Walking Condition Estimation Using Silhouette and Skeletal Feature Analysis
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Solution Overview
Problem
Current methods lack accuracy in diagnosing medical conditions related to walking disorders from observational data, as they rely on human expertise and cannot identify all diseases through walking behavior alone.
Innovation Solution
A computer system and method that processes video images of a subject walking to generate silhouette images and extract skeletal features, using machine learning models to estimate health-related conditions, including diseases that cause walking disorders, by integrating scores from silhouette and skeletal feature analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human expertise is used to diagnose medical conditions from walking behavior, then the method is simple to operate, but the accuracy and completeness of disease identification is insufficient
Solution Approach 1:
The patent replaces human expert observation with an automated image processing system that uses computer vision algorithms to analyze walking behavior. The system automatically extracts skeletal features, generates silhouette images, and diagnoses medical conditions through machine learning models, eliminating the need for human experts to manually interpret walking patterns while achieving higher diagnostic accuracy.
Solution Approach 2:
The patent creates simplified representations of the subject's body through silhouette images and skeletal feature extractions. These copies capture essential movement characteristics without the complexity of full-body 3D modeling, allowing accurate disease diagnosis while reducing computational requirements and system complexity.
2Measurement precision
If only silhouette images are used for condition estimation, then the processing is simpler, but the diagnostic accuracy is reduced
Solution Approach 1:
The patent combines multiple types of image data including silhouette images, skeletal features, and original image information into a comprehensive diagnostic system. By merging these different data representations, the system achieves higher diagnostic accuracy while managing complexity through integrated processing architectures that handle multiple data types协同ly.
Solution Approach 2:
The patent segments the image processing into distinct functional modules: silhouette generation, skeletal feature extraction, and condition estimation. This segmentation allows each module to process specific types of information independently, improving overall accuracy while making the complex system more manageable and maintainable.
3Measurement precision
If multiple image processing steps are applied, then the estimation accuracy improves, but the processing time increases
Solution Approach 1:
The patent performs preliminary image processing steps such as silhouette generation and skeletal feature extraction during the data collection phase, so that when actual diagnosis is needed, the processed data is already ready for rapid analysis. This preliminary action reduces the time required for final diagnosis while maintaining high accuracy through the pre-computed features.
Solution Approach 2:
The patent implements continuous processing where image analysis occurs in real-time during the subject's movement, rather than requiring separate discrete processing steps. The system continuously extracts features and updates diagnostic assessments as the subject moves, eliminating idle processing time while maintaining consistent accuracy through uninterrupted analysis.
Data Source
AI summary
The present disclosure provides a computer system and the like for estimating a condition of a subject. In one embodiment, the present disclosure provides a computer system for estimating a condition of a subject, and the computer system includes a receiving means for receiving a plurality of images photographed of the subject walking, a generation means for generating at least one silhouette image of the subject from the plurality of images, and an estimation means for estimating a condition related to at least one disease of the subject at least based on the at least one silhouette image.