Interestingness measure for mining sequential patterns in sports

The increasing availabilities of tracking devices, including mobile devices and sports trackers with heart-rate monitors, accelerometers and GPS receivers, have increased the interest in developing fitness applications. The aims of these applications are to improve the motivations of athletes during training, as to track the histories of their sports activities, to advise the type of training for the future, and even to share this information with friends on social networks. This study proposes a novel method for analyzing the time series data gathered from a single athlete over an extensive time period of training. Using this method, the transformed time series data are exploited by a sequential pattern mining algorithm, then the novel trend of interestingness measures are calculated for discovering sequential patterns and finally these patterns are visualized. Essentially, the main novelty of the proposed method is significance testing for trends that serve as interestingness measures for mined sequential patterns. As a result, two types of trend plots together with glyph-based sequence charts are provided to trainers for determining the progresses of their athletes based on time periods of several months. Beside the trainers, this algorithm is also useful for amateur athletes usually preparing without trainers.