Today, Swimio creates workouts by combining multiple data sources instead of relying on a fixed template.
AI believes:
- Swimmer profile information collected during onboarding (experience level, goals, training frequency, strengths, weaknesses, etc.)
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Historic Swimming Performances and Complete Workouts
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Apple Health and Apple Watch metrics when available
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Progress over time and previous training adaptations
On top of this, swimmers can clearly customize the workout creation process by selecting parameters such as preferred stroke, available equipment (pull buoys, paddles, fins, kickboards, etc.), workout length, training focus and other constraints.
The goal is to create sessions that are individualized to the athlete rather than creating generic swim plans. Regarding the model, the current version uses AI-powered workout generation combined with swimming-specific training logic, optimized algorithms and constraints.
For future releases, we are actively working towards improving a dedicated model based on swimming training data and athlete performance patterns to provide even more personalized and adaptive coaching recommendations.
I’d love to hear what parameters you think would be most valuable for workout personalization!
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