Predicting sports performance of elite female football players through smart wearable measurement platform

Recent development of information technology and wearable devices has led to the analysis of multidimensional sports information and the enhancement of athletes' sports performance convenient and potentially more efficient. In this study, we present a novel data platform tailored for capturing athletes' cognitive, physiological, and body composition data. This platform incorporates diverse visualization modes, enabling athletes and coaches to access data seamlessly. Fourteen elite female football players (average age = 20.6 ± 1.3 years; 3 forwards, 5 midfielders, 4 defenders, and 2 goalkeepers) were recruited from National Taiwan Normal University, Taiwan, as the primary observational group, and 12 female university students without regular sport/exercise habits (average age = 21.6 ± 1.3 years) were recruited as control group. Through multidimensional data analysis, we identified significant differences in limb muscle mass and several cognitive function scores (e.g., reaction times of attention and working memory) between elite female football varsity team and general female university students. Furthermore, 1-month heart rate data obtained from wearable devices revealed a significant negative correlation between average heart rate median and cognitive function scores. Overall, this study demonstrates the potential of this platform as an efficient multidimensional data collection and analysis platform. Therefore, valuable insights between cognitive functions, physiological signals and body composition can be obtained via this multidimensional platform for facilitating sports performance.