Metabolic Data Optimization for Athletic Performance
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Solution Overview
Problem
Conventional exercise regimens fail to optimize athletic performance as they rely on sub-optimal metric data tied to power output rather than individualized metabolic profiles, and do not effectively monitor or correct posture for aerodynamic efficiency.
Innovation Solution
A system and method that utilize metabolic energy-system data to optimize athletic performance by receiving user data, generating power intervals, calculating energy data, and using artificial intelligence to identify energy sources and optimize exercise regimens, while also monitoring and adjusting posture for aerodynamic efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional exercise regimens use standard power output metrics for all athletes, then the system is simple to operate and implement, but the training optimization is insufficient because it does not account for individual metabolic profiles
Solution Approach 1:
The system segments athletes into different metabolic types (e.g., carbohydrate oxidizers, fat oxidizers) based on their individual metabolic profiles. This segmentation allows for personalized training regimens tailored to each athlete's energy system characteristics, thereby improving performance optimization without requiring overly complex individualized monitoring for every athlete.
Solution Approach 2:
The system changes the metric parameters from standard power output alone to include metabolic rate, energy substrate utilization, and oxygen consumption. By measuring and utilizing these additional parameters, the system can optimize training intensity and duration based on individual metabolic responses, resolving the contradiction between simple operation and effective optimization.
2Measurement precision
If the system monitors multiple metabolic parameters and energy sources, then the measurement precision of energy utilization is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system uses intermediary measurements such as heart rate, respiratory exchange ratio, and power output as proxies to infer metabolic state and energy substrate utilization. These intermediary parameters allow the system to estimate energy source oxidation (carbohydrate vs. fat) without requiring direct measurement of all metabolic parameters, thereby maintaining measurement precision while reducing device complexity.
Solution Approach 2:
The system employs multi-functional sensors that can measure multiple parameters simultaneously (e.g., power output, heart rate, and respiratory metrics) using a single integrated platform. This universality reduces the overall device complexity compared to using separate specialized sensors for each measurement, while still achieving high measurement precision for energy source identification.
3Adaptability or versatility
If exercise intervals are customized based on individual metabolic data, then the adaptability of training programs is improved, but the difficulty of detecting and measuring individual metabolic responses increases
Solution Approach 1:
The system performs preliminary metabolic assessments and tests before creating personalized training programs. By conducting initial measurements of resting metabolic rate, substrate oxidation preferences, and functional thresholds, the system establishes baseline data that simplifies subsequent monitoring and allows for easier detection of individual metabolic responses during training intervals.
Solution Approach 2:
The system implements continuous feedback loops that monitor real-time metabolic parameters during exercise and automatically adjust training intervals based on individual responses. This feedback mechanism simplifies the detection and measurement process by using real-time data to refine personalized prescriptions, making the system more adaptable without proportionally increasing measurement complexity.
Data Source
AI summary
A method for optimizing at least one exercise is provided. The method includes receiving user data. The user data includes biometric attribute data associated with a user of an exercise device. The method includes generating at least one interval, the at least one interval including at least duration data and target power data each associated with the exercise. The method includes during the at least one interval, receiving measurement data associated with at least one of the user and the exercise device. The method includes calculating, based on the measurement data and the user data, energy data associated with an expended energy of the user during the at least one interval, the energy data including quantity data and source information each associated with at least one energy source. The method includes generating, via an artificial intelligence engine, a machine learning model trained to identify the at least one energy source.