English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Automatic fault detection is a major challenge in many sports. In racewalking, referees visually judge faults according to the rules. Hence, ensuringobjectivity and fairness while judging is important. To address this issue,some studies have attempted to use sensors and machine learning toautomatically detect faults. However, there are problems associated with sensorattachments and equipment such as a high-speed camera, which conflict with thevisual judgement of referees, and the interpretability of the fault detectionmodels. In this study, we proposed a fault detection system for non-contactmeasurement. We used pose estimation and machine learning models trained basedon the judgements of multiple qualified referees to realize fair faultjudgement. We verified them using smartphone videos of normal race walking andwalking with intentional faults in several athletes including the medalist ofthe Tokyo Olympics. The validation results show that the proposed systemdetected faults with an average accuracy of over 90%. We also revealed that themachine learning model detects faults according to the rules of race walking.In addition, the intentional faulty walking movement of the medalist wasdifferent from that of university walkers. This finding informs realization ofa more general fault detection model. The code and data are available athttps://github.com/SZucchini/racewalk-aijudge.

Automatic detection of faults in race walking from a smartphone camera: a comparison of an Olympic medalist and university athletes