RFiDAR: Contactless RFID and Radar Data Fusion for Enhanced Human Activity Recognition

Human activity recognition (HAR) in indoor environments is essential for healthcare, elder care, and assisted living applications, especially in complex scenarios involving non-line-of-sight (NLoS) and long-range conditions. Traditional single-sensor HAR systems often struggle with accuracy and reliability in such environments. This study introduces the RFiDAR system, a novel fusion approach that combines radio frequency identification (RFID) and Radar technologies with an LSTM-variational autoencoder (LSTM-VAE) model to enhance HAR accuracy and reliability. The RFiDAR system applied data-level, feature-level, and decision-level fusion techniques to integrate temporal patterns from RFID and Radar data, facilitating the recognition of five distinct activities at varying distances. Results indicate that fusion methods, particularly feature-level fusion, significantly improve classification accuracy. For instance, feature-level fusion achieves up to 98.8% accuracy at 2 meters and 97.9% at 3 meters, outperforming single-sensor models by 5.3% and 6.2%, respectively. The RFiDAR system demonstrates superior performance in complex scenarios, offering reliable and cost-effective solutions for autonomous, long-term monitoring. The proposed approach has potential applications in healthcare and accessible IoT environments and demonstrates the innovative impact of multi-sensor fusion in developing safe, flexible, and inclusive technology.