Gaze Data Performance Analysis for Sports Athletes
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Solution Overview
Problem
Sports participants lack real-time and granular insights into their performance metrics during matches, as existing analysis methods fail to provide detailed statistical data on reaction times, attentiveness, and other key performance indicators across various match scenarios.
Innovation Solution
A computer-implemented method and system that captures gaze parameter data using an image sensor during sports matches, identifies key performance indicators (KPIs) associated with specific sports domains, filters the data to generate KPI reports, and presents them to participants via a user interface, enabling real-time and post-match performance analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video analysis is used to assess sports performance post-match, then macro-level statistics can be obtained, but granular differences in performance characteristics across multiple match scenarios cannot be elucidated
Solution Approach 1:
The patent segments the continuous video stream into discrete event-based time segments corresponding to specific match scenarios (e.g., serves, rallies, breaks). By dividing the analysis into scenario-specific segments, the system achieves granular performance measurement without requiring analysis of the entire match continuously, thus improving measurement precision while managing system complexity through focused processing of segmented data.
2Loss of information
If comprehensive performance data is collected across all match scenarios, then detailed statistical insights are available, but real-time feedback during the match cannot be provided
Solution Approach 1:
The system performs preliminary processing of video data during the match to extract and store key performance indicators as events occur. By preparing and pre-processing data in real-time during the match rather than waiting for post-match analysis, the system eliminates information loss while enabling timely feedback, as the data extraction and initial analysis are completed preliminarily during gameplay.
Solution Approach 2:
The patent implements a feedback mechanism that provides performance statistics to athletes in real-time or near-real-time during or immediately after matches. This feedback loop allows players to receive actionable performance information without significant delay, addressing both the need for comprehensive data collection and timely delivery by processing and delivering key metrics as they become available.
3Quantity of substance
If gaze parameter data is captured continuously over the entire match, then complete performance data is obtained, but processing time and computational resources increase significantly
Solution Approach 1:
The system extracts only the essential gaze parameters and performance metrics relevant to specific match scenarios from the continuous data stream, rather than processing all captured data. By selecting and extracting only the critical information needed for performance analysis (e.g., gaze direction during serves, fixation duration on key targets), the system maintains data completeness for performance evaluation while dramatically reducing processing time and computational resource requirements.
4Measurement precision
If detailed filtering rules are applied to gaze data, then accurate KPI data is generated, but the complexity of data processing increases
Solution Approach 1:
The patent applies different filtering rules and processing criteria tailored to specific match scenarios and performance metrics rather than using a single uniform filtering approach for all data. By customizing the filtering logic to match the specific requirements of each KPI (e.g., different temporal windows for reaction time vs. attentiveness metrics), the system achieves high measurement precision for each metric while managing processing complexity through scenario-specific rather than universally complex filtering logic.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for capturing gaze data for a sports participant over the course of a sports match, identifying a key performance indicator (KPI) corresponding to a sports domain with which the sports match is associated, and generating KPI data corresponding to the KPI. Report data indicative of the KPI data may be generated and presented to the sports participant in-match or post-match. KPI data for a player may be aggregated across multiple sports domain KPIs and multiple sports matches and analyzed to determine player activity patterns. The player activity patterns may indicate statistical differences in KPIs for the player in relation to different in-match scenarios. Recommendations may be provided to the player for improving the player's performance with respect to various KPIs.