Sleep Position Detection for Closed-Loop Treatment of Sleep-Related Breathing Disorders
- Resource Type
- Conference
- Authors
- Breuss, A.; Vonau, N.; Ungricht, C.; Schwarz, E.; Irion, M.; Bradicich, M.; Grewe, F. A.; Liechti, S.; Thiel, S.; Kohler, M.; Riener, R.; Wilhelm, E.
- Source
- 2022 International Conference on Rehabilitation Robotics (ICORR) Rehabilitation Robotics (ICORR), 2022 International Conference on. :1-6 Jul, 2022
- Subject
- Bioengineering
Computing and Processing
Robotics and Control Systems
Pressure sensors
Medical treatment
Robot sensing systems
Assistive robots
Sleep apnea
Robustness
Real-time systems
- Language
- ISSN
- 1945-7901
Reliable detection of sleep positions is essential for the development of technical aids for patients with position-dependent sleep-related breathing disorders. We compare personalized and generalizable sleeping position classifiers using unobtrusive eight-channel pressure-sensing mats. Data of six male patients with confirmed position-dependent sleep apnea was recorded during three subsequent nights. Personalized position classifiers trained using leave-one-night-out cross-validation on average reached an F1-score of 61.3% for supine/non-supine and an F1-score of 46.2% for supine/lateral-left/lateral-right classification. The generalizable classifiers reached average F1-scores of 62.1% and 49.1% for supine/non-supine and supine/lateral-left/lateral-right classification, respectively. In-bed presence (“bed occupancy”) could be detected with an average F1-score of 98.1%. This work shows that personalized sleep-position classifiers trained with data from two nights have comparable performance to classifiers trained with large interpatient datasets. Simple eight-channel sensor mattresses can be used to accurately detect in-bed presence required for closed-loop systems but their use to classify sleep-positions is limited.