Biomechanical Evaluation of Athletic Performance
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Solution Overview
Problem
Current methods for evaluating the biomechanical performance of racehorses are limited by the difficulty in obtaining an unobstructed view of multiple horses exercising or racing simultaneously, and the time-consuming nature of manual monitoring, which can lead to missed early detection of pathology or injury.
Innovation Solution
A computer system that uses cameras to capture image data of animals, identifies specific animals and anatomical markers, tracks the position of these markers, determines biomechanic metrics, and applies them to a biomechanical evaluation model to generate assessment metrics for evaluating animal performance, with the ability to transmit these metrics to computing devices for real-time monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual monitoring methods are used to evaluate biomechanical performance of racehorses, then detailed assessment can be performed, but the process is time-consuming and cannot keep pace with multiple horses training or racing simultaneously
Solution Approach 1:
The patent replaces manual visual monitoring with an automated computer vision system that uses machine learning models to detect and analyze biomechanical markers on horses. The system processes video footage from cameras positioned around the track, automatically identifying anatomical landmarks and calculating movement metrics without human intervention, thereby maintaining measurement precision while dramatically increasing monitoring throughput.
Solution Approach 2:
The system creates digital copies of biomechanical data by capturing video images and generating synthetic 2D representations of horse anatomy from multiple camera angles. These digital models are then analyzed by machine learning algorithms to extract movement patterns, replacing the need for direct human observation while preserving the detailed assessment capability.
2Reliability
If trainers or veterinarians manually monitor each racehorse during training or racing, then early pathology or injury may be detected, but the capacity to monitor all horses is limited
Solution Approach 1:
The system enables self-service monitoring where each horse's biomechanical data is automatically captured and analyzed independently through the computer vision system. The machine learning model processes each horse's movement patterns autonomously, detecting anomalies that may indicate injury or pathology without requiring human expertise for each individual animal, thus scaling monitoring capacity to cover all horses simultaneously.
Solution Approach 2:
The system implements continuous feedback monitoring by analyzing biomechanical metrics in real-time and comparing them against established norms. When deviations indicating potential injury or pathology are detected, the system generates alerts to notify trainers or veterinarians, enabling early intervention while maintaining the ability to monitor large numbers of horses through automated anomaly detection.
3Measurement precision
If visual monitoring is used to detect pathology or injury in early stages, then intervention can be timely, but even experienced trainers or veterinarians cannot reliably detect early-stage issues
Solution Approach 1:
The system performs preliminary analysis of biomechanical data by continuously tracking movement patterns and establishing baseline metrics for each horse. The machine learning model is trained on extensive datasets to recognize subtle deviations that precede visible injury symptoms, enabling early detection of pathology before it becomes apparent through traditional visual inspection, while the automated nature of the system manages complexity through algorithmic processing rather than human expertise.
Data Source
AI summary
A method for assessing the biomechanics of an animal including applying, to a biomechanic evaluation model, i) a tracked position of biomechanic markers, and ii) a determined biomechanic metric, to generate an output including at least one biomechanic assessment metric for use in assessing the performance of the identified animal.