Hybrid Athletic Performance Prediction Model Using Conditional Probabilistic Analysis
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Solution Overview
Problem
Existing methods for evaluating and predicting athletic performance are limited by their reliance on either statistical associations that fail to account for mechanistic cause-and-effect relationships or deterministic models that neglect underlying randomness and are impractical to incorporate all relevant variables, leading to inaccurate predictions and inability to interpolate or extrapolate effectively.
Innovation Solution
A hybrid system that combines deterministic physical and biomechanical analytics with non-deterministic data from sensors to create a conditional probabilistic model, using methods like maximum entropy filtering, neural networks, and nonlinear regression to produce accurate predictions of athletic performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical methods are used to evaluate athletic performance, then predictions can be made based on observed data, but the methods fail to account for mechanistic cause-and-effect relationships and cannot reliably interpolate or extrapolate
Solution Approach 1:
The patent combines statistical methods with deterministic physical models to create a hybrid approach. Statistical methods provide probabilistic predictions from observed data, while deterministic models provide mechanistic cause-and-effect relationships. By merging these approaches, the system achieves both prediction accuracy and physical interpretability, resolving the contradiction between precision and complexity.
Solution Approach 2:
The patent introduces an intermediary layer that translates between statistical observations and deterministic physical models. This intermediary allows the system to leverage statistical data for prediction while maintaining deterministic models for physical understanding, enabling both accurate predictions and reliable interpolation/extrapolation without excessive complexity.
2Reliability
If deterministic models are used to evaluate athletic performance, then mechanistic cause-and-effect relationships can be captured, but the models neglect underlying randomness and are impractical to incorporate all relevant variables
Solution Approach 1:
The patent merges deterministic physical models with statistical methods to create a hybrid system. The deterministic component captures mechanistic cause-and-effect relationships for reliability, while the statistical component accounts for randomness and uncertainty. This combination resolves the contradiction between reliability and complexity by using statistical methods to manage the practicality issues of deterministic models.
Solution Approach 2:
The patent changes the parameters of the model by introducing probabilistic elements into the deterministic framework. Instead of using fixed deterministic parameters that are difficult to estimate, the system uses statistical parameters that can be inferred from data, making the model more practical while maintaining the benefits of deterministic physics-based reasoning.
3Measurement precision
If all relevant variables are incorporated into a deterministic model, then comprehensive analysis is achieved, but the model becomes impractical and computationally intractable
Solution Approach 1:
The patent extracts the essential deterministic physics from the complex system and separates it from the statistical variability. By taking out only the critical deterministic components and representing the rest through statistical methods, the system achieves comprehensive analysis without the impracticality of modeling all variables deterministically.
Solution Approach 2:
The patent segments the athletic performance model into deterministic physical components and statistical probabilistic components. This segmentation allows the system to handle different aspects of the problem with appropriate methods, making the overall model implementable while maintaining comprehensive analysis capability.
4Loss of information
If statistical methods are used, then associations between variables can be identified, but the methods cannot provide mechanistic understanding or interpolate for unobserved conditions
Solution Approach 1:
The patent merges statistical association-based methods with deterministic mechanistic models. The statistical component provides complete information from observed data, while the deterministic component provides mechanistic understanding and enables interpolation for unobserved conditions through physical principles, resolving the contradiction between information completeness and adaptability.
Data Source
AI summary
Exemplary systems, apparatus, and methods for evaluating and predicting athletic performance are described. Systems may include a receiver that gathers non-deterministic data on one or more aspects of athletic performance, a deterministic model of the athletic performance, a hybrid processor that creates a conditional probabilistic model from these elements, and a display presenting the evaluated or predicted performance. The system may include sensors affixed to an athlete or their equipment to convey position, acceleration, heart rate, respiration, biomechanical attributes, and detached sensors to record video, audio, and other ambient conditions. Apparatus may include a hybridization processor that communicates the output of conditional probabilistic models directly to athletes, coaches, and trainers using sound, light, or haptic signals, or to spectators using audiovisual enhancements to broadcasts. The methods enable more accurate evaluations and predictions of athletic performance than are possible with either statistical or deterministic methods alone.