This study investigates a novel approach for analyzing Sit-to-Stand (STS) movements using millimeterwave (mmWave) radar technology, aiming to develop a noncontact, privacy-preserving, and all-day operational solution for healthcare applications. A 60GHz mmWave radar system was employed to collect radar point cloud data from 45 participants performing STS motions. Using a deep learning-based pose estimation model and Inverse Kinematics (IK), we calculated joint angles, segmented STS motions, and extracted clinically relevant features for fall risk assessment.
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