AI Patient Activity Monitoring to Reduce False Alarms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing camera monitoring systems for individuals, such as patients in hospitals or nursing homes, often generate false alarms due to incorrect situation analysis, leading to unnecessary work and potential safety risks when medical staff are diverted from their tasks.
Innovation Solution
A computer-implemented monitoring system that uses AI models to detect and label individuals as 'persons to be monitored' based on predetermined conditions, tracks their activities, and triggers alarms only when specific activities matching predefined profiles are detected, thereby reducing false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic camera monitoring is used to monitor all rooms, then monitoring coverage is improved, but false alarms increase due to incorrect situation analysis
Solution Approach 1:
The system segments the monitoring task by dividing the space into multiple zones (e.g., bed area, floor area, movement paths) and applies different monitoring rules to each zone. This allows the system to focus analysis on specific areas where falls or unauthorized movements are most likely to occur, improving situation analysis accuracy while maintaining comprehensive coverage.
Solution Approach 2:
The system applies different monitoring sensitivities and algorithms to different spatial locations. For example, the floor area has higher alarm sensitivity for falling detection, while the bed area uses different criteria for unauthorized movement. This localized approach reduces false alarms by adapting the monitoring strategy to the specific characteristics of each zone.
2Measurement precision
If full-time manual monitoring is performed, then situation analysis accuracy is improved, but resource consumption increases
Solution Approach 1:
The monitoring system performs self-service by automatically analyzing video feeds, detecting situations, and generating alarms without requiring constant human intervention. The AI algorithms continuously process the video data, identify potential incidents, and trigger appropriate alerts, enabling the system to monitor multiple rooms simultaneously with high accuracy while minimizing the need for human supervisors.
Solution Approach 2:
The system replaces the mechanical human monitoring process with an automated electronic monitoring system using AI and computer vision technology. This substitution allows the system to process and analyze video data from multiple rooms simultaneously with consistent accuracy, eliminating the need for multiple human supervisors and reducing financial resource requirements.
3Quantity of substance
If manual monitoring is reduced, then resource consumption is decreased, but false alarms increase due to incorrect detection
Solution Approach 1:
The system incorporates feedback mechanisms where alarm events are continuously analyzed and used to refine the detection algorithms. When false alarms occur, the system learns from these events and adjusts its detection parameters to prevent similar false alarms in the future. This feedback loop improves detection accuracy over time while maintaining automated operation with reduced resource consumption.
Solution Approach 2:
The system performs preliminary analysis of video data to identify potential incidents before they become critical situations. By detecting early signs of falls, unauthorized movements, or other incidents, the system can prepare appropriate responses and reduce false alarms by confirming situations before triggering alarms, thereby maintaining high detection accuracy with automated monitoring.
Data Source
Figure 1~2
Figure 3~4
Figure 5
AI summary
The invention relates to a monitoring method of at least one person in a space. The method comprises receiving image data of the space, detecting at least one person from the image data, and determining the at least one detected person (15) 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 invention also relates to monitoring system for monitoring at least one person in a space and a computer program product performing the monitoring method.