Kavli Affiliate: Xiang Zhang
| First 5 Authors: Shuting Hu, Peggy Ackun, Xiang Zhang, Siyang Cao, Jennifer Barton
| Summary:
This study explores a novel approach for analyzing Sit-to-Stand (STS)
movements using millimeter-wave (mmWave) radar technology. The goal is to
develop a non-contact sensing, privacy-preserving, and all-day operational
method for healthcare applications, including fall risk assessment. We used a
60GHz mmWave radar system to collect radar point cloud data, capturing STS
motions from 45 participants. By employing a deep learning pose estimation
model, we learned the human skeleton from Kinect built-in body tracking and
applied Inverse Kinematics (IK) to calculate joint angles, segment STS motions,
and extract commonly used features in fall risk assessment. Radar extracted
features were then compared with those obtained from Kinect and wearable
sensors. The results demonstrated the effectiveness of mmWave radar in
capturing general motion patterns and large joint movements (e.g., trunk).
Additionally, the study highlights the advantages and disadvantages of
individual sensors and suggests the potential of integrated sensor technologies
to improve the accuracy and reliability of motion analysis in clinical and
biomedical research settings.
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