Runner Speed Prediction Using Momentum Balance Feedback
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Solution Overview
Problem
Existing systems fail to accurately predict and improve maximum running speed (MRS) of athletes due to the complexity of physical, physiological, and motor control principles involved, and lack of a comprehensive system to provide meaningful feedback on runner characteristics and control inputs.
Innovation Solution
A system that predicts MRS by using a predictive algorithm based on environmental characteristics, runner's characteristics, and control inputs, achieving a balance of linear and angular momentum over a half-running cycle, with real-time feedback through wearable sensors and displays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If GPS based systems are used to provide trajectory and speed data, then speed information is provided, but the data is not meaningful enough to clearly identify how to improve runner's performance
Solution Approach 1:
The system implements feedback by processing runner data through predictive algorithms that generate meaningful performance indicators. The processor unit analyzes trajectory and speed data to provide actionable feedback that clearly identifies how to improve running performance, transforming raw GPS data into interpretable performance insights.
Solution Approach 2:
The processor unit acts as an intermediary between the GPS data collection system and the runner. It transforms raw trajectory and speed data into meaningful performance information through predictive algorithms, providing a bridge that converts meaningless data into actionable performance guidance.
2Measurement precision
If a comprehensive system with multiple running determinants is implemented, then prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the complex task of MRS prediction into distinct functional components: a processor unit that executes predictive algorithms and a database that stores running determinant data. This segmentation allows the system to handle multiple running determinants (foot contact time, stride length, ground impulse magnitude, leg stiffness, athlete's mass, limb kinematics, kinetics, running economy, and metabolic demands) in an organized manner, improving prediction accuracy while managing system complexity through modular architecture.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Provides accurate prediction and optimization of MRS, enabling athletes to achieve and maintain maximum running speed by adjusting control inputs and characteristics, with real-time feedback and post-performance analysis.
Implementation Method 1
The PU computes these POs by establishing a congruency for the speed condition required to obtain a zero linear momentum balance (vLM) for a given ground impulse ratio R value, with the speed condition required to obtain a zero angular momentum balance (vAM) for the same R value
Implementation Method 2
The PU computes these POs by establishing a congruency for the speed condition required to obtain a zero linear momentum balance (vLM) for a given ground impulse ratio R value, with the speed condition required to obtain a zero angular momentum balance (vAM) for the same R value
Data Source
AI summary
A system (10) and a method for determining a maximum miming speed (MRS) of a runner include a memory unit (32) (MU) with miming determinants (30) (RDs) of the runner and venue stored therein. A processor unit (22) (PU), connected to the memory unit (32) (MU), runs a predictive algorithm (24) (PA) using the running 5 determinants (30) (RDs) to determine the maximum running speed of the runner by zeroing a linear momentum balance and an angular momentum balance of the runner, typically over at least a half-running cycle (HRC). An output unit (34) (OU), connected to the processor unit (22) (PU), receives the determined maximum running speed therefrom. The zeroing also allows the determination of a critical 10 ground impulse ratio (Rcr) of the runner.