INTELLIGENT PRESSURE MANAGEMENT SYSTEMS: INTEGRATING IOT SENSORS AND MACHINE LEARNING FOR PERSONALIZED PREVENTION OF PRESSURE ULCERS IN BEDRIDDEN PATIENTS

Abstract

This study investigates how smart mattr.esses with loT sensors and machine learning algorithms I might change pressure ulcer prevention for bedridden people: For those with restricted mobility, pressure ulcers, or bedsores, represent serious health dangers, requiring creative patient care 5 solutions. Pressure, temperature, motion, and humidity sensors are embedded in the mattress surface in our suggested system. These sensors capture real-time data on the patient's body dynamics, revealing pressure distribution, movement patterns, and ambient conditions. The data is sent to a central server where advanced machine algorithms forecast pressure ulcer risk. Patient profiles, medical histories, and pressure ulcer results are used to train the machine 10 learning model. The program customizes pressure ulcer prevention forecasts based on age, movement, and medical problems. Based on risk estimations, the system automatically niodifies repositioning schedules to optimize pressure relief and reduce ulcer risk. Healthcare professionals and caregivers get alerts and messages when risk exceeds established levels, enabling early actions. Temperature and humidity sensors offer context, allowing the system to excessive sweating or incontinence, which are crucial to risk assessment

Specification

Healthcare innovation is coming from clever solutions that avoid pressure ulcers for immobile
patients. This novel technique addresses pressure ulcers in non-mobile patients. Using cuttingedge
loT sensors and machine learning algorithms, healthcare practitioners are changing how
5 they treat pressure ulcers. Smart mattresses with pressure, temperature, motion, and humidity
sensors are at the heart of this invention. These sensors capture real-time, detailed patient body
dynamics and ambient data. loT technology streamlines data transfer to a central server, enabling
advanced machine learning algorithms. The field's innovation is using machine learning
algorithms to examine massive datasets of patient profiles, medical histories, and pressure ulcer
10 outcomes. These algorithms learn complex patterns to forecast each patient's pressure ulcer risk.
This personalised technique considers age, mobility, and medical problems for accurate risk
assessment. The technology optimizes pressure relief schedules based on real-time risk
estimations. Alerts and alerts urge healthcare practitioners to intervene quickly and customize
treatment. Temperature and humidity sensors give context, allowing the system to identify subtle
15 variables that affect risk assessment An intelligent pressure control system uses JoT sensors and machine learning algorithms to
revolutionize bedridden pressure ulcer prevention. This technology uses smart mattresses with
pressure, temperature, motion, and humidity sensors to monitor patients' body dynamics and
5 surroundings. The technology profiles each patient's demands by collecting real-time pressure
distribution and movement data. Based on patient characteristics, medical histories, and case
. outcomes, advanced machine learning algorithms predict pressure ulcer risk. Based on real-time
risk evaluations, the system adjusts repositioning schedules to optimize pressure relief and
reduce ulcer risk. Healthcare workers get notifications when danger levels surpass thresholds,
10 enabling prompt actions. Temperature and humidity sensors can identity ulcer-risk factors
including excessive sweating and incontinence. This unique strategy improves patient care,
encourages proactive healthcare management, and greatly .lowers pressure ulcers, increasing
patient outcomes and healthcare quality. The purpose of this project is to evaluate the potential for smart mattresses equipped with
Internet of Things sensors and machine learning algorithms to improve the prevention of
pressure ulcers in bedridden individuals. Patients with limited mobility are more likely to get
5 pressure ulcers or bedsores, which pose significant health risks and need the development of
innovative approaches to patient care. inside the surface of the mattress, our proposed system
incorporates sensors that measure pressure, temperature, motion, and humidity inside the
mattress. It is possible to get real-time data on the patient's body dynamics with the use of these
sensors, which indicate the distribution of pressure, movement patterns, and environmental
10 circumstances.
The data is then sent to a central computer, where sophisticated machine learning algorithms
anticipate the likelihood of developing a pressure ulcer. In order to train the machine learning
model, patient profiles, medical histories, and the outcomes of pressure ulcer measurements are
used. Forecasts for the avoidance of pressure ulcers are customized by the software depending on
15 factors such as age, mobility, and medical conditions. Additionally, the system automatically
adjusts repositioning schedules in accordance with risk assessments in order to maximize
pressure relief and minimize the danger of ulcers. When the risk surpasses the thresholds that
have been specified, alarms and messages are sent to healthcare professionals and caregivers,
which enables early steps to be taken. Sensors that measure temperature and humidity provide
20 context, which enables the system to recognize indicators of excessive sweating or incontinence;
both of which are essential for risk assessment
Detailed Description of Drawings
(I) Figure (i) shows the Block Diagram
(2) Figure (ii) shows the Power Adapter
The Raspberry Pi power adapter usually offers enough power for reliable operation. Depending
5 on the Raspberry Pi model, it has micro-USB or USB-C. The adapter converts AC mains power
to DC voltage, typically 5V output with various current ratings depending on model and
peripherals. A basic Raspberry Pi adapter may produce 5V at 2A, but the Raspberry Pi 4 needs a
more robust adapter with 5V at 3A to accommodate power-hungry components and peripherals.
(3) Figure (iii) shows the Raspberry pi 4
10 The Raspberry Pi 4 Model B is a flexible single-board computer with several advancements over
its predecessors. Its 1.5 GHz Broadcom BCM2711 quad-core ARM Cortex-A72 CPU boosts
performance for demanding workloads. Multitasking and complicated programs are supported by
the board's 2GB, 4GB, or 8GB LPDDR4 RAM. The Raspberry Pi 4 has two USB 3.0 ports, two
USB 2.0 ports, and a Gigabit Ethernet connector for fast network access. Dual micro-HDMI
15 connections accommodate up· to 4K monitors. The board has a 40-pin GPIO header. for
Cll connecting sensors, modules, and other devices.
(4) Figure (iv) shows DHTII module
The DHTII digital temperature and humidity sensor estimates ambient iemperature aiid
humidity accurately. It uses 3.3V to 5.5V, making it Raspberry Pi-compatible. Its single-wire
communication protocol makes the sensor straightforward to integrate into designs. Its tiny size
and low power consumption make it perfect for healthcare continuous monitoring. a. Heart rate and oxygen saturation sensors may be added to the system to provide immobile
individuals a complete health assessment.
b. Caregivers may monitor real-time data and get warnings on their cellphones using a
mobile app, improving remote patient management.
c. Machine learning models may be tailored to individual patient groups and requirements
in hospitals, nursing homes, and home care.
d. Healthcare practitioners may securely access previous patient data for trend analysis and
enhanced decision-making using cloud-based data storage.
10 e .. The design may accommodate different mattress kinds and sizes,· providing patient
comfort and health care infrastructure compatibility
We Claim
The above invention Intelligent Pressure Management Systems: Integrating loT Sensors and
Machine Learning for Personalized Prevention of Pressure Ulcers in ,Bedridden
Patientscomprises of:
5 . I. A smart mattress with inbuilt loT sensors that monitor pressure, temperature, motion,
and humidity to avoid pressure ulcers in bedridden patients.
2. A machine learning system that predicts pressure ulcer risk and customizes repositioning
programs based on patient profiles using real-time sensor data.
3. An alarm system that alerts healthcare personnel when pressure ulcer risk levels reach
thresholds, allowing quick intervention and treatment modifications.
4. Adding sensors like heart rate and oxygen saturation monitors to build a complete health
profile for patient monitoring.
5. Thi~ cloud-based data storage technology securely. stores prev1ous patient data so
healthcare practitioners may examine patterns for better patient care decisions.

Documents