IMRadar: Bidirectional Velocity Mamba for Contactless Human Behavior Sensing

In recent years, intelligent human behavior sensing based on channel state information (CSI) has garnered significant attention from researchers, serving as a pivotal application of contactless health monitoring. However, the feature extraction networks used in existing perception schemes have significant limitations in terms of global context perception, computational complexity, and only consider features in one direction. To address these issues, this article proposes a novel bidirectional velocity Mamba (BVMamba) model and constructs an intelligent behavior sensing system, named IMRadar. The system first analyzes the velocity information that better characterizes the human motion state from CSI data, and uses the BVMamba model to extract global deep behavioral features from both forward and reverse directions. The BVMamba model includes forward velocity Mamba block (FVMamba), reverse velocity Mamba block (RVMamba), and bidirectional velocity feature fusion block (FUBlock), which can comprehensively capture the dynamic characteristics of complex behaviors. Experiments have shown that IMRadar exhibits excellent recognition performance on both publicly available datasets (ARIL, Widar) and self-built dataset (IM-HAR), with accuracy rates exceeding 98% for all datasets, providing an efficient and robust solution for non-contact behavior perception technology.