TorsoTrack: Fine-Grained Nonintrusive Torso Inclination Assessment Using mmwave Radar

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Abstract
Proper sitting posture is essential for health, as maintaining appropriate torso inclination improves comfort and reduces the risk of cervical and spinal disorders. Despite extensive research on posture detection, fine-grained variations in torso inclination have received limited attention. We propose TorsoTrack, a nonintrusive, device-free system for monitoring torso inclination. TorsoTrack leverages wireless respiratory signals to capture posture changes, applies signal restoration to correct abnormal segments, and extracts key features for precise angle estimation. A multilayer feature extraction network models the relationship between wireless signals and torso inclination, emphasizing critical posture features. To enhance adaptability to new users, TorsoTrack integrates a Reptile-PG meta-learning strategy, improving cross-individual prediction accuracy. Evaluations on 25 participants show that TorsoTrack reliably identifies significant features corresponding to different torso angles, achieving an average $R^{2}$ of 0.9822, RMSE of 1.6733, and MAE of 0.9977. For previously unseen individuals, the system maintains an average $R^{2}$ of 0.9033, demonstrating its effectiveness in precise torso inclination assessment.
Funding Information
  • Wuhan Textile University Foundation “Research and Effectiveness Evaluation of Parkinson’s Disease Motion Behavior Recognition Technology Using Fabric Based Flexible Fiber Optic Sensing” (2025257)

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