Abstract
The WHO has recommended ‘frequent hand washing’ as means to curtail the spread of ‘Public Health Emergencies of International Concern.’ Improvement in the seven step hand wash compliance rate has been shown to reduce the spread of hospital acquired infections. Most of the hand hygiene compliance identification systems developed over the years have restricted their focus on tracking the movement of healthcare workers to and from the hand wash station. However, these systems have failed to detect if the seven step hand wash were performed or not. We proposed and implemented a computer vision and artificial intelligence based system to detect seven steps of the hand wash process. We used the Visual Geometry Group-16 (VGG-16) network combined with the Long Short Term Memory (LSTM) module as a classification system. We developed the hand wash database of 3000 videos to train and optimize the parameters of the VGG16-LSTM model. The optimized model detects different steps of handwash with high accuracy and near real time detection ability. This system will prove to be useful for improving hand wash compliance rate and to curb the spread of infectious diseases.
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Acknowledgement
We would like to thank the Center of Excellence in Signal and Image Processing (COE-S&IP), College of Engineering, Pune for providing us with all the required resources to carry out this work.
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Gawali, P., Latke, R., Bartakke, P., Shinde, A. (2021). Deep Learning Based Lens for Mitigating Hospital Acquired Infections. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_20
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DOI: https://doi.org/10.1007/978-981-16-1086-8_20
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