Action Quality Assessment Model Using Specialists' Gaze Location and Kinematics Data-Focusing on Evaluating Figure Skating Jumps.

Action quality assessment (AQA) tasks in computer vision evaluate action quality in videos, and they can be applied to sports for performance evaluation. A typical example of AQA is predicting the final score from a video that captures an entire figure skating program. However, no previous studies have predicted individual jump scores, which are of great interest to competitors because of the high weight of competition. Despite the presence of unnecessary information in figure skating videos, human specialists can focus and reduce information when they evaluate jumps. In this study, we clarified the eye movements of figure skating judges and skaters while evaluating jumps and proposed a prediction model for jump performance that utilized specialists' gaze location to reduce information. Kinematic features obtained from the tracking system were input into the model in addition to videos to improve accuracy. The results showed that skaters focused more on the face, whereas judges focused on the lower extremities. These gaze locations were applied to the model, which demonstrated the highest accuracy when utilizing both specialists' gaze locations. The model outperformed human predictions and the baseline model (RMSE:0.775), suggesting a combination of human specialist knowledge and machine capabilities could yield higher accuracy.

Determinant analysis and developing evaluation indicators of grade of execution score of double axel jump in figure skating.

A figure skating jump score is determined by the sum of the base value based on the difficulty and grade of execution (GOE) that indicates the performance quality. Therefore, performing a high-quality jump to obtain a high GOE is essential to win a competition. However, the relationship between the GOE and kinematic parameters remains unclear. We analysed the horizontal distance, vertical height, and landing speed of double axel jumps in the Ladies' Short Program at the 2019 World Championships. The highest GOE group had significantly larger horizontal distances than the middle and lower groups, while the landing speed and vertical height were not significantly different. A principal component regression analysis was conducted to clarify the contrast between the three variables affecting the GOE. The results showed that greater horizontal distance and landing speed compared to vertical height (component 1) and greater horizontal distance compared to landing speed (component 3) contributed to higher GOE. We divided skaters into four clusters using these two components and provided general GOE acquisition strategies for each cluster. Finally, to apply our results to the industry, we proposed two new evaluation indicators which are highly correlated with the two components and easy to interpret.