AI Camera Monitoring for Selective Patient Activity Alarms
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Solution Overview
Problem
Existing camera monitoring systems for individuals in spaces like hospitals and nursing homes often result in unnecessary alarms due to incorrect situation detection, leading to inefficiencies and resource misallocation, as they cannot distinguish between individuals who need monitoring and those who do not, and may not be feasible due to privacy or financial constraints.
Innovation Solution
A system and method that utilizes AI models to analyze image data from cameras to identify and label individuals as 'persons to be monitored' based on predetermined conditions, track their activities, and trigger alarms only when specific activities matching their personalized alarm profiles occur, thereby reducing false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic camera monitoring is implemented to monitor all rooms, then safety monitoring coverage is improved, but false alarms increase due to incorrect situation detection
Solution Approach 1:
The monitoring system segments individuals into different categories (persons to be monitored vs. others) based on predetermined conditions such as staying on a bed for a first predetermined time period, staying in a predetermined area for a second predetermined time period, or staying alone in the space for a third predetermined time period. This segmentation allows the system to apply monitoring resources selectively, improving both coverage and detection accuracy by focusing analysis on relevant subjects.
Solution Approach 2:
The system performs preliminary classification of detected persons before full monitoring activation. By detecting whether a person fulfills predetermined conditions and generating a 'person to be monitored' label in advance, the system prepares the monitoring state proactively, ensuring that full monitoring resources are allocated only when necessary, thereby reducing false alarms while maintaining comprehensive safety coverage.
2Reliability
If full-time monitoring is performed to ensure safety, then safety level is improved, but privacy concerns and financial costs increase
Solution Approach 1:
Instead of implementing excessive full-time monitoring of all individuals in all rooms, the system applies partial monitoring selectively to persons who meet predetermined conditions. The monitoring intensity is adjusted based on need: full monitoring is applied only to labeled persons to be monitored, while other individuals receive minimal or no monitoring. This approach maintains necessary safety levels while significantly reducing financial resource consumption.
Solution Approach 2:
The system applies different monitoring qualities to different spatial locations and different individuals. Rather than uniform monitoring across all rooms and persons, the system dynamically adjusts monitoring focus to local areas where persons to be monitored are detected, concentrating computational and financial resources where they are most needed for safety assurance.
3Reliability
If monitoring of all persons is implemented, then comprehensive safety coverage is improved, but system complexity and processing load increase
Solution Approach 1:
The system divides the monitoring task into distinct segments: initial person detection, condition evaluation for 'person to be monitored' labeling, tracking of labeled persons, and activity recognition. This segmentation of the monitoring process into manageable stages reduces overall system complexity by allowing each component to focus on a specific function rather than handling all monitoring tasks uniformly.
Solution Approach 2:
The system performs preliminary detection and labeling of persons to be monitored before initiating full tracking and activity recognition processes. This preliminary action filters out individuals who do not require intensive monitoring, thereby reducing the processing load on subsequent system components and simplifying the overall system architecture by avoiding unnecessary processing of non-relevant subjects.
Data Source
AI summary
A monitoring method of at least one person in a space includes receiving image data of the space, detecting at least one person from the image data, and determining the at least one detected person as a person to be monitored if it is detected from the image data that the at least one detected person fulfils at least one predetermined person to be monitored conditions. The present disclosure also relates to monitoring system for monitoring at least one person in a space and a computer program product performing the monitoring method.