Eye-CU: Sleep Pose Classification for Healthcare using Multimodal Multiview Data
This addresses the need for reliable automated monitoring in healthcare to reduce manual workload, though it appears incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of automated sleep pose classification for bed-ridden patients in ICUs by introducing a new method (cc-LS) and system (Eye-CU), achieving performance improvements of 13% in poor illumination and 70% in poor illumination with occlusions compared to existing techniques.
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 Least-Squares (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.