Transferability of a Sensing Mattress for Posture Classification from Research into Clinics.

Gnarra, Oriella; Breuss, Alexander; Rossi, Lorenzo; Fujs, Manuel; Knobel, Samuel E. J.; Warncke, Jan; Gerber, Stephan M.; Bassetti, Claudio L. A.; Riener, Robert; Nef, Tobias; Schmidt, Markus H. (2023). Transferability of a Sensing Mattress for Posture Classification from Research into Clinics. IEEE International Conference on Rehabilitation Robotics (ICORR), 2023, pp. 1-6. IEEE 10.1109/ICORR58425.2023.10304684

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Sleep is crucial in rehabilitation processes, promoting neural plasticity and immune functions. Nocturnal body postures can indicate sleep quality and frequent repositioning is required to prevent bedsores for bedridden patients after a stroke or spinal cord injury. Polysomnography (PSG) is considered the gold standard for sleep assessment. Unobtrusive methods for classifying sleep body postures have been presented with similar accuracy to PSG, but most evaluations have been done in research lab environments. To investigate the challenges in the usability of a previously validated device in a clinical setting, we recorded the sleep posture of 17 patients with a sensorized mattress. Ground-truth labels were collected automatically from a PSG device. In addition, we manually labeled the body postures using video data. This allowed us also to evaluate the quality of the PSG labels. We trained neural networks based on the VGG-3 architecture to classify lying postures and used a self-label correction method to account for noisy labels in the training data. The models trained with the video labels achieved a higher classification accuracy than those trained with the PSG labels (0.79 vs. 0.68). The self-label correction could further increase the models' scores based on video and PSG labels to 0.80 and 0.70, respectively. Unobtrusive sensors validated in clinics can, therefore, potentially improve the quality of care for bedridden patients and advance the field of rehabilitation.

Item Type:	Journal Article (Original Article)
Division/Institute:	10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Gerontechnology and Rehabilitation 04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Neurology 10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
UniBE Contributor:	Gnarra, Oriella, Knobel, Samuel Elia Johannes, Warncke, Jan, Gerber, Stephan Moreno, Bassetti, Claudio L.A., Nef, Tobias, Schmidt, Markus Helmut
Subjects:	600 Technology > 610 Medicine & health 500 Science > 570 Life sciences; biology
ISSN:	1945-7901
Publisher:	IEEE
Language:	English
Submitter:	Chantal Kottler
Date Deposited:	22 Dec 2023 18:56
Last Modified:	22 Dec 2023 18:56
Publisher DOI:	10.1109/ICORR58425.2023.10304684
PubMed ID:	37941201
URI:	https://boris.unibe.ch/id/eprint/190681