contribution

It presents a novel, energy-efficient TTA framework using TCNs, causal batch normalization, GMM alignment with experience replay, and meta-learning for rapid calibration.

Findings

Significantly reduces inter-session accuracy gap.

Experience-replay updates improve stability with limited data.

Meta-learning achieves competitive performance with minimal data.

Abstract

Reliable long-term decoding of surface electromyography (EMG) is hindered by signal drift caused by electrode shifts, muscle fatigue, and posture changes. While state-of-the-art models achieve high intra-session accuracy, their performance often degrades sharply. Existing solutions typically demand large datasets or high-compute pipelines that are impractical for energy-efficient wearables. We propose a lightweight framework for Test-Time Adaptation (TTA) using a Temporal Convolutional Network (TCN) backbone. We introduce three deployment-ready strategies: (i) causal adaptive batch normalization for real-time statistical alignment; (ii) a Gaussian Mixture Model (GMM) alignment with experience replay to prevent forgetting; and (iii) meta-learning for rapid, few-shot calibration. Evaluated on the NinaPro DB6 multi-session dataset, our framework significantly bridges the inter-session accuracy gap with minimal overhead. Our results show that experience-replay updates yield superior stability under limited data, while meta-learning achieves competitive performance in one- and two-shot regimes using only a fraction of the data required by current benchmarks. This work establishes a path toward robust, "plug-and-play" myoelectric control for long-term prosthetic use.