Pressure-Sensing Support Device With AI Pressure Injury and Fall Prediction
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Solution Overview
Problem
Pressure injuries and falls in clinical or at-home settings are common issues that current turn regimens fail to address effectively, leading to increased hospital time, medical costs, and potential serious complications.
Innovation Solution
A continuous patient monitoring system using a weight support device with embedded sensors and AI-powered algorithms to predict pressure injuries and falls, providing real-time adjustments to prevent these issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a continuous monitoring system with AI algorithms is implemented, then patient safety and care personalization are improved, but device complexity and initial costs increase
Solution Approach 1:
The monitoring system is divided into discrete functional modules: sensor grid layer for pressure detection, processor for data analysis, and alert generation system. This segmentation allows the complex system to be implemented and maintained in manageable parts while achieving high reliability through coordinated operation of specialized components.
Solution Approach 2:
The system performs preliminary analysis of pressure data using AI algorithms to predict potential pressure injuries before they occur. By continuously monitoring and analyzing pressure distributions in advance, the system can alert caregivers to reposition patients before skin breakdown happens, thereby improving patient safety proactively rather than reactively.
2Measurement precision
If AI-powered algorithms are used to predict pressure injuries, then measurement precision of injury risk is improved, but device complexity increases
Solution Approach 1:
The processor acts as an intermediary between the simple pressure sensors and the complex AI algorithms. It collects raw pressure data from the sensor grid, processes it through machine learning models to generate injury risk assessments, and presents simplified results to caregivers. This intermediary layer enables high measurement precision while shielding users from algorithmic complexity.
Solution Approach 2:
Traditional mechanical assessment methods (manual inspection, simple pressure gauges) are replaced with electronic sensor arrays and computational AI algorithms. This substitution dramatically improves measurement precision for predicting pressure injuries, as the system can analyze multiple pressure points simultaneously and process data through sophisticated risk assessment models that far exceed human capability.
3Speed
If real-time pressure monitoring is implemented, then detection speed of high-pressure areas is improved, but use of energy increases
Solution Approach 1:
The system implements periodic sampling of pressure data at optimized intervals rather than continuous monitoring at maximum rate. The processor adjusts sampling frequency based on patient activity level and risk factors, maintaining fast detection capability when needed while reducing energy consumption during stable periods. This periodic action allows the system to achieve effective real-time monitoring without sustained high energy use.
Solution Approach 2:
The monitoring system dynamically adjusts its operation mode based on detected conditions. When sudden movement or elevated pressure patterns are detected, the system increases sampling rate and processing intensity for faster detection. During stable periods with low risk, it reduces activity to conserve energy. This dynamic adaptation maintains detection speed when critical while minimizing overall energy consumption.
Data Source
AI summary
Embodiments may relate to an intelligent patient monitoring system, which may include a weight support device, a computer, and a display. The weight support device supports a patient and includes a sensor grid layer with a plurality of sensors to measure pressure data. The computer predicts a pressure injury outcome and/or a fall outcome based on the pressure data. The pressure injury outcome includes a prediction of risk of the patient developing a pressure injury. The fall outcome includes a prediction of risk of the patient experiencing a fall. The computer may utilize a machine learning model to determine either or both outcomes. The display presents a notification generated based on the pressure injury outcome or fall outcome. The notification indicates that an adjustment of a positioning of the patient is needed to aid in the prevention of the pressure injury or the fall.