SmartFit: Using knowledge-based configuration for automatic training plan generation

Florian Grigoleit, Peter Struss, Florian Kreuzpointner

Research output: Contribution to journalConference articlepeer-review

Abstract

The fitness industry has been booming for several decades, and there is an increasing awareness of the essential impact of physical exercise on health. Those who are interested in exercising usually lack detailed knowledge about how to do this in a way that is effective and appropriate. Existing apps mainly offer a set of standard training plans that do not take all relevant individual and contextual conditions into account. The resulting effect of following these apps may not only be ineffective, but even harmful to health. Properly designed training plans, as usually produced by an experienced trainer, must consider both individual goals and physical abilities of the trainees to avoid adverse effects. We developed smartfit as a knowledge-based system for generating training plans tailored to the individual trainee without requiring detailed knowledge. It has been developed as an application of our generic constraint-based configuration system GECKO, which generates optimal or optimized configurations that satisfy high-level user demands. We briefly introduce GECKO, present the application problem and the domain knowledgebase, and discuss the evaluation of the current system and future work.

Original languageEnglish
Pages (from-to)30-37
Number of pages8
JournalCEUR Workshop Proceedings
Volume2467
StatePublished - 2019
Event21st International Configuration Workshop, ConfWS 2019 - Hamburg, Germany
Duration: 19 Sep 201920 Sep 2019

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