# Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case Study
## 1 Introduction  
Performance is tracked and optimized everywhere at any time. The sports world is an area where one can observe this ever growing need to perform better assessed by the growing market of mobile performance applications (see Deloitte 2017's report). In the business field, companies try to maximize their benefits by increasing their performance in multiple levels [1]. The common aspect between these two fields is that one can track the performance and get an idea of it through quantitative information. Data is nowadays ubiquitous, and devices are always collecting data. This makes it easy to access micro-quantitative and visual feedback of our performance but does not necessarily explain the meaning in a macro perspective. Indeed, amateurs as well as professionals in the sports field are relying on wearable devices as a motivational object, but more importantly to perform better [2].  
The growing market of wearables devices enables the tracking of new body-related information to be more precise and more detailed [3]. These ameliorations enable one to get a deeper quantitative feedback, and might also overwhelm the athletes with numerical and complex data, reducing their capabilities to understand and use it properly [4]. This high amount of data can lead the athletes to confusion or misunderstanding and could even make them lose motivation towards using such tools [5].  
Making sense of one's data may become a complicated task. The athlete's level will also determine how one interacts and uses the collected data. However, one can fall into overtraining syndrome, which is experienced when a person trains disproportionallyand ends up training in an inappropriate way compared to their capabilities and what their body can manage [6]. Additionally, such syndrome can occur when the equilibrium between the training load and the resting time is not respected and can lead to critical situations, both related to physical and mental health [7]. Amateur athletes are also more prone to develop such syndrome, as they do not benefit from coach feedback on the way they train [6].  
Personalized training feedback may be seen as a way to mitigate the issues induced by performance tracking in sports. Professional athletes rely on human coaches with expertise in managing training, efforts, and resting time. We suppose that amateur and semi-professional athletes are not interested in the price of a human expert to manage their training as this would be too much for their needs. This level of athletes use training software that lacks a proper personalization component and may experience a decrease in their motivation past a certain time [8].  
In our study, we present a novel approach to training management. We leverage on new artificial intelligence-based work in order to create a virtual coach with training personalization capabilities. The technique we use is able to tailor and take into consideration any level of athletes, as it bases the feedback instructions on the capabilities of its users. Using state-of-the-art technologies and specific measurements, we ensure a proper and convenient training plan designed and managed around the athletes. Thus, we provide amateurs and semi-professionals with a convenient training coaching system, enabling them to get expert-like instructions and feedback. Additionally, this solution can provide human coaches with a different point of view on their athletes' training and introduce more diversity in training plans.