Video Analysis on Handwashing Movement for the Completeness Evaluation

Prakasa, Esa; Sugiarto, Bambang

doi:10.1109/icramet51080.2020.9298612

Cited by 3 publications

(1 citation statement)

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“…MobileNet-V2 and Xception networks were used for classification of 7 gestures. Prakasa and Sugiarto [7] used skin color to detect hands in a 6 gesture hand-washing process. They used a Convolutional Neural Network (CNN) to classify six gestures.…”

Section: Vision-based Workmentioning

confidence: 99%

Evaluation of Hand Washing Procedure Using Vision-Based Frame Level and Spatio-Temporal Level Data Models

Ozakar

Gedikli

2023

Electronics

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Hand hygiene is obligatory for all healthcare workers and vital for patient care. During COVID-19, adequate hand washing was among recommended measures for preventing virus transmission. A general hand-washing procedure consisting several steps is recommended by World Health Organization for ensuring hand hygiene. This process can vary from person to person and human supervision for inspection would be impractical. In this study, we propose computer vision-based new methods using 12 different neural network models and 4 different data models (RGB, Point Cloud, Point Gesture Map, Projection) for the classification of 8 universally accepted hand-washing steps. These methods can also perform well under situations where the order of steps is not observed or the duration of steps are varied. Using a custom dataset, we achieved 100% accuracy with one of the models, and 94.23% average accuracy for all models. We also developed a real-time robust data acquisition technique where RGB and depth streams from Kinect 2.0 camera were utilized. Results showed that with the proposed methods and data models, efficient hand hygiene control is possible.

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Section: Vision-based Workmentioning

confidence: 99%