AI–baserat träningsstöd för löpning

Abstract

In the field of middle- and long–distance running in athletics, personalized training plans are an important factor for athletic success, yet the creation and adjustment of these plans remain a time–consuming process for coaches. Coaching methodologies often vary based on individual experience and philosophy, placing high demands on the flexibility of digital tools. This thesis explores how advanced AI models can be utilized to automate parts of this decision–making logic, aiming to reduce the administrative workload for coaches and enable an increased focus on technical analysis and active coaching. Through a needs analysis, a training platform has been developed to serve as an AI–based decision support system. The system integrates a regression model for predicting running pace, based on individual Garmin data, with a model based on retrieval–augmented generation for generating structured training schedules from educational material and the athlete’s profile. To enable individualized adjustments, a Large Language Model has been fine-tuned on specific coaching logic to handle athlete’s current physical condition. The result is a web–based prototype demonstrating how AI and machine learning can complement human expertise within middle- and long–distance running.