提出RE-TCN模型，提升居家养老中动作识别的准确性与抗干扰能力。
Action Recognition in Real-World Ambient Assisted Living Environment
- 引入自适应时间加权与深度可分离卷积，增强模型鲁棒性与效率
- 在4个数据集上优于现有方法，对噪声和遮挡更具鲁棒性
- 适合需要实时、稳定动作识别的智慧养老场景
老龄化社会背景下，老年人更倾向于居家养老，需通过环境感知技术保障安全。居家养老（AAL）系统依赖动作识别来监测跌倒、行动力下降等异常行为。然而，真实场景中的遮挡、噪声和实时性要求使动作识别面临挑战。本文提出鲁棒高效的时间卷积网络RE-TCN，包含自适应时间加权（ATW）、深度可分离卷积（DSC）和数据增强技术，兼顾精度、抗噪能力和计算效率。在NTU RGB+D 60、Northwestern-UCLA、SHREC'17和DHG-14/28四个基准数据集上验证，性能优于现有模型。代码已开源。
原文摘要 · Abstract (English)
The growing ageing population and their preference to maintain independence by living in their own homes require proactive strategies to ensure safety and support. Ambient Assisted Living (AAL) technologies have emerged to facilitate ageing in place by offering continuous monitoring and assistance within the home. Within AAL technologies, action recognition plays a crucial role in interpreting human activities and detecting incidents like falls, mobility decline, or unusual behaviours that may signal worsening health conditions. However, action recognition in practical AAL applications presents challenges, including occlusions, noisy data, and the need for real-time performance. While advancements have been made in accuracy, robustness to noise, and computation efficiency, achieving a balance among them all remains a challenge. To address this challenge, this paper introduces the Robust and Efficient Temporal Convolution network (RE-TCN), which comprises three main elements: Adaptive Temporal Weighting (ATW), Depthwise Separable Convolutions (DSC), and data augmentation techniques. These elements aim to enhance the model's accuracy, robustness against noise and occlusion, and computational efficiency within real-world AAL contexts. RE-TCN outperforms existing models in terms of accuracy, noise and occlusion robustness, and has been validated on four benchmark datasets: NTU RGB+D 60, Northwestern-UCLA, SHREC'17, and DHG-14/28. The code is publicly available at: https://github.com/Gbouna/RE-TCN
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