3D-Sitpose: Millimeter Wave Radar-Based Human Sitting Posture Estimation

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Abstract
Sitting posture is closely related to our health. Poor sitting posture can cause various diseases and jeopardize our health. Among the current methods for detecting sitting posture, computer vision solutions suffer from privacy leakage and wearable sensor solutions suffer from inconvenience and cost of wearing. In this study, we introduce 3D-Sitpose, which leverages millimeter-wave radar to detect human sitting posture. 3D-Sitpose utilizes wireless signal transmission for non-contact detection, ensuring privacy protection and cost reduction. Firstly, we analyze the impact of variations in human sitting posture on millimeter-wave radar signals, and design sophisticated signal processing methods to refine the collected radar data, yielding clearer point cloud information for volunteers in various sitting postures. Secondly, we develop a two-channel neural network to extract fine-grained features related to volunteers from the point cloud data. Finally, we obtain coordinates for 25 human skeletal points. 3D-Sitpose can instruct users to maintain correct sitting posture based on a set of six key angles. We recruit 20 volunteers from our institute to conduct comprehensive evaluations of 3D-Sitpose. Experiments are conducted in two indoor environments to estimate sitting posture. The results reveal the mean Euclidean distance error for all skeletal point locations is 6.65 cm. This demonstrates that our method is able to estimate various sitting changes in volunteers.

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