RECOGNITION OF SPORTS EXERCISES USING INERTIAL SENSOR TECHNOLOGY
Pascal Krutz
pascal.krutz@mb.tu-chemnitz.deChemnitz University of Technology (Germany)
https://orcid.org/0009-0002-1962-983X
Matthias Rehm
Chemnitz University of Technology (Germany)
https://orcid.org/0000-0001-7354-3856
Holger Schlegel
Chemnitz University of Technology (Germany)
Martin Dix
Chemnitz University of Technology, Fraunhofer IWU (Germany)
https://orcid.org/0000-0002-2344-1656
Abstract
Supervised learning as a sub-discipline of machine learning enables the recognition of correlations between input variables (features) and associated outputs (classes) and the application of these to previously unknown data sets. In addition to typical areas of application such as speech and image recognition, fields of applications are also being developed in the sports and fitness sector. The purpose of this work was to implement a workflow for the automated recognition of sports exercises in the Matlab® programming environment and to carry out a comparison of different model structures. First, the acquisition of the sensor signals provided in the local network and their processing were implemented. The functionalities to be realised included the interpolation of lossy time series, the labelling of the activity intervals performed and, in part, the generation of sliding windows with statistical parameters. The preprocessed data were used for the training of classifiers and artificial neural networks (ANN). These were iteratively optimised in their corresponding hyper parameters for the data structure to be learned. The most reliable models were finally trained with an increased data set, validated and compared with regard to the achieved performance. In addition to the usual evaluation metrics such as F1 score and accuracy, the temporal behaviour of the assignments was also displayed graphically, which enabled statements to be made about potential causes for incorrect assignments. In this context, especially the transition areas between the classes were detected as erroneous assignments as well as exercises with insufficient or clearly deviating execution. The best overall accuracy achieved with ANN and the increased dataset was 93.7 %.
Supporting Agencies
Keywords:
machine learning, neural networks, classifier, human activity recognitionReferences
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Authors
Pascal Krutzpascal.krutz@mb.tu-chemnitz.de
Chemnitz University of Technology Germany
https://orcid.org/0009-0002-1962-983X
Authors
Matthias RehmChemnitz University of Technology Germany
https://orcid.org/0000-0001-7354-3856
Authors
Holger SchlegelChemnitz University of Technology Germany
Authors
Martin DixChemnitz University of Technology, Fraunhofer IWU Germany
https://orcid.org/0000-0002-2344-1656
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