FMCW Radar-based Sleep Posture Recognition with Transfer Learning and Range-Aware Dataset

• Published in: Smart World Congress (SWC), IEEE pp. 131 - 135
• Main Authors: Lu, Chang, Brahim, Walid, Wang, Haotian, Ma, Jianhua
• Format: Conference Proceeding
• Language: English
• Published: IEEE 02.12.2024
• Subjects: Accuracy; Data models; FMCW radar; Frequency modulation; Meters; Monitoring; Radar; Radar imaging; Radar signal processing; range-aware; sleep posture; Training; Transfer learning; VGG16
• ISSN: 2993-396X
• DOI: 10.1109/SWC62898.2024.00052

Sleep posture recognition is crucial for understanding sleep quality and diagnosing sleep-related disorders. In this paper, we propose a fine-tuned VGG16 model for classifying four fundamental sleep postures-Supine, Prone, Right side, and Left side-using data collected from Frequency Modulated Continuous Wave (FMCW) radar. Radar signals are processed into range-time images and fed into the deep-learning model for training and classification. We evaluate the impact of varying window sizes, radar distances (1.5, 1.7, and 1.9 meters), and subject independence on the model's performance. Our fine-tuned VGG16 model achieves an average accuracy of over 96% across different distances and demonstrates superior performance compared to a basic CNN. However, the model struggles to generalize to unseen subjects, indicating the need for further research to address subject-dependent challenges. This study highlights the potential of radar-based systems for contactless sleep monitoring and suggests future improvements to enhance model generalization.