Sensor-based jump detection and classification with machine learning in trampoline gymnastics

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

Abstract

The task of the judge of difficulty in trampoline gymnastics is to check the elements and difficulty values entered on the competition cards and the difficulty of each element according to a numeric system. To do this, the judge must count all somersaults and twists for each jump during a routine and thus record the difficulty of the routine. This assessment can be automated with the help of inertial measurement units (IMUs) and facilitate the judges’ task during the competition. Currently, there is no known reliable method for the automated detection and recognition of the various elements to determine the difficulty of an exercise in trampoline gymnastics. Accordingly, a total of 2076 jumps and 50 different jump types were recorded over the course of several training sessions. In the first instance, 10 different jump types were used to train different machine learning (ML) models. Eight ML models were used for the automatic jump classification. Supervised learning approaches include a naive classifier, deep feedforward neural network, convolutional neural network, k‑nearest neighbors, Gaussian naive Bayes, support-vector classification, gradient boosting classifier, and stochastic gradient descent. When all classifiers were compared for accuracy, i.e., how many jumps were correctly detected by the ML model, the deep feedforward neural network and the convolutional neural network provided the best matches with 96.4 and 96.1%, respectively. The findings of this study will help to develop the automated classification of sensor-based data to support the judge and, simultaneously, for automated training logging.

Details

OriginalspracheEnglisch
Seiten (von - bis)187-195
Seitenumfang9
Fachzeitschrift German journal of exercise and sport research : Sportwissenschaft
Jahrgang53
Ausgabenummer2
PublikationsstatusVeröffentlicht - 8 Dez. 2022
Peer-Review-StatusJa

Externe IDs

Mendeley c4e804d6-c2a7-3340-be2b-cb50db90ed08
Scopus 85143656255
ORCID /0000-0001-8107-2775/work/142253561

Schlagworte

Forschungsprofillinien der TU Dresden

Fächergruppen, Lehr- und Forschungsbereiche, Fachgebiete nach Destatis

Schlagwörter

  • Automated classification, Inertial measurement unit, Machine learning, Training, Trampoline, Automated classification, Inertial measurement unit, Machine learning, Training, Trampoline