RadarSleepNet: Sleep Pose Classification via PointNet++ and 5D Radar Point Clouds - Fusco, Alessandra; Akkus, Mervenur; Vysotskaya, Nastassia; Hazra, Souvik; Servadei, Lorenzo; Maier, Andreas; Wille, Robert (2023 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON), 2023)
Understanding sleep patterns and postures is critical for assessing overall well-being. However, traditional sleep analysis methods are often limited in their practicality due to invasive devices or complex configurations. In this study, we introduce RadarSleepNet, a non-intrusive 60 GHz Frequency-modulated Continuous Wave (FMCW) radar-based system for sleep posture monitoring that accurately infers sleep postures without compromising privacy or comfort, even in low-light conditions. Our system combines a SincNet classifier and a PointNet++ sleep pose estimation model, achieving remarkable class accuracy for each sleep posture: 98.43% for supine, 98.01% for side (chest facing radar), 97.22% for prone, and 95.72% for side (back facing radar). This demonstrates its effectiveness in accurately classifying sleep postures. This innovation offers significant potential in healthcare, providing insights into disease management and improving individual health understanding.<br
Keywords: Technological innovation; Privacy; Pose estimation; Radar detection; Medical services; Feature extraction; Sleep apnea; FMCW radar; sleep monitoring; contactless; deep learning; SincNet; PointNet+