Human Activity Recognition Based on Data Fusion of FMCW Radar and Image

As we are getting deeper into the world of human-computer interaction, the human activity recognition technology becomes more and more important. Due to the complexity of the actual environment, we need a more reliable and powerful system that can recognize human activities in a variety of environments with high accuracy. Taking these factors into consideration, we propose a human activity recognition method based on data fusion of the FMCW radar and image, which uses the complementarity of the different data to improve the performance of the system. Apart from this, the domain adaptation is applied to reduce the data differences caused by the changes of environments and user habits in the practical application. Finally, we have implemented real-time applications based on the proposed algorithm on the edge computing platform. The experimental results show that the recognition accuracy of the fusion system can reach 98.7%, and the average running time of the real-time system is about 0.17s.