mmWave Radar for Sit-to-Stand Analysis: A Comparative Study With Wearables and Kinect

Abstract

This study investigates a novel approach for analyzing Sit-to-Stand (STS) movements using millimeter-wave (mmWave) radar technology, aiming to develop a non-contact, privacy-preserving, and all-day operational solution for healthcare applications. A 60 GHz 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. The extracted features were compared with those obtained from Kinect and wearable sensors. While Kinect provided a reference for motion capture, we acknowledge its limitations compared to the gold-standard VICON system, which is planned for future validation. The results demonstrated that mmWave radar effectively captures general motion patterns and large joint movements (e.g., trunk), though challenges remain for more fine-grained motion analysis. This study highlights the unique advantages and limitations of mmWave radar and other sensors, emphasizing the potential of integrated sensor technologies to enhance the accuracy and reliability of motion analysis in clinical and biomedical research. Future work will expand the scope to more complex movements and incorporate high-precision motion capture systems to further validate the findings.