Running sports decision aid tool based on reinforcement learning approach

Abstract

Recently, an increasing number of people is interested in healthy lifestyle and gets involved in a variety of sports activities such as jogging, nordic walking, cycling, hiking or swimming. In order to better plan and track everyday training, people use a wide variety of smart mobile systems on their smartphones, smartbands and smartwatches. However, many of training mobile systems have their functional limitations and in many cases do not allow the user to perform in-depth data analysis in order to optimise the training. Therefore, in this paper, we propose adaptive decision aid tool that supports running sports practitioners in their daily training activities. This tool aims at suggesting the user the strategies for next training session (e.g. the maximum number of training days that can be skipped, duration of the training, the distance that should be covered) in order to meet the goals (in the experiments we have considered winning the one-year running competition). From the scientific standpoint, the tool adapts reinforcement learning in order to propose the runner suggestions that will allow for improving performance.