GNN-XAR: A Graph Neural Network for Explainable Activity Recognition in Smart Homes

TL;DR

This paper introduces GNN-XAR, the first explainable graph neural network tailored for human activity recognition in smart homes, offering improved interpretability and slightly better accuracy over existing methods.

Contribution

The paper presents a novel explainable GNN model specifically designed for smart home activity recognition, addressing the lack of explainability in existing GNN-based HAR approaches.

Findings

Provides better explanations than state-of-the-art methods

Achieves slightly higher recognition accuracy

Validated on two public datasets

Abstract

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.