Video monitoring over anti-decubitus protocol execution with a deep neural network to prevent pressure ulcer.

Abstract

Video monitoring of the patient position in the intensive care units is complicated by the obstacles covering the patient body. Conventional posture detection algorithms do not work in this case. A reformulation of the posture detection problem for the case as an object detection/image classification problem and the use of recent deep learning techniques allowed us to achieve 94.5% accuracy on a pre-clinical test classifying 4 postures using imagery from an off-the-shelf camera and edge processing, which is a 60% improvement over the result previously known in literature. This in turn allowed us to build a ready for the clinical trials system based on inexpensive off-the-shelf cameras.Clinical Relevance - A cheap and practical system of automatic video monitoring of bedridden patients allows to minimize the risks of pressure ulcer in ICU.