StanceScorer: A Data Driven Approach to Score Badminton Player

In recent times, wearable devices have gained immense popularity for IoT applications, especially for sports analytics. Recent works in sports analytics primarily focuses on improving a player's performance and help devise a winning strategy based on the player's strengths and weaknesses which is also the objective of this paper. In a racquet-based sports, it is often assumed that handling the racquet majorly influences the performance of the players, however, the stance and the posture of the player are of greater importance. A perfect posture and stance allow a player to play a stroke efficiently by directing the shuttle to strategic spots. Therefore, it helps to utilize less energy and make it difficult for the opponent to return the shot and eventually score a point. Hence, we hypothesize that the performance of a player equally correlates with the stance and the efficiency of handling the racquet. In this paper, we propose to analyze the stance of the player based on the shot played. In an attempt to do so, we propose a data-driven approach to evaluate a player's performance based on the player's stance or posture. First, we employ both shallow learning and deep learning algorithms to classify the strokes which is then used to analyse the stance. Secondly, we propose a distance based methodology to compare the stance of an intermediate or a novice player with that of a professional player. Further, we learn the error between the professional player's stance with that of a participant and propose a scoring methodology. To evaluate our proposed methodology, we deploy a sensor network comprising of inertial measurement units (IMU) sensors on the dominant wrist and palm; and both the legs. We collect the data from a professional player, an intermediate player and a novice player for 12 different frequently played shots and evaluate our proposed methodology with this dataset.