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.Comment: Ten-page manuscript including references and ten figure
