Danilovich, I., Moshkin, V., Reimche, A., Tevelevich, M., & Mikhaylovskiy, N. (2021, November). Video monitoring over anti-decubitus protocol execution with a deep neural network to prevent pressure ulcer. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1384-1387). IEEE.

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.