In this work, we propose a position and orientation-aware one-shot learning
framework for medical action recognition from signal data. The proposed
framework comprises two stages and each stage includes signal-level image
generation (SIG), cross-attention (CsA), dynamic time warping (DTW) modules and
the information fusion between the proposed privacy-preserved position and
orientation features. The proposed SIG method aims to transform the raw
skeleton data into privacy-preserved features for training. The CsA module is
developed to guide the network in reducing medical action recognition bias and
more focusing on important human body parts for each specific action, aimed at
addressing similar medical action related issues. Moreover, the DTW module is
employed to minimize temporal mismatching between instances and further improve
model performance. Furthermore, the proposed privacy-preserved
orientation-level features are utilized to assist the position-level features
in both of the two stages for enhancing medical action recognition performance.
Extensive experimental results on the widely-used and well-known NTU RGB+D 60,
NTU RGB+D 120, and PKU-MMD datasets all demonstrate the effectiveness of the
proposed method, which outperforms the other state-of-the-art methods with
general dataset partitioning by 2.7%, 6.2% and 4.1%, respectively.

本文提出了一种基于位置和方向感知的一次性学习框架，用于医学动作识别。通过信号数据转化为隐私保护特征进行训练，在模型的两个阶段中引入交叉注意力和动态时间规整等模块进行信息融合，筛选重要的人体部位并减少动作识别偏见，同时采用位置和方向特征相互支持以提高模型性能。实验证明了该方法在多个数据集上的有效性，并与先进方法相比获得了优异的结果。