Horse-Riding Simulator Posture Recognition via Standard Model Matching
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Solution Overview
Problem
Current horse-riding simulators lack effective posture recognition technology to provide personalized coaching and training, failing to accurately assess and improve user horse-riding skills using visual sensors.
Innovation Solution
An apparatus and method that generate a standard posture model from expert horse-riding images, match user posture characteristics with this model, and suggest progressive lessons tailored to individual horse-riding levels by extracting and normalizing feature points such as shoulder, elbow, and feet positions, using a vision sensor to recognize and correct user posture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If posture recognition technology is implemented in horse-riding simulators, then personalized coaching and training effectiveness are improved, but device complexity increases
Solution Approach 1:
The posture recognition system is segmented into multiple independent modules: visual sensor module for image acquisition, feature point extraction module for identifying key body positions, posture matching module for comparing against standard models, and feedback module for providing coaching suggestions. This modular segmentation allows each component to perform a specific function efficiently while reducing overall system complexity through clear division of labor.
Solution Approach 2:
A standard posture model serves as an intermediary between the visual sensor input and the coaching output. The system captures user posture images, compares them against pre-established standard posture models containing feature points of correct horse-riding positions, and generates feedback based on the matching results. This intermediary model simplifies the recognition process by providing a reference framework for automated assessment.
2Measurement precision
If accurate posture recognition is achieved through multiple feature points, then measurement precision is improved, but calculation complexity increases
Solution Approach 1:
The system extracts only the essential feature points necessary for posture assessment rather than processing entire images or all body parts. Key feature points such as shoulder position, elbow position, wrist position, hip position, knee position, and ankle position are identified and extracted for comparison with standard models. This selective extraction maintains measurement precision while significantly reducing computational complexity by focusing only on critical posture indicators.
Data Source
AI summary
An apparatus for recognizing a user's posture in a horse-riding simulator, the apparatus comprising: a standard posture model generation module configured to find out a standard posture model by selecting feature points from an expert database, and generate the standard posture model; and a posture recognizing module configured to obtain a user's posture from the horse-riding simulator, recognize a user's horse-riding posture by matching the obtained user's posture with the standard posture model generated in the standard posture model generation module, and suggest a standard posture model appropriate for a user's level.