One-to-One Matching of Life Activities and Identity Information in Multitarget Scenarios Using Millimeter-Wave Radar and RFID

One-to-one correspondence between life activities (such as breathing, heartbeat, and trajectory) and identity information is essential for accurately reflecting an individual’s state. However, existing research often fails to achieve one-to-one matching of life activities and identity information when using wireless devices. In addition, millimeter-wave radar typically relies on pre-established datasets and neural networks to verify identity, which can be impractical in dynamic settings, such as meeting scenarios with frequent personnel changes. The accuracy of physiological signal detection via radio frequency identification (RFID) is also affected by the position and type of tags used. To address these limitations, this study proposes an algorithm for matching life activities and identity information called (M-LAII), which achieves static and dynamic information matching. It combines millimeter-wave radar and RFID for the first time, perceives physiological signal information and position information through millimeter-wave radar, perceives position information and identity information through RFID, and connects position information to match physiological signal (trajectory) information and identity information. The proposed algorithm offers several advantages: it can filter out nonhuman targets (removing environmental noise), maintain a consistent 100% accuracy in identity recognition, minimize positioning bias, decrease errors in physiological signal detection, and define unknown trajectories. M-LAII achieves one-to-one matching of identity, location, and physiological signal information, as well as one-to-one matching of identity and trajectory data, in multitarget scenarios. This approach eliminates the need for dataset creation and neural network construction and minimizes the impact of tag position and type on performance.