Using Wearables Data for Differentiating Between Injured and Non-Injured Athletes

Abstract

Smartwatches nowadays are rich sensors that may provide their wearers with various data about their physical performance and physiological status. In this paper, we explore the possibility of using this data for identifying differences between athletes during a training program. We aim to distinguish between those who suffered musculoskeletal injuries to athletes that were not injured, by considering the external load and athletes' heart rate (Internal load). By Comparing the two groups, we found significant differences between the groups in the following features: Heart rate at rest and during sleep. In addition, percent time of rapid eye movement (REM) and deep sleep were significantly different between the two groups and the external load expressed by distance was significantly lower in the injured group. Our findings suggest that by tracing heart rate and sleep quality during a training program, we were able to characterize athletes that were at risk of injuries. This may be a first step for further analysis aimed to explore the possibility to predict the risk of injuries and to adapt the training loads accordingly to prevent injuries. In addition, upon such characteristics, user profiles can be built and used for personalized recommendations for avoiding injuries during training.