Hand-hygiene is a critical component for safe food handling. In this paper, we apply an iterative engineering process to design a hand-hygiene action detection system to improve food- handling safety. We demonstrate the feasibility of a baseline RGB-only convolutional neural network (CNN) in the restricted case of a single scenario; however, since this baseline system performs poorly across scenarios, we also demonstrate the application of two methods to explore potential reasons for its poor performance. This leads to the development of our hierarchical system that incorporates a variety of modalities (RGB, optical flow, hand masks, and human skeleton joints) for recognizing subsets of hand-hygiene actions. Using hand-washing video recorded from several locations in a commercial kitchen, we demonstrate the effectiveness of our system for detecting hand hygiene actions in untrimmed videos. In addition, we discuss recommendations for designing a computer vision system for a real application.


This article is published under a CC-BY license.

Zhong, C.; Reibman, A.R.; Mina, H.A.; Deering, A.J. Designing a Computer-Vision Application: A Case Study for Hand-Hygiene Assessment in an Open-Room Environment. J. Imaging 2021, 7, 170. https://doi.org/10.3390/jimaging7090170


Activity recognition; deep learning; domain adaptation

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