Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletes
This addresses the problem of ensuring objectivity and fairness in judging race walking faults for referees and athletes, though it is incremental as it builds on prior sensor-based methods.
The study tackled automatic fault detection in race walking by developing a non-contact system using pose estimation and machine learning, achieving over 90% average accuracy in detecting faults from smartphone videos, including comparisons between an Olympic medalist and university athletes.
Automatic fault detection is a major challenge in many sports. In race walking, referees visually judge faults according to the rules. Hence, ensuring objectivity and fairness while judging is important. To address this issue, some studies have attempted to use sensors and machine learning to automatically detect faults. However, there are problems associated with sensor attachments and equipment such as a high-speed camera, which conflict with the visual judgement of referees, and the interpretability of the fault detection models. In this study, we proposed a fault detection system for non-contact measurement. We used pose estimation and machine learning models trained based on the judgements of multiple qualified referees to realize fair fault judgement. We verified them using smartphone videos of normal race walking and walking with intentional faults in several athletes including the medalist of the Tokyo Olympics. The validation results show that the proposed system detected faults with an average accuracy of over 90%. We also revealed that the machine learning model detects faults according to the rules of race walking. In addition, the intentional faulty walking movement of the medalist was different from that of university walkers. This finding informs realization of a more general fault detection model. The code and data are available at https://github.com/SZucchini/racewalk-aijudge.