Monitor & Assess Activities
monitors athlete activities in near real-time and makes timely adjustments.
Essential Capabilities
Most fitness apps claiming "AI" are simply Large Language Models (LLMs) trained on internet articles, blog posts, and training theories—not actual training data. Other apps automate templates and philosophies with superficial AI based on incomplete and inaccurate metrics. Garbage in. Garbage out.
True training intelligence requires turning raw data into meaningful information, then knowledge, and finally actionable wisdom
All of the technological capabilities listed below are essential to develop a genuine fitness intelligence engine that goes beyond copying and pasting templates or automating someone's training philosophy. FitLogic Training Intelligence Engine uniquely delivers all these capabilities, built from 20+ years of actual training and race data—not internet text about training.
Quantify Training Stress
quantifies prescribed and actual training stress considering variables including discipline type, environment, intensity distribution, intensity levels, and intensity durations.
Quantify Residual Training Stress
measures the residual effects of training stress based on its varying intensity, duration, and frequency. This is not merely an accumulation of previous training stress. Training sessions with the same training stress value may have very different residual effects based on the nature of the sessions and systems stressed.
Normalize for Age and Gender
systematically evaluates each athlete's performance ability, expected and actual improvements, training stress, residual training stress, and other metrics relative to the athlete's specific age and gender.
Account for Environmental Factors
- quantifies the impact of environmental factors such as temperature, humidity, elevation, and terrain,
- dynamically adjusts prescribed training to account for environmental changes, and
- considers these factors when calculating training stress metrics.
Account for Genetics
analyzes athlete genomes and quantifies training intensity response, aerobic potential, recovery rate, and injury predisposition. Uses information during the analysis and prescription of training.
Leverage a Contextualized Dataset in Decision-Making
uses a contextualized dataset of training and race data with specific expected and actual outcomes. The dataset accounts for variables including but not limited to training stress, residual training stress, environmental conditions, age, gender, and genetics. The dataset size and quality is sufficient to identify and quantify correlations between training variables and outcomes as well as overall training efficacy.
Predict Training & Race Outcomes
predicts training and race outcomes. Without the ability to predict expected outcomes, the training system or platform cannot evaluate its own efficacy by quantifying the difference between the predicted and actual outcomes. Without the ability to evaluate efficacy, training quality cannot be evaluated or improved.
FitLogic® represents the evolution from data collection to data intelligence—delivering what others can't because it does what others don't. Built on 20+ years of evidence, not opinion, FitLogic® transforms raw training data into the actionable intelligence that powers better results in less time with fewer injuries.