Sit-to-Stand Estimation and Person Identification Using Millimeter-Wave Radar in Casual Home Scenarios

Conventional radar-based sit-to-stand (STS) estimation methods typically require subjects to perform the movement at specific locations or follow predefined directions, which limits their applicability in real home environments and significantly restricts the natural freedom of user behaviors. Moreover, most existing approaches treat the human body as a whole, neglecting the micro-Doppler characteristics of local limbs. In this paper, we explore a millimeter-wave radar-based STS estimation and person identification method that allows users to perform the STS action casually in home scenarios, while dynamically capturing motion characteristics and extracting critical local limb micro-Doppler signatures for person identification. The core idea is to segment limb regions from the point cloud data, identify motion directions that exhibit high-quality micro-Doppler features, and estimate radial velocities of the corresponding local limbs. Finally, by utilizing the radial velocity of limbs in combination with Doppler features, person identification is performed using long short-term memory (LSTM) networks. Experimental results demonstrate that the proposed method effectively extracts limb-level radial velocities, with most intraclass correlation coefficients (ICCs) exceeding 0.9. Furthermore, by applying the extracted velocity features to person identification for eight individuals, we achieve an accuracy of 95.3%, highlighting its potential applications in home monitoring systems.