Hand gestures can provide a natural means of human-computer interaction and
enable people who cannot speak to communicate efficiently. Existing hand
gesture recognition methods heavily depend on pre-defined gestures, however,
motor-impaired individuals require new gestures tailored to each individual's
gesture motion and style. Gesture samples collected from different persons have
distribution shifts due to their health conditions, the severity of the
disability, motion patterns of the arms, etc. In this paper, we introduce the
Latent Embedding Exploitation (LEE) mechanism in our replay-based Few-Shot
Continual Learning (FSCL) framework that significantly improves the performance
of fine-tuning a model for out-of-distribution data. Our method produces a
diversified latent feature space by leveraging a preserved latent embedding
known as \textit{gesture prior knowledge}, along with \textit{intra-gesture
divergence} derived from two additional embeddings. Thus, the model can capture
latent statistical structure in highly variable gestures with limited samples.
We conduct an experimental evaluation using the SmartWatch Gesture and the
Motion Gesture datasets. The proposed method results in an average test
accuracy of 57.0\%, 64.6\%, and 69.3\% by using one, three, and five samples
for six different gestures. Our method helps motor-impaired persons leverage
wearable devices, and their unique styles of movement can be learned and
applied in human-computer interaction and social communication.

本文介绍了一种基于回放的少样本持续学习框架中的潜在嵌入利用（LEE）机制，该框架显著提高了模型对分布不同的数据进行微调的性能。实验评估结果显示，该方法对于六种不同手势，使用一个、三个和五个样本时，平均测试准确率分别为 57.0％，64.6％和 69.3％。本方法有助于运动受限的人通过可穿戴设备进行人机交互和社交沟通，可以学习和应用他们独特的运动风格。