Improved human action recognition in a smart home environment setting

In the development of the next generation of smart homes and remote health monitoring system, human action analyzing algorithms are of vital importance. Among different sensor technologies, vision-based systems are superior in the sense that they can provide a non-intrusive interface between human occupants and the environment. It is almost impossible to build an efficient system for human action recognition without fine-tuning and evaluating its performance on a realistic dataset. An important challenge here is the absence of such a comprehensive dataset. To address this issue, we introduce a new dataset designed for human action recognition applications in a smart home environment. The performance of the existing human action recognition algorithms is tested using this dataset. In addition, we propose a heuristic approach based on error-correction codes to prioritized different actions in the learning process and improve the recognition accuracy for difficult actions up to 17%.