- Tytuł:
- Recognition of sports exercises using inertial sensor technology
- Autorzy:
-
Krutz, Pascal
Rehm, Matthias
Schlegel, Holger
Dix, Martin - Powiązania:
- https://bibliotekanauki.pl/articles/30148258.pdf
- Data publikacji:
- 2023
- Wydawca:
- Polskie Towarzystwo Promocji Wiedzy
- Tematy:
-
human activity recognition
machine learning
neural networks
classifier - Opis:
- 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 is 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 is implemented. Realised functionalities include 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 are used for the training of classifiers and artificial neural networks (ANN). These are iteratively optimised in their corresponding hyper parameters for the data structure to be learned. The most reliable models are 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 is also displayed graphically, allowing statements to be made about potential causes of incorrect assignments. In this context, especially the transition areas between the classes are 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 %.
- Źródło:
-
Applied Computer Science; 2023, 19, 1; 152-163
1895-3735
2353-6977 - Pojawia się w:
- Applied Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł