# On deploying the Artificial Sport Trainer into practice

**Authors:** Iztok Fister Jr., Iztok Fister, Andres Iglesias, Akemi Galvez, Suash Deb, Dušan Fister  
**arXiv:** [2109.13334](https://arxiv.org/abs/2109.13334)  
**Date:** 2021-09-03  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2109.13334v1/

## Key points

- The paper discusses the use of Computational Intelligence methods for automatic generation of sport training plans in individual sport disciplines.
- These methods have been successfully deployed into practice to confirm their added value.
- A new low-cost and efficient embedded device called AST-Monitor has been introduced for monitoring cycling training sessions, based on the Raspberry Pi platform and supporting external sensors.
- An adjusted graphical user interface (GUI) tailored to athletes' needs has been created for the AST-Monitor.
- The authors demonstrate the potential of the AST-Monitor for automatically monitoring realized sport training sessions through a proof-of-concept study.
- The research contributes to enhancing the automation and efficiency of sport training processes through innovative technology solutions like the AST-Monitor, paving the way for future utilization of Artificial Intelligence in various sports disciplines.

## Summary

The paper "On Deploying the Artificial Sport Trainer into Practice" by Iztok Fister Jr., Iztok Fister, Andres Iglesias, Akemi Galvez, Suash Deb, and Dušan Fister delves into the use of Computational Intelligence methods for automatic generation of sport training plans in individual sport disciplines. These methods have reached a mature phase and have been successfully deployed into practice to confirm their added value. The authors highlight the development of various techniques for generating well-formulated training plans on computers that rely on past sport activities data. However, one challenge remains in automating the overall sport training process: monitoring the realization of training sessions. To address this issue, the paper introduces a new low-cost and efficient embedded device specifically designed for monitoring cycling training sessions - the AST-Monitor. This bike computer is based on the Raspberry Pi platform and supports external sensors for capturing data during training sessions. Along with hardware development, an adjusted graphical user interface (GUI) tailored to athletes' needs has been created. Through a proof-of-concept study utilizing the AST-Monitor in practice, the authors demonstrate its potential for automatically monitoring realized sport training sessions. Furthermore, they suggest that this new device paves the way for future utilization of Artificial Intelligence in various sports disciplines. Overall, this research contributes to enhancing the automation and efficiency of sport training processes through innovative technology solutions like the AST-Monitor. By integrating Computational Intelligence methods with practical monitoring devices, athletes and coaches can benefit from more personalized and effective training programs across different sports domains.