Athletic Performance Model Biomechanical Feedback
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Solution Overview
Problem
Current athletic-performance monitoring systems lack the ability to provide personalized and accurate feedback to athletes for improving technique and preventing injuries, as they do not effectively analyze biomechanical data to generate customized models for specific actions.
Innovation Solution
A method that utilizes a network of sensors, including cameras and wearable devices, to collect and analyze biomechanical data, determining action-parameter values and generating personalized athletic-performance models based on the data, which are then used to provide targeted feedback for improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are used to collect biomechanical data, then athletic performance monitoring capability is improved, but system complexity increases
Solution Approach 1:
The system segments the complex monitoring task into multiple independent sensor components, each responsible for collecting specific biomechanical parameters. This allows the overall system to achieve high measurement precision through specialized sensors while managing complexity by dividing functionality into manageable segments.
Solution Approach 2:
The patent implements a universal sensor network platform that can monitor multiple athletic actions and parameters using the same infrastructure. This multi-functional approach reduces system complexity by avoiding the need for separate specialized systems for each athletic discipline while maintaining high measurement accuracy across diverse applications.
2Measurement precision
If personalized athletic-performance models are generated, then feedback accuracy is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary data processing and model generation during training phases, preparing personalized athletic-performance models in advance. This preliminary action reduces real-time processing complexity while maintaining high feedback accuracy, as the heavy computational work is completed beforehand rather than during live monitoring.
Solution Approach 2:
The patent creates simplified copies or representations of complex biomechanical data through standardized performance models. These model copies capture essential performance characteristics while reducing data complexity, enabling accurate feedback generation without processing the full complexity of raw sensor data in real-time.
3Productivity
If target ranges with probability calculations are determined, then performance optimization is improved, but computational requirements increase
Solution Approach 1:
The system calculates probability-based target ranges for only the most critical performance parameters rather than all possible metrics. This partial action approach provides sufficient performance optimization while significantly reducing computational energy requirements by focusing resources on key performance indicators with the greatest impact.
Data Source
AI summary
In one embodiment, a method includes accessing an athletic-performance model of a first user, where the model is based on a plurality of sets of action-parameter values of action parameters of the first user. The action-parameter values may be determined based on biomechanical data of the first user performing a plurality of actions of a first action-type and outcome data of each action. A current skill level of the first user may be determined based on a measure of variances associated with action parameters of the first action-type. Target ranges of action-parameter values may be calculated for action parameters based on the athletic-performance model. Each target range may be based on a measure of probability with respect to the particular action parameters and the outcome data. A report of athletic-performance feedback may be generated and may include current skill level information and the target ranges of action-parameter values.