Abstract
Pressure ulcers (PU) are deep scars on the skin that cause pain, infections and severe health complications. Most movement-impaired subjects are vulnerable to PU, leading to permanent and irreversible skin damage. The system proposed in this paper aims to prevent PU formation with the design and implementation of a wirelessly controlled device that predicts PUs before their occurrence and attempts to prevent it using therapeutic feedback. A flexible pad that consists of multiple types of sensors is used, theses sensors continuously and non-invasively monitor ulcer-related vital signs in vulnerable areas, and uses these data to predict PU with a decision-making process. When PU is detected an electrical stimulation (ES) unit is automatically activated. Stimulation prevents PU formation by increasing local blood flow to the simulated area and eliminating the main factor that leads to PU formation. The system successfully monitored and predicted PU; tests were performed on three healthy volunteers and one volunteer with sacral ulcers. Results including readings of blood oxygenation, force, humidity and temperature were recorded as graphs to monitor decay/increase in values more efficiently.
Highlights
Pressure ulcers (PU) are localized injuries to the skin and underlying tissues caused by a local breakdown of cells as a result of compression between a bony prominence and an external surface [1]
Many people are susceptible to developing PU, but it is most critical in bedridden patients, including the elderly, quadriplegic subjects, intensive care unit patients and respiratory care unit patients [3]
Statistics show that PU affects 2.5 million patients per year according to the Centers for Disease Control (CDC), including 159,000 subjects in nursing homes
Summary
Pressure ulcers (PU) are localized injuries to the skin and underlying tissues caused by a local breakdown of cells as a result of compression between a bony prominence and an external surface [1]. Artificial intelligence (AI) was used to predict the subject’s position based on sensor readings and to alert the nursing staff when the subject maintained one position for a critical period of time (90 min) [8]. Prediction is based on readings from various sensors which monitor PU early clinical remarks: oxygen saturation in the blood-SpO2, force applied by patient’s weight to the monitored area, relative humidity and temperature of the monitored area [19,20] Statistical and physiological information is used to build a decision-making process which is a form of artificial intelligence, allowing the system to decide whether the subject is at risk of PU or not; prevention of PU is achieved by automatically activating the ES unit to eliminate PU symptoms.
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