<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Automatic fault detection is a major challenge in many sports. In race walking, judges visually detect faults according to the rules. Hence, automatic fault detection systems will help a training of race walking without experts’ visual judgement. 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 judges, and the interpretability of the fault detection models. In this study, we proposed an automatic fault detection system for non-contact measurement. We used pose estimation and machine learning models trained based on the judgements of multiple qualified judges 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 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. In addition, the intentional faulty walking movement of the medalist was different from that of other walkers. This finding informs realization of a more general fault detection model.</p>
</abstract>

Automatic fault detection is a major challenge in many sports. In race walking, judges visually detect faults according to the rules. Hence, automatic fault detection systems will help a training of race walking without experts’ visual judgement. 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 judges, and the interpretability of the fault detection models. In this study, we proposed an automatic fault detection system for non-contact measurement. We used pose estimation and machine learning models trained based on the judgements of multiple qualified judges 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 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. In addition, the intentional faulty walking movement of the medalist was different from that of other walkers. This finding informs realization of a more general fault detection model.

Automatic Detection of Faults in Simulated Race Walking from a Fixed Smartphone Camera

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Automatic fault detection is a major challenge in many sports. In race walking, judges visually detect faults according to the rules. Hence, automatic fault...

Automatic Detection of Faults in Simulated Race Walking from a Fixed Smartphone Camera

Publicado en línea: 09 mar 2024

Páginas: 22 - 36

DOI: https://doi.org/10.2478/ijcss-2024-0002

Palabras clave
MACHINE LEARNING, MOTION ANALYSIS, RACE-WALKING, POSE ESTIMATION

© 2024 Tomohiro Suzuki et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Automatic Detection of Faults in Simulated Race Walking from a Fixed Smartphone Camera

Publicado en línea: 09 mar 2024

Páginas: 22 - 36

DOI: https://doi.org/10.2478/ijcss-2024-0002

Palabras claveMACHINE LEARNING, MOTION ANALYSIS, RACE-WALKING, POSE ESTIMATION

© 2024 Tomohiro Suzuki et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
MACHINE LEARNING, MOTION ANALYSIS, RACE-WALKING, POSE ESTIMATION