Hand Washing Gesture Recognition Using Synthetic Dataset
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| Publicat a: | Journal of Imaging vol. 11, no. 7 (2025), p. 208-227 |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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|---|---|---|---|
| 001 | 3233226470 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2313-433X | ||
| 024 | 7 | |a 10.3390/jimaging11070208 |2 doi | |
| 035 | |a 3233226470 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Rüstem, Özakar |u Deparment of Computer Engineering, Faculty of Engineering and Architecture, Erzurum Technical University, Erzurum 25100, Turkey | |
| 245 | 1 | |a Hand Washing Gesture Recognition Using Synthetic Dataset | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Hand hygiene is paramount for public health, especially in critical sectors like healthcare and the food industry. Ensuring compliance with recommended hand washing gestures is vital, necessitating autonomous evaluation systems leveraging machine learning techniques. However, the scarcity of comprehensive datasets poses a significant challenge. This study addresses this issue by presenting an open synthetic hand washing dataset, created using 3D computer-generated imagery, comprising 96,000 frames (equivalent to 64 min of footage), encompassing eight gestures performed by four characters in four diverse environments. This synthetic dataset includes RGB images, depth/isolated depth images and hand mask images. Using this dataset, four neural network models, Inception-V3, Yolo-8n, Yolo-8n segmentation and PointNet, were trained for gesture classification. The models were subsequently evaluated on a large real-world hand washing dataset, demonstrating successful classification accuracies of 56.9% for Inception-V3, 76.3% for Yolo-8n and 79.3% for Yolo-8n segmentation. These findings underscore the effectiveness of synthetic data in training machine learning models for hand washing gesture recognition. | |
| 653 | |a Washing | ||
| 653 | |a Computer-generated imagery | ||
| 653 | |a Datasets | ||
| 653 | |a Classification | ||
| 653 | |a Neural networks | ||
| 653 | |a Open source software | ||
| 653 | |a Color imagery | ||
| 653 | |a Public health | ||
| 653 | |a Sensors | ||
| 653 | |a Gesture recognition | ||
| 653 | |a Machine learning | ||
| 653 | |a Synthetic data | ||
| 700 | 1 | |a Gedikli Eyüp |u Deparment of Computer Engineering, Faculty of Computer and Information Sciences, Trabzon University, Trabzon 61300, Turkey; eyupgedikli@trabzon.edu.tr | |
| 773 | 0 | |t Journal of Imaging |g vol. 11, no. 7 (2025), p. 208-227 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3233226470/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3233226470/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3233226470/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |