MUMBAI, India, Sept. 14 -- Intellectual Property India has published a patent application (202641108126 A) filed by Pragati Engineering College on September 09, 2026, for Ai-Based Early Pressure Ulcer Prevention System For Bedridden Patients.
Inventors include Mrs. Lakshmi Viveka K; Mr. Sayantan Kar; Mrs. P. Ramya Krishna; and Mr. K. Chakravarthy Bheri.
The application for the patent was published on September 11, 2026, under issue no. 37/2026.
Abstract: This system integrates AI, IoT, wearable sensing, edge computing, cloud technology, and mobile health for a comprehensive approach to real-time bedridden patient monitoring and pre-emptive pressure ulcer development prevention. The architecture seamlessly combines smart pressure sensing, environmental monitoring, AI- powered risk assessment, and automated caregiver alerts into a unified health platform. This system enables continuous real-time monitoring of pressure distribution, body posture, skin temperature, humidity, and patient movement. The collected physiological and environmental data are then analysed by the proposed solution, to estimate the likelihood of developing pressure ulcer prior to visible skin damage, thereby enabling timely preventive actions to improve patient safety. The Flexible Force Sensitive Resistor (FSR) pressure sensors will monitor pressure distribution over body areas highly prone to ulcer formation (i.e. Back, hips, shoulders and heels). The MPU6050 accelerometer and gyroscope sensors will monitor patient movement, body orientation, and how often a patient is repositioned. DHT22 sensors are to monitor skin temperature and humidity for conditions conducive to pressure ulcer formation while other body temperature sensors will be deployed to assess the likelihood of skin inflammation localized skin temperature elevation. The ESP32 micro-controller will aggregate all readings from the above sensors, sync and filter, and normalize the recorded data before processing for an intelligent analysis. The data recorded and processed will be input to a trained machine learning model, to analyse pressure duration, pressure intensity, movement parameters, temperature variability, and the environmental parameters in order to determine a patient’s pressure ulcer risk level. Instead of a static, threshold-based monitoring system, the proposed AI system continuously learns from the interrelationship of the variables to make accurate predictions and reduce false alarms. When excessive pressure or high-risk condition is detected, the system immediately provides an auditory notification via a local buzzer as well as sending repositioning alerts to caregivers via the mobile app. The recorded health data, warning log, sensor logs and patient logs will be sent over Wi-Fi to the cloud server for permanent data storage and remote access. Doctors and caregivers will have the ability to use the web and mobile portal to access real-time dashboards, past pressure maps, pressure ulcer risk level of patients, repositioning schedules, and customized preventative recommendations.
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