Human Activity Recognition based on Transformer in Smart Home

With the advancement of artificial intelligence, smart home has attracted much attention from scholars. Human Activity Recognition (HAR) is a crucial foundation for various applications in smart home. In this paper, to improve the accuracy of HAR and promote the development of applications and services in smart home, we propose a Transformer-based approach that integrates multiple sensor sequence inputs for HAR. We integrate sequence features, collect contextual information, and employ Transformer to recognize various activities for the CASAS Aruba dataset that uses environmental sensors. The validation results on real-world dataset demonstrate its effectiveness compared to traditional machine learning and deep learning methods.