Pet Medical Checkup Device Using Motion Analysis
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Solution Overview
Problem
Existing pet health examination systems face challenges in accurately detecting diseases without stressing the pet, as they often require constant sensor attachment or rely solely on facial images, which limits the detection of diseases in other body parts and reduces accuracy.
Innovation Solution
A pet medical checkup device that captures moving images of pets, uses databases to identify specific motions associated with diseases, and combines motion and image analysis to estimate the pet's health condition without attaching sensors, allowing for comprehensive and accurate disease detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are attached to the pet's body for health monitoring, then measurement precision is improved, but the pet experiences stress and discomfort
Solution Approach 1:
The patent replaces mechanical sensor attachment with optical imaging technology. Instead of using sensors that physically contact the pet's body, the system uses a camera to capture images and videos of the pet, then extracts physiological information through image analysis. This substitutes a mechanical measurement system with an optical field-based system, eliminating the need for physical contact and reducing pet stress while maintaining measurement capability.
Solution Approach 2:
The patent introduces image processing algorithms and motion analysis techniques as intermediaries between the pet and the measurement system. Rather than directly measuring physiological parameters through sensors, the system uses images and videos as intermediate data that can be analyzed to infer health conditions, thereby indirectly obtaining measurement data without physical contact.
2Ease of operation
If only facial images are used for disease detection, then ease of operation is improved, but measurement precision deteriorates due to limited body part coverage
Solution Approach 1:
The patent transitions from two-dimensional static facial images to three-dimensional dynamic full-body video analysis. By capturing videos of the pet's overall motion and behavior, the system adds temporal and spatial dimensions to the data, enabling detection of diseases affecting various body parts through motion patterns, not just facial features.
Solution Approach 2:
The patent creates a multi-functional detection system that can identify various types of diseases through different analysis methods. The same video data can be analyzed for multiple disease conditions (respiratory, musculoskeletal, neurological, etc.), making the system universally applicable to diverse health issues while maintaining ease of operation.
3Measurement precision
If comprehensive motion analysis is performed to improve detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements self-service through automated image and video analysis algorithms. The system automatically processes captured media, extracts relevant features, compares them against disease patterns, and generates diagnostic suggestions without requiring complex manual intervention or specialized equipment operation, thereby managing complexity through automation.
Solution Approach 2:
The patent applies preliminary action by pre-storing disease-specific motion patterns and comparison data in a database. Before actual diagnosis, the system prepares reference data for various diseases, enabling rapid comparison and accurate detection during the examination process, thus reducing real-time computational complexity.
Data Source
AI summary
A pet medical checkup device of the present disclosure includes a shooting section that shoots a moving image of a pet, a first storage section that stores the moving image of the pet shot by the shooting section, a first database that stores motion information representing a specific motion made by the pet when the pet has a disease for each disease of the pet, a first determination section that determines whether or not the pet is making the specific motion represented by the motion information stored in the first database using the moving image of the pet stored in the first storage section, and an estimation section that estimates the disease of the pet based on a determination result of the first determination section.