This predictor introduces machine learning into the domain of physiotherapy and sports medicine.
The elite sports analytics market focuses on historical data and general fatigue scoring. Sport Twin introduces forward-looking, simulation-driven analytics, offering athlete specific performance predictive insight.
Using a wearable continuous vital-sign monitor, Sport Twin couples real-time data streaming with personalised adaptive learning. Each individual, focused on optimising athletic output, benefits from a digital twin comprised of a continuously learning computational model that predicts short-term physiological change and guides intervention.
Sport Twin uses an individually personalised adaptive learning approach, dynamically calibrating baselines for each individual and learning how the body reacts to stress, load, and recovery. This establishes a high-fidelity connection between physiology, environment, and action.
The sports predictor addresses the priorities of tracking changes in elite performance to optimise the training plan on an individualised basis. The predictor uses three signal indicators and predictors to support training and coaching objectives.
This predictor is designed to enable elite athlete trainers, coaches and their clinical colleagues in sports medicine to optimise training load, predict fatigue, and personalise recovery strategies for professional and elite athletes by using continuous physiological data and digital-twin simulations. It has applications in endurance sports such as cycling, marathon running, triathlon, explosive power sports such as football, rugby, athletics and precision sports such as tennis, golf where micro-fatigue influences accuracy.
The dashboard displays unique indicators to track the training (HR-power ratio) trajectory, using vital signs data.
The three metrics are defined uniquely in Sport Twin. While similar constructs exist (e.g., WHOOP Readiness, HRV-based fatigue metrics), the metrics used here are proprietary composites and algorithms that integrate physiological data predictions. They are not yet established or standardised across the sports analytics industry.
- Performance Deviation Index (PDI): detects statistically significant dips in key metrics (VO₂ proxy, HR-power ratio).
- Fatigue-Injury Risk (FIR): probability of strain or overuse injury given training load.
- Readiness Score (RS): composite of recovery, sleep, and psychological indicators.
The advanced prediction functionality simulates what-if recovery protocols including massage, nutrition, or sleep and learns from longitudinal adaptation curves to forecast performance peaks and tapering points and uses reinforcement learning to fine-tune recommendations based on observed vs simulated outcomes.
The dashboard captures data for individuals and for teams, with fatigue mapping to enable benchmarking of individual performance monitoring within teams. It is intended to integrate through API with existing performance management products.
The focus is early detection of overtraining and injury risk, enhanced recovery efficiency and load accuracy, and competitive advantage through precision readiness forecasting.
for more information: Dr Michael Tremblay, mike_tremblay@skythunder.net, www.skythunder.net