Patient Monitoring Video Analytics for Privacy-Safe Elopement Detection
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Solution Overview
Problem
Existing patient monitoring systems in hospital settings require continuous human surveillance to detect adverse events, which is inefficient and labor-intensive, and they often rely on video feeds that may infringe on patient privacy due to the need for continuous recording and analysis.
Innovation Solution
A patient monitoring system that converts video feeds into generic data points, using estimation networks to analyze patient position and predict adverse events without recording identifiable information, focusing on features like shoulders, hands, and feet, and utilizing audio analysis to enhance detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous video surveillance is used to monitor patient position and status, then adverse event detection capability is improved, but patient privacy is compromised and labor requirements increase
Solution Approach 1:
The system extracts only the essential geometric data points (shoulders, hands, feet positions) from the video feed while discarding all identifiable personal information. This extraction approach maintains adverse event detection capability while eliminating privacy infringement by removing facial features, clothing details, and other identifying characteristics.
Solution Approach 2:
The patent introduces an intermediary processing layer that converts video data into abstract geometric representations. This intermediary stage acts as a mediator between the video camera and the monitoring system, transforming detailed visual information into simplified coordinate data that preserves monitoring effectiveness while protecting patient identity.
2Reliability
If continuous video surveillance is implemented, then adverse event detection is improved, but labor intensity and operational costs increase
Solution Approach 1:
The system implements self-service monitoring by automatically analyzing video feeds and detecting adverse events without requiring continuous human observation. The automated processing of geometric data points and generation of alerts enables the system to monitor patients independently, significantly reducing the labor intensity associated with traditional continuous surveillance methods.
Solution Approach 2:
The patent replaces the mechanical system of continuous human visual monitoring with an automated computational system. The processor automatically extracts geometric data, identifies adverse events based on position changes, and generates alerts, substituting human labor with machine-based detection that maintains high reliability while reducing operational burden.
3Measurement precision
If detailed video analysis is performed to identify patient characteristics, then monitoring accuracy is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system applies local quality by focusing analysis only on specific body regions (shoulders, hands, feet) that are critical for detecting adverse events. By concentrating processing resources on these localized anatomical points rather than analyzing the entire video frame in detail, the system maintains monitoring precision while reducing overall data processing complexity and storage requirements.
Data Source
AI summary
A patient monitoring system includes a camera that selectively delivers a video feed to a monitoring station. A processor evaluates the video feed, and converts the video feed into a plurality of data points for an elopement detection system. The plurality of data points correspond at least to a combination of facial features and components of clothing for each person within the video feed. The processor is further configured to associate the combination of facial features and the components of clothing for each person to define a confirmed association, to identify whether the person is a patient or a non-patient, to verify the confirmed association of the patient by comparing updated data points from the video feed with the confirmed association, and to activate an alert when the confirmed association of the patient is unverified based upon a comparison with the updated data points.