Adaptive Resistance Training Programs From Athlete Biometric Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing athletic training systems lack individualized, automated, and efficient methods to optimize resistance levels and training programs based on athletes' specific biometric and performance data, leading to unpredictable and subjective gains.
Innovation Solution
An adaptive athletic training system utilizing machine learning algorithms and electronically adjustable resistance equipment to generate athlete-specific training programs, adjusting resistance levels dynamically based on biometric and performance data, and recording improvements for personalized training routines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If coaches and trainers manually create individualized training programs based on personal expertise, then training programs can be tailored to athlete needs, but the process becomes time-consuming and costly
Solution Approach 1:
The system enables athletes to receive automated, individualized training programs without manual intervention from coaches. The machine learning algorithm processes athlete biometric data and performance metrics to generate customized training regimens autonomously, eliminating the time-consuming manual program creation process while maintaining personalization.
Solution Approach 2:
The patent replaces the manual mechanical process of coach-athlete interaction and program creation with an automated computational system. Machine learning algorithms process data and generate training programs, substituting human expertise with algorithmic decision-making to reduce time investment while preserving individualization.
2Manufacturing precision
If coaches manually adjust resistance levels for each athlete through trial and error, then resistance can be optimized for individual athletes, but the process remains imprecise and time-consuming
Solution Approach 1:
The system continuously monitors athlete performance data and biometric information, using this feedback to automatically adjust resistance levels. The machine learning algorithm processes real-time data to optimize resistance precision without manual trial and error, achieving both high precision and time efficiency through closed-loop control.
Solution Approach 2:
The system dynamically changes resistance parameters based on processed athlete data. By automatically adjusting resistance levels according to individual athlete capabilities and performance metrics, the system achieves precise calibration while eliminating the time-consuming manual adjustment process.
3Productivity
If generalized training programs are used for all athletes, then training delivery becomes efficient and scalable, but athletic gains become unpredictable and random
Solution Approach 1:
The system segments the training program into individualized components based on each athlete's unique biometric data and performance metrics. Rather than applying a single generalized program, the machine learning algorithm creates customized training pathways for each athlete, maintaining scalability through automation while ensuring predictable, individualized results.
Solution Approach 2:
The training program transitions from a static, generalized approach to a dynamic, adaptive system. The machine learning algorithm continuously adjusts training parameters based on real-time athlete data, making the program responsive to individual athlete progress and needs. This dynamic adaptation ensures predictable gains while maintaining efficient automated delivery.
4Adaptability or versatility
If coaches manually optimize accessories and drill sequences for each athlete, then training can be highly customized, but the process becomes inefficient for large numbers of athletes
Solution Approach 1:
The system enables automated customization of drill sequences and accessory selection without manual coach intervention. The machine learning algorithm processes athlete data to generate optimized training routines, allowing the system to serve large numbers of athletes simultaneously while maintaining high levels of customization for each individual.
Data Source
AI summary
An athletic training system includes an athlete data repository, an adaptive algorithm, and at least one training unit. The athlete data repository includes biometric and performance data of athletes. The adaptive algorithm has access to the athlete data repository and is trained on biometric and performance data to generate athlete-specific training programs for use with the training unit. Each athlete-specific training program is adapted to biometric and performance data of an individual athlete. The training unit includes electronically adjustable athletic resistance equipment and a controller. The controller is configured to receive at least one athlete-specific training program of the plurality of athlete-specific training programs and to adjust resistance levels of the adjustable resistance equipment in response to the received athlete-specific training program. The controller is also configured to record performance data of an athlete's use of the training unit in accordance with the received athlete-specific training program.