RadarSleepNet: Sleep Pose Classification via PointNet++ and 5D Radar Point Clouds

Fusco A, Akkus M, Vysotskaya N, Hazra S, Servadei L, Maier A, Wille R (2023)

Publication Type: Conference contribution

Publication year: 2023

Publisher: IEEE

City/Town: New York City

Conference Proceedings Title: 2023 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON)

Event location: Ahmedabad IN

DOI: 10.1109/MAPCON58678.2023.10463831

Abstract

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.

Authors with CRIS profile

Nastassia Vysotskaya Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Infineon Technologies AG DE Germany (DE) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Fusco, A., Akkus, M., Vysotskaya, N., Hazra, S., Servadei, L., Maier, A., & Wille, R. (2023). RadarSleepNet: Sleep Pose Classification via PointNet++ and 5D Radar Point Clouds. In 2023 IEEE Microwaves, Antennas, and Propagation Conference (MAPCON). Ahmedabad, IN: New York City: IEEE.

MLA:

Fusco, Alessandra, et al. "RadarSleepNet: Sleep Pose Classification via PointNet++ and 5D Radar Point Clouds." Proceedings of the 2023 IEEE Microwaves, Antennas, and Propagation Conference, Ahmedabad New York City: IEEE, 2023.

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