Eye-CU: Sleep Pose Classification for Healthcare using Multimodal Multiview Data.

Carlos Torres, Victor Fragoso, Scott D. Hammond, Jeffrey C. Fried, and B. S. Manjunath.

Abstract

Manual analysis of body poses of bed-ridden patients requires staff to continuously track and record patient poses. Two limitations in the dissemination of pose-related therapies are scarce human resources and unreliable automated systems. This work addresses these issues by introducing a new method and a new system for robust automated classification of sleep poses in an Intensive Care Unit (ICU) environment. The new method, coupled-constrained LeastSquares (cc-LS), uses multimodal and multiview (MM) data and finds the set of modality trust values that minimizes the difference between expected and estimated labels. The new system, Eye-CU, is an affordable multi-sensor modular system for unobtrusive data collection and analysis in healthcare. Experimental results indicate that the performance of cc-LS matches the performance of existing methods in ideal scenarios. This method outperforms the latest techniques in challenging scenarios by 13% for those with poor illumination and by 70% for those with both poor illumination and occlusions. Results also show that a reduced Eye-CU configuration can classify poses without pressure information with only a slight drop in its performance.

[Link] [BibTex]
Carlos Torres, Victor Fragoso, Scott D. Hammond, Jeffrey C. Fried, and B. S. Manjunath.,
IEEE Proceedings, pp. 1--9, Nov. 2016.