![]() ![]() ![]() Despite these studies, the emotional states of the athletes have been measured in a very limited way, usually analysing the behaviour after performing the activity and through self-assessment questionnaires about their mood, which they use to fill in before and after the activity. For several years now, there have been many works that have studied and proved that emotional factors are fundamental to athletes’ training, performance and competition achievements. Athletes can use different strategies to regulate their emotions and therefore improve their performance. Emotions also affect our health and are a very important aspect in sports performance. It has been demonstrated that emotions have a significant impact on our daily lives, on our work, studies, choices or our ability to learn or make decisions. They are not used to give feedback in real-time since data are processed offline and visualised by the user when the activity is over in order to understand how the training evolved, what problems arose during that practice, what she/he felt in some specific moments, and how that could affect their performance. However, to achieve these issues, most of the commercial devices only capture and provide tracking information and performance measurements that are processed later and downloaded and analysed by the athletes or their coaches. These solutions usually obtain information about the health parameters of athletes or detect postural or physical problems in the performance of the activity. The use of wearables in the field of sports mainly focuses on tracking, analysis and improvement of performance, reducing injuries or controlling fatigue. It has required integrating the wearable and the models into the DJ-Running mobile application, which interacts with the technological infrastructure of the project to select and play the most suitable songs at each instant of the training. ![]() As part of the DJ-Running project, we have used these emotions to increase runners’ motivation through music. The solution is based on the analysis of runners’ electrodermal activity, a physiological parameter widely used in the field of emotion recognition. In this paper, we present a wearable and a set of machine learning models that are able to deduce runners’ emotions during their training. Emotions are involved in most of these solutions, but unfortunately, they are not monitored in real-time or used as a decision element that helps to increase the quality of training sessions, nor are they used to guarantee the health of athletes. In the field of sports, this technology is being used to implement solutions that improve athletes’ performance, reduce the risk of injury, or control fatigue, for example. Wearable technology is playing an increasing role in the development of user-centric applications. ![]()
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