Deep learning for gesture recognition in gym training performed by a vision-based augmented reality smart mirror
(Deep Learning für die Erkennung von Gesten im Fitnessstudio durch einen visuellen Augmented-Reality-Spiegel)
This paper illustrates the development and the validation of a smart mirror for sport training. The application is based the skeletonization algorithm MediaPipe and runs on an embedded device Nvidia Jetson Nano equipped with two fisheye cameras. The software has been evaluated considering the exercise biceps curl. The elbow angle has been measured by both MediaPipe and the motion capture system BTS (ground truth), and the resulting values have been compared to determine angle uncertainty, residual errors, and intra-subject and inter-subject repeatability. The uncertainty of the joints` estimation and the quality of the image captured by the cameras reflect on the final uncertainty of the indicator over time, highlighting the areas of improvements for further developments.
© Copyright 2022 ISBS Proceedings Archive (Michigan). Northern Michigan University. Veröffentlicht von International Society of Biomechanics in Sports. Alle Rechte vorbehalten.
| Schlagworte: | |
|---|---|
| Notationen: | Naturwissenschaften und Technik Freizeitsport |
| Tagging: | deep learning Augmented Reality |
| Veröffentlicht in: | ISBS Proceedings Archive (Michigan) |
| Sprache: | Englisch |
| Veröffentlicht: |
Liverpool
International Society of Biomechanics in Sports
2022
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| Online-Zugang: | https://commons.nmu.edu/isbs/vol40/iss1/87/ |
| Jahrgang: | 40 |
| Heft: | 1 |
| Seiten: | Article 87 |
| Dokumentenarten: | Kongressband, Tagungsbericht |
| Level: | hoch |