Computer Vision Gait Monitoring for Early Ailment Prediction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Ailments in humans and animals are often detected too late for successful treatment due to inadequate visual and diagnostic methods, particularly in cases like arthritis and diabetes in pets, where physical degradation is observed only after significant movement impairment.
Innovation Solution
A computer vision system using a device with a digital camera captures real-time video data, analyzes gait and posture, and predicts ailments like arthritis and diabetes by comparing current movements to historical data, with optional cloud-based or local prediction capabilities and electronic notifications for timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional visual and diagnostic methods are used to detect ailments, then the detection process is simple and requires minimal equipment, but the detection timing is too late for successful treatment
Solution Approach 1:
The system performs preliminary action by continuously capturing and analyzing movement data before ailments manifest as obvious symptoms. The computer vision system monitors gait, posture, and other movement parameters in real-time, detecting early signs of arthritis or diabetes before they become clinically apparent through traditional methods.
Solution Approach 2:
The patent replaces traditional mechanical/diagnostic examination methods with an optical system. Instead of relying on physical exams or laboratory tests that are performed periodically, the system uses digital cameras and computer vision algorithms to continuously non-contact monitoring of movement patterns, enabling earlier detection.
2Reliability
If continuous real-time monitoring is implemented, then early detection of ailments is enabled, but the system complexity and resource requirements increase
Solution Approach 1:
The system segments the monitoring task into distinct functional components: image capture, feature extraction, pattern recognition, and diagnosis. By dividing the complex monitoring process into separate modules, each handled by specialized algorithms, the system achieves high reliability while managing computational complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary processing layer between the camera and the diagnosis. This intermediary system includes feature extraction modules that identify specific movement patterns (gait, posture) and pattern recognition algorithms that compare them against disease signatures, acting as a mediator that translates raw visual data into medical insights.
3Productivity
If traditional diagnostic methods are used, then the equipment required is minimal, but the detection is performed too late after significant movement impairment occurs
Solution Approach 1:
The system implements continuous monitoring that operates without interruption. The computer vision system captures images and analyzes movement patterns continuously, ensuring that no early signs of illness are missed. This continuous action contrasts with periodic traditional diagnostics, enabling detection at the earliest stage when intervention is most effective.
Solution Approach 2:
The system incorporates feedback mechanisms where detected movement anomalies trigger alerts and can initiate automated workflows for veterinary intervention. The continuous monitoring provides real-time feedback about the animal's condition, enabling rapid response when abnormalities are detected, thereby reducing the time between detection and treatment.
Data Source
AI summary
A mobile or stationary device generates video data of a human's or an animal's movements. The device may then perform a vision analysis to predict an ailment, based on the video data. The video data may be compared to historical data to determine a difference in the human's or the animal's movements. If the difference is within a threshold value, then the vision analysis may infer that the human or animal is moving as expected. However, if the difference lies outside a normal range, or exceeds the threshold value, then the vision analysis may infer that the human or animal suffers from the ailment. Different ranges of values and thresholds may thus be established to infer different ailments. For example, gait and posture may be monitored over time to infer the early onset of arthritis and diabetes.