A novel skin temperature estimation system for predicting pressure injury occurrence based on continuous body sensor data: A pilot study.

BACKGROUND

Pressure injury prevention is important in older patients with immobility. This requires an accurate and efficient prediction of the development of pressure injuries. We aimed to develop a method for estimating skin temperature changes due to ischemia and inflammation using temperature sensors placed under bedsheets to provide an objective, non-invasive, and non-constrained risk assessment tool.

METHODS

This study consisted of a thermal skin simulation study and a descriptive correlation study in healthy participants. A thermal skin simulation study was conducted using a model reproducing the body surface (underwear, diaper, or wet diaper conditions) and bed environment. In a descriptive-correlational study, the participants lay supine on a mattress with a temperature sensor attached to their sacral skin.

FINDINGS: The thermal skin simulation study showed that temperature changes in the skin can be estimated under the sheets by inputting time-shifted temperature data into machine learning (R2 = 0.9967 for underwear, 0.9950 for diapers, and 0.9869 for wet diapers). It was also demonstrated that the absolute skin temperature of a healthy individual (N = 17) could be estimated with the best accuracy by inputting time-shifted data into an extra-tree regressor (R2 = 0.8145).

INTERPRETATION: A combination of interface pressure and temperature sensors can be used to estimate skin temperature changes. These findings contribute to the development of a skin temperature measurement method that can capture temperature changes over time in clinical settings.