A Meta-Learning Framework for Few-Shot Dynamic Hand Gesture Recognition with Soft Temporal-aware Contrastive Learning

Few-shot learning improves data efficiency in surface electromyography (sEMG) gesture recognition, yet cross-subject and cross-posture generalization remains challenging due to nonstationary signals, electrode displacement, and time-varying within-gesture dynamics. We propose STC+Meta, a framework that couples temporally informed representation learning with efficient personalization. In pre-training, a soft temporal contrastive loss assigns adaptive weights by time lag to encourage temporal continuity while preserving short-lived discriminative transients, yielding coherent fused features from noninvasive sEMG and forearm accelerometer (ACC). In adaptation, implicit MAML provides data-efficient meta-updates that personalize the pretrained encoder to a new subject using only 10% of the subject-specific calibration data (∼28 s in our 7-class protocol), while matching the performance of subject-specific training that uses the full Session 1 data (∼19 min). On a self-collected multi-session dataset comprising eight upper-limb postures and seven gestures, the configuration that applies STC pre-training followed by multi-posture few-shot meta-adaptation achieves 97.63% accuracy when 10% of subject-specific data (∼28 sec) is used for calibration; in a 10-class task-transfer setting, it attains 95.75%. Relative to batch fine-tuning, STC+Meta shows faster early-stage convergence and lower inter-subject variability under limited calibration data. These results indicate that temporally aware contrastive pre-training combined with meta-learning enables calibration-efficient personalization for myoelectric gesture recognition under biased conditions.