Machine Learning Models for Sports Remote Coaching Platforms

Offering timely support to users in eCoaching systems is a crucial factor to keep them engaged. However, coaches usually follow many users, so it is hard to prioritize those they should interact with first. Timeliness is especially needed when health implications might be the consequence of a lack of support. Thanks to the data provided by U4FIT (an eCoaching platform for runners we will describe in Chapter 1) and the rise of high-performance computing, Artificial Intelligence can turn such challenges into unparalleled opportunities. One of its sub-fields, namely Machine Learning, enables machines to receive data and learn for themselves without being programmed with rules. Bringing this intelligent support to the coaching domain has many advantages, such as reducing coaches’ workload and fostering sportspeople to keep their exercise routine. This thesis’s main focus consists of the design, implementation, and evaluation of Machine Learning models in the context of online coaching platforms. On the one hand, our goal is to provide coaches with dashboards that summarize the training behavior of the sportspeople they follow and with a ranked list of the sportspeople according to the support they need to interact with them timely. On the other hand, we want to guarantee a fair exposure in the ranking to ensure that sportspeople of different genres have equal opportunities to get supported. Past research in this field often relied on statistical processes hardly applicable at a large scale. Our studies explore opportunities and challenges introduced by Machine Learning for the above goals, a relevant and timely topic in literature. Extensive experiments support our work, revealing a clear opportunity to combine human and machine sensing for researchers interested in online coaching. Our findings illustrate the feasibility of designing, assessing, and deploying Machine Learning models for workout quality prediction and sportspeople dropout prevention, in addition to the design and implementation of dashboards providing trainers with actionable knowledge about the sportspeople they follow. Our results provide guidelines on model motivation, model design, data collection, and analysis techniques concerning the applicable scenarios above. Researchers can use our findings to improve data collection on eCoaching platforms, reduce bias in rankings, increase model effectiveness, and increase the reliability of their models, among others.