Automated Athletic Evaluation System Using Motion Capture and Force Data
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Solution Overview
Problem
Conventional methods for assessing an athlete's physical capabilities are prone to human error, lack standardization, and fail to provide actionable insights or guidance for improving performance, as they rely on manual tests that are time-consuming and limited in accuracy.
Innovation Solution
A method that involves obtaining position data during athletic movements, calculating movement metrics such as acceleration and power, and applying these metrics to a reference data set to determine performance categories and generate individualized training regimens that balance strength and speed, using tools like isokinetic dynamometers, force plates, and markerless motion capture systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual tests are used for assessment, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The patent replaces manual mechanical assessment methods with automated measurement systems including force plates, isokinetic dynamometers, and motion capture systems. These devices automatically collect and process data to generate performance metrics, eliminating human error while maintaining operational simplicity through computerized interfaces.
2Measurement precision
If automated measurement tools are used, then measurement precision is improved, but device complexity deteriorates
Solution Approach 1:
The patent integrates multiple measurement functions into a unified automated assessment system. The system combines force measurement, motion capture, and performance calculation capabilities into a single platform that can assess various athletic performance metrics through standardized protocols, reducing the need for multiple separate complex devices.
Solution Approach 2:
The automated measurement system performs self-calibration and automatic data processing. The system autonomously collects raw data from sensors, calculates performance metrics using embedded algorithms, and generates standardized reports without requiring complex manual intervention or specialized operator expertise.
3Measurement precision
If comprehensive position data is collected, then measurement precision is improved, but loss of time deteriorates
Solution Approach 1:
The patent implements continuous data collection during athletic movements using high-frequency sampling from force plates and motion capture systems. This continuous measurement approach captures complete movement trajectories and force profiles without interruption, enabling precise calculation of performance metrics while minimizing testing time through efficient data acquisition.
Solution Approach 2:
The system performs preliminary setup and calibration before actual testing, including sensor placement and system configuration. Once initialized, the system automatically executes standardized assessment protocols that streamline data collection, reducing the time required for each assessment while maintaining comprehensive measurement coverage.
4Measurement precision
If standardized assessment protocols are implemented, then measurement precision is improved, but adaptability deteriorates
Solution Approach 1:
The patent employs dynamic assessment protocols that adapt to individual athlete capabilities while maintaining standardized measurement principles. The system adjusts testing parameters such as resistance levels, movement velocities, and repetition ranges based on real-time performance feedback, allowing standardized metrics to be collected across diverse athlete populations without compromising assessment validity.
Data Source
AI summary
Performance of an athlete is evaluated using position data of the athlete over time during performance of a set of movements. Movement metrics for the set of movements are determined, the movement metrics including measures of acceleration and power. Performance metrics for the athlete are then calculated to indicate the athlete's strength and speed. A reference data set is defined based on various attributes associated with the athlete. The performance metrics are applied to the reference data set to determine a performance category for the athlete, the performance category indicating relative strength and speed of the athlete among other athletes represented in the reference data set. Lastly, a training regimen for the athlete is generated based on the performance category.