首个面向智能家居活动识别的可解释图神经网络，提升准确率并提供直观解释。
GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes
- 用图神经网络建模传感器数据间的时空关系，支持可解释性推理。
- 在两个公开数据集上识别率略升，解释效果优于现有最先进方法。
- 适合需要透明决策过程的医疗健康场景，如老人居家监护。
基于传感器的智能家居人体活动识别（HAR）在医疗健康等领域至关重要。现有方法多采用深度学习模型，虽有效但决策过程不透明。近年来，可解释人工智能（XAI）方法被用于提供直观解释，但大多基于传统深度模型如CNN或RNN。图神经网络（GNN）在传感器驱动的HAR中表现优异，但现有方法未考虑可解释性。本文提出首个专为智能家居HAR设计的可解释图神经网络。实验在两个公开数据集上显示，该方法在保持识别率略升的同时，解释能力显著优于现有最先进方法。
原文摘要 · Abstract (English)
Sensor-based Human Activity Recognition (HAR) in smart home environments is crucial for several applications, especially in the healthcare domain. The majority of the existing approaches leverage deep learning models. While these approaches are effective, the rationale behind their outputs is opaque. Recently, eXplainable Artificial Intelligence (XAI) approaches emerged to provide intuitive explanations to the output of HAR models. To the best of our knowledge, these approaches leverage classic deep models like CNNs or RNNs. Recently, Graph Neural Networks (GNNs) proved to be effective for sensor-based HAR. However, existing approaches are not designed with explainability in mind. In this work, we propose the first explainable Graph Neural Network explicitly designed for smart home HAR. Our results on two public datasets show that this approach provides better explanations than state-of-the-art methods while also slightly improving the recognition rate.
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