Personalized Athletic-Performance Models Using Biomechanical Data
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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 fail to effectively analyze and utilize biomechanical data from various sensors to generate customized models for specific actions.
Innovation Solution
A method that involves receiving biomechanical data from sensors, analyzing action-parameter values, and generating athletic-performance models to provide personalized feedback, including current skill level assessment and target ranges for improved performance, using a network environment with sensors and cameras to capture and process data for real-time feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor networks are used to monitor athletic performance, then performance tracking capability is improved, but the ability to provide personalized and accurate feedback is insufficient
Solution Approach 1:
The system segments athletic performance monitoring into multiple specialized modules: biomechanical data collection from sensors, outcome data collection, action-parameter analysis, and personalized model generation. Each module handles specific aspects of performance tracking, allowing the system to provide both comprehensive monitoring and personalized feedback by analyzing specific parameters like release angle, velocity, and spin for individual athletes
Solution Approach 2:
The system changes parameters from generic performance metrics to specific action parameters (release angle, velocity, spin, etc.) that are customized for each athlete and sport. By adjusting and analyzing multiple parameters simultaneously, the system generates personalized feedback models that improve measurement precision while adapting to individual athlete needs
2Quantity of substance
If biomechanical data from multiple sensors is collected, then data comprehensiveness is improved, but the complexity of analyzing and utilizing this data increases
Solution Approach 1:
The system extracts only the most relevant action parameters from the comprehensive biomechanical data collected by multiple sensors. Rather than processing all available data, it identifies and extracts key parameters such as release angle, velocity, and spin that are most critical for performance improvement, thereby reducing processing complexity while maintaining data comprehensiveness
Solution Approach 2:
The system introduces an intermediary processing layer that translates raw sensor data into meaningful action parameters. This intermediary layer standardizes data from multiple sensor sources and converts it into actionable insights, simplifying the overall data processing architecture while handling comprehensive biomechanical information
3Ease of operation
If generic performance monitoring is implemented, then system simplicity is maintained, but the ability to provide tailored feedback for technique improvement is reduced
Solution Approach 1:
The system dynamically adapts from generic monitoring to personalized feedback by generating individual performance models for each athlete. These models are created through statistical analysis of each athlete's specific action parameters and are continuously refined, allowing the system to maintain operational simplicity while providing reliable, tailored feedback that improves technique
Data Source
AI summary
In one embodiment, a method includes receiving, from one or more sensors, biomechanical data of a first user performing a plurality of actions of a first action-type and outcome data of a plurality of outcomes corresponding to the respective plurality of actions. A plurality of sets of action-parameter values may be determined based on the received biomechanical data, where each of the sets of action-parameter values corresponds to a respective action of the plurality of actions. An athletic-performance model of the first user may be generated based on the plurality of sets of action-parameter values. The athletic-performance model may be a statistical model including probabilities computed with respect to the sets of action-parameter values and the outcome data.