Transactions on Machine Intelligence 2821-1693 3 2 2020 06 01 Design and Development of a Pressure Monitoring System to Prevent Bedsores in Immobile Patients 101 110 208929 10.47176/TMI.2020.101 EN A. Memari Faculty of Medical Sciences and Technologies, Science and Research Branch, Islamic Azad University A. Tavakoli Golpayegani Standard Research Institute, Research Center for Technology and Engineering, Biomedical Engineering Group M. S. Hosseinzadeh Faculty of Medical Sciences and Technologies, Science and Research Branch, Islamic Azad University Journal Article 2020 04 19 Pressure ulcers, commonly known as bedsores, are localized injuries to the skin and underlying tissue caused by prolonged pressure, which restricts or entirely halts blood circulation. These ulcers typically develop in bony prominences such as the sacrum, heels, elbows, and hips due to the limited cushioning provided by subcutaneous fat in these areas. Immobile patients, particularly the elderly and individuals with chronic illnesses, are at a heightened risk of developing pressure ulcers, necessitating continuous monitoring and effective preventive measures. Traditional prevention methods involve regular patient repositioning, but manual monitoring is often inconsistent and labor-intensive. In response to this challenge, this paper presents the design and implementation of a smart mattress system capable of detecting and predicting the onset of pressure ulcers. The system utilizes an array of pressure sensors embedded beneath the mattress surface to continuously measure and analyze the distribution of pressure exerted on different body regions. By leveraging real-time data processing, the system can identify prolonged pressure points and issue timely alerts to caregivers or nursing staff, prompting necessary repositioning interventions. The proposed smart mattress aims to enhance patient care by automating pressure ulcer prevention, reducing the burden on healthcare personnel, and improving overall patient outcomes. The study explores the technical aspects of sensor integration, data analysis algorithms, and alert mechanisms to ensure the system's reliability and effectiveness in clinical settings. https://www.tmachineintelligence.ir/article_208929_1c055a7b6f884ebb6d0366927e0ddbb5.pdf