Computer Vision Patient Monitoring for Clinical Parameter Detection
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Solution Overview
Problem
Caregivers in healthcare facilities face challenges in continuously monitoring patients for clinical parameters such as pressure injuries, bed exit risks, sleep patterns, mobility, and falls, as visual observation is impractical and relies on manual detection, which can be inaccurate and time-consuming.
Innovation Solution
A system utilizing a camera and computer vision processor to analyze real-time video feeds of patients, employing machine learning models to detect clinical parameters like pressure injuries, bed exit intentions, sleep quality, and mobility, and automatically issuing alerts to caregivers when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If visual observation by caregivers is used to monitor patients, then patient safety and clinical parameter detection can be achieved, but caregiver workload increases and continuous monitoring becomes impractical
Solution Approach 1:
The system enables self-service monitoring where the patient monitoring system automatically detects clinical parameters and issues alerts without requiring continuous human observation. The computer vision processor and machine learning model autonomously analyze video feeds to identify patient postures, movements, and potential risks, freeing caregivers from constant visual monitoring duties.
Solution Approach 2:
The patent replaces the mechanical system of human visual observation with an automated computer vision system. The camera and computer vision processor substitute for human eyes and brain processing, using optical detection and algorithmic analysis to monitor patients continuously without human intervention.
2Measurement precision
If manual detection of clinical parameters is used, then some patient conditions can be identified, but detection accuracy decreases and time consumption increases
Solution Approach 1:
The system implements continuous monitoring of patients through uninterrupted video feed analysis. The computer vision processor continuously processes video frames to track patient postures, movements, and behavioral patterns over time, providing constant surveillance without the interruptions inherent in manual checking schedules.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from caregiver inputs and detected clinical parameters. The system provides real-time feedback through alerts when specific conditions are detected, and the model is trained on feedback from caregivers to improve detection accuracy over time.
3Productivity
If automated computer vision monitoring is implemented, then monitoring accuracy and efficiency improve, but system complexity increases
Solution Approach 1:
The system achieves multi-functionality by using a single computer vision processor to detect multiple different clinical parameters including pressure injury risk, bed exit risk, falls, sleep patterns, and mobility issues. The machine learning model is trained to recognize various patient postures and behaviors, allowing one system to perform multiple monitoring functions simultaneously.
Solution Approach 2:
The patent introduces a camera as an intermediary device that captures video feeds, which are then processed by the computer vision processor. This intermediary approach allows non-intrusive monitoring while converting physical patient states into digital signals that can be analyzed algorithmically, bridging the gap between physical patient conditions and digital detection systems.
4Reliability
If continuous visual monitoring is performed, then all patient conditions can be detected, but resource consumption and operational cost increase
Solution Approach 1:
The system uses periodic analysis of video feeds rather than processing every single frame continuously. The computer vision processor analyzes patient postures and movements at appropriate intervals, triggering detailed analysis only when changes are detected. This periodic approach maintains detection reliability while reducing computational energy consumption compared to continuous full-frame processing.
Data Source
AI summary
Monitoring patients utilizing computer vision to analyze patient movements on or near a patient support apparatus such as a bed. Computer vision is employed to analyze video feed of patients to detect movements indicative of clinical parameters. Information generated by the computer vision processor can be recorded to a patient's electronic medical record. Such information can be used to make clinical assessments, diagnoses, or detect critical patient events requiring attention of caregivers. In instances where caregiver assistance is required, an alert can be communicated to one or more caregivers through a caregiver call system.