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.

翻译：基于传感器的人类活动识别在智能家居环境中对多种应用至关重要，尤其是在医疗保健领域。现有方法大多利用深度学习模型。虽然这些方法有效，但其输出背后的原理是不透明的。最近，可解释人工智能方法兴起，旨在为HAR模型的输出提供直观解释。据我们所知，这些方法利用了CNN或RNN等经典深度模型。近年来，图神经网络已被证明在基于传感器的HAR中具有良好效果。然而，现有方法在设计时并未考虑可解释性。在本工作中，我们提出了首个专为智能家居HAR设计的可解释图神经网络。我们在两个公开数据集上的实验结果表明，该方法不仅能提供优于现有先进技术的解释，同时还能略微提升识别率。