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

TL;DR

This paper introduces Eye-CU, a multimodal, multiview system with a novel cc-LS method for robust sleep pose classification in ICUs, significantly improving accuracy in challenging conditions like poor lighting and occlusions.

Contribution

The paper presents a new multimodal, multiview system and a coupled-constrained Least-Squares method for improved sleep pose classification in healthcare environments.

Findings

cc-LS matches existing methods in ideal scenarios

Outperforms latest techniques by 13% under poor illumination

Achieves 70% improvement with occlusions and poor lighting

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 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.