Abstract
Micro-gestures are subtle and transient movements triggered by unconscious neural and emotional activities, holding great potential for human-computer interaction and clinical monitoring. However, their low amplitude, short duration, and strong inter-subject variability make existing deep models prone to degradation under low-sample, noisy, and cross-subject conditions. This paper presents an active inference-based framework for micro-gesture recognition, featuring Expected Free Energy (EFE)-guided temporal sampling and uncertainty-aware adaptive learning. The model actively selects the most discriminative temporal segments under EFE guidance, enabling dynamic observation and information gain maximization. Meanwhile, sample weighting driven by predictive uncertainty mitigates the effects of label noise and distribution shift. Experiments on the SMG dataset demonstrate the effectiveness of the proposed method, achieving consistent improvements across multiple mainstream backbones. Ablation studies confirm that both the EFE-guided observation and the adaptive learning mechanism are crucial to the performance gains. This work offers an interpretable and scalable paradigm for temporal behavior modeling under low-resource and noisy conditions, with broad applicability to wearable sensing, HCI, and clinical emotion monitoring.
Abstract (translated)
微手势是由于无意识的神经和情绪活动触发的细微且短暂的动作，它们在人机交互和临床监测方面具有巨大的潜力。然而，这些动作幅度小、持续时间短以及个体间的高度差异性使得现有的深度学习模型在样本量少、数据噪声大及跨不同用户的情况下容易退化。本文提出了一种基于主动推理框架的微手势识别方法，该方法结合了预期自由能（EFE）引导的时间采样和预测不确定性驱动的学习机制。通过EFE指导，模型能够主动选择最具判别力的时间片段，实现动态观察与信息增益的最大化。同时，由预测不确定性的样本权重调节减轻了标签噪声和分布变化的影响。 在SMG数据集上的实验表明，该方法在多个主流骨干网络上均取得了持续的性能提升。消融研究表明，EFE引导观测和自适应学习机制对于性能改进都是至关重要的因素。这项工作提供了一种可解释且可扩展的时间行为建模范式，在资源有限及噪声较大的条件下具有广泛的应用潜力，包括可穿戴传感、人机交互（HCI）以及临床情绪监测等领域。
URL
https://arxiv.org/abs/2603.07559
