Wearable Prophylaxis Tool for AI-Driven Identification of Early Warning Patterns of Pressure Ulcers

Abstract

As today’s society ages, age-related diseases become more frequent. One very common but yet preventable disease is the development of pressure ulcers (PUs). PUs can occur if tissue is exposed to a long-lasting pressure load, e.g., lying on tissue without turning. The cure of PUs requires intensive care, especially for the elderly or people with preexisting conditions whose tissue needs longer healing times. The consequences are heavy suffering for the patient and extreme costs for the health care system. To avoid these consequences, our objective is to develop a pressure ulcer prophylaxis device. For that, we built a new sensor system able to monitor the pressure load and tissue vital signs in immediate local proximity at patient’s predilection sites. In the clinical study, we found several indicators showing correlations between tissue perfusion and the risk of PU development, including strongly reduced SpO2 levels in body tissue prior to a diagnosed PU. Finally, we propose a prophylaxis system that allows for the prediction of PU developments in early stages before they become visible. This work is the first step in generating an effective system to warn patients or caregivers about developing PUs and taking appropriate preventative measures. Widespread application could reduce patient suffering and lead to substantial cost savings.

Artificial Intelligence Support for Informal Patient Caregivers: A Systematic Review

“…Meanwhile, in countries like Italy with aging populations and caregiver shortages, initiatives like the GUARDIAN ecosystem offer robotic assistance and mobile applications to improve the daily lives and care quality of the elderly through human-robot interactions [4]. Moreover, artificial intelligence (AI) has been proven to be beneficial in patient care, from facilitating communication for those with cerebral palsy using facial recognition-based Morse codes to preventing pressure ulcers in immobile patients and detecting early signs of Alzheimer's disease [5][6][7]. AI-driven wearable devices also enable remote patient monitoring [8].…”

Smart Cushions with Machine Learning-Enhanced Force Sensors for Pressure Injury Risk Assessment

“…2 Existing PI risk warning techniques focus more on integrating sensor technology and artificial intelligence algorithms to classify patient position and posture to assess PI risk. 3,4 This method does not fully consider the specific mechanism of PI occurrence, leading to a low warning accuracy. Researchers usually use maximal shear strain as an important indicator to reflect the degree of internal tissue deformation 5 and assessing PI risk.…”