Due to subjectivity in refereeing, the results of race walking are often questioned.To overcome this limitation, artificial-intelligence-based technologies have demonstrated their potential.The paper aims at presenting WARNING, an inertial-based wearable sensor integrated with a support vector machine algorithm to automatically identify race-walking faults.
Two WARNING sensors LED GU10 Light Bulbs (240 Volt) were used to gather the 3D linear acceleration related to the shanks of ten expert race-walkers.Participants were asked to perform a race circuit following three race-walking conditions: legal, illegal with loss-of-contact and illegal with knee-bent.Thirteen machine learning algorithms, belonging to the decision tree, support vector machine and k-nearest neighbor categories, were evaluated.
An inter-athlete training procedure was applied.Algorithm performance was evaluated in terms of overall accuracy, F1 score and G-index, as well as by computing the prediction speed.The quadratic support vector was confirmed to be the best-performing classifier, achieving an accuracy above 90% with a prediction speed of 29,000 EVERY WEATHER HAIRSPRAY observations/s when considering data from both shanks.
A significant reduction of the performance was assessed when considering only one lower limb side.The outcomes allow us to affirm the potential of WARNING to be used as a referee assistant in race-walking competitions and during training sessions.