Vision-Based Distress Detection Using Movement Tracking
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Solution Overview
Problem
Current systems for monitoring distress in individuals, especially the elderly, face challenges such as high costs, privacy concerns, and accuracy issues with human observers, and limitations with devices that require user activation, which are not feasible for those with mental illnesses or memory problems.
Innovation Solution
A vision-based computer method that detects distress conditions by analyzing images from monitored locations, identifying human bodies, tracking movement, and requesting actions to confirm safety, with automated responses if no movement is detected in approved areas, using audio or visual cues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video surveillance with human observers is used to monitor distress, then detection capability is provided, but costs increase and privacy concerns arise
Solution Approach 1:
The patent replaces the mechanical system of human observers watching video streams with an automated computer vision system that uses image processing algorithms to detect distress conditions. The system automatically analyzes images from cameras, detects human bodies, tracks movement, and identifies distress situations without human intervention, thereby eliminating the need for continuous human monitoring while maintaining detection capability.
Solution Approach 2:
The system enables self-service monitoring where the monitored environment automatically detects and responds to distress conditions without requiring external human observers. The computer vision system performs autonomous analysis of visual data, automatically identifies distress situations, and can trigger alerts or responses, making the monitoring system self-sufficient and eliminating ongoing operational costs associated with human staff.
2Reliability
If continuous video monitoring by human observers is implemented, then distress situations can be identified, but accuracy decreases due to human error and fatigue
Solution Approach 1:
The patent replaces the human observational mechanism with an automated computer vision system that processes images through algorithms. This substitution eliminates human factors such as fatigue, distraction, and subjective judgment that compromise detection accuracy. The system consistently applies predefined criteria for identifying distress conditions, ensuring uniform and objective detection across all monitoring instances.
Solution Approach 2:
The system incorporates feedback mechanisms where the computer vision algorithm continuously analyzes image data, compares detected features against known distress patterns, and adjusts its detection criteria based on learned patterns. This feedback loop enables the system to improve its accuracy over time and maintain high precision in identifying genuine distress situations while reducing false alarms.
3Ease of operation
If devices requiring user activation are used, then false alarms are reduced, but usability fails for individuals with mental illnesses or memory problems
Solution Approach 1:
The patent replaces the manual activation mechanism with an automated detection system that uses computer vision to identify distress conditions without requiring any user action. The system automatically analyzes visual data, detects abnormal behaviors or states indicating distress, and triggers alerts independently, making it fully accessible to individuals who cannot operate activation devices due to cognitive impairments, memory problems, or physical limitations.
Solution Approach 2:
The monitoring system provides self-service functionality by automatically detecting and responding to distress situations without requiring the monitored individual to initiate any action. The computer vision system autonomously performs surveillance, analysis, and alert generation, creating a passive monitoring solution that serves vulnerable populations who cannot actively engage with traditional alarm systems.
Data Source
AI summary
A system and method for detecting a distress condition of a person in a monitored location. The system is configured to receive an image stream of the monitored location, and detect a human body or body part within the monitored location. The system maintains and updates a list of areas in which the lack of movement is permitted, e.g., bed, sofa, chairs. Upon detecting that the person is no longer moving and exists in a new area that is not in the list of areas, the system enters into an acknowledgement session in which the system asks the person to perform a certain action if everything is fine. If the given action is detected within a pre-determined period the system updates the list of areas to add the new area therein, otherwise the system would execute a pre-defined function representing a response to the distress condition, e.g., call 911.