RFID-WiFi-Radar Fusion for Health Monitoring: A Cross-Modal Supervision Framework

Wireless sensing technology, with its advantages in privacy protection and high recognition accuracy, has demonstrated remarkable performance in complex behavioral monitoring environments. However, single-modal sensing technologies often fail to meet the demands of multitask complex behavior recognition. Although multimodal fusion can significantly enhance system performance, challenges such as data heterogeneity and uneven modal contributions severely limit fusion efficiency. To address these challenges, this article proposes an innovative multimodal data fusion framework called the multimodal temporal-topology feature fusion network (MTTFNet). By combining the strengths of a convolutional neural network (CNN) and transformer architectures, the framework introduces a temporal feature enhancement module (TFEM) and incorporates an adaptive weighting fusion mechanism alongside a cross-modal supervision strategy. This design facilitates efficient collaborative optimization of modal features, effectively mitigating the challenges posed by heterogeneity and contribution imbalance. Experimental results demonstrate that the recognition accuracy of the radio frequency identification (RFID) modality has increased to 70.81%, while the accuracy of multimodal fusion consistently exceeds 90%. Notably, the fusion of all three modalities achieves an accuracy of 96.83%, significantly outperforming existing algorithms. These findings validate the effectiveness of MTTFNet and provide a robust solution for complex behavior recognition tasks.