Antonio Greco, Gennaro Percannella, Pierluigi Ritrovato, Alessia Saggese, Mario Vento
{"title":"A deep learning based system for handwashing procedure evaluation.","authors":"Antonio Greco, Gennaro Percannella, Pierluigi Ritrovato, Alessia Saggese, Mario Vento","doi":"10.1007/s00521-022-07194-5","DOIUrl":null,"url":null,"abstract":"<p><p>Hand washing preparation can be considered as one of the main strategies for reducing the risk of surgical site contamination and thus the infections risks. Within this context, in this paper we propose an embedded system able to automatically analyze, in real-time, the sequence of images acquired by a depth camera to evaluate the quality of the handwashing procedure. In particular, the designed system runs on an NVIDIA Jetson Nano <math><msup><mrow></mrow> <mi>TM</mi></msup> </math> computing platform. We adopt a convolutional neural network, followed by a majority voting scheme, to classify the movement of the worker according to one of the ten gestures defined by the World Health Organization. To test the proposed system, we collect a dataset built by 74 different video sequences. The results achieved on this dataset confirm the effectiveness of the proposed approach.</p>","PeriodicalId":35733,"journal":{"name":"Annual review of nursing research","volume":"3 1","pages":"1-16"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022899/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual review of nursing research","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s00521-022-07194-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
Hand washing preparation can be considered as one of the main strategies for reducing the risk of surgical site contamination and thus the infections risks. Within this context, in this paper we propose an embedded system able to automatically analyze, in real-time, the sequence of images acquired by a depth camera to evaluate the quality of the handwashing procedure. In particular, the designed system runs on an NVIDIA Jetson Nano computing platform. We adopt a convolutional neural network, followed by a majority voting scheme, to classify the movement of the worker according to one of the ten gestures defined by the World Health Organization. To test the proposed system, we collect a dataset built by 74 different video sequences. The results achieved on this dataset confirm the effectiveness of the proposed approach.
期刊介绍：
This landmark annual review has provided nearly three decades of knowledge, insight, and research on topics critical to nurses everywhere. The purpose of this annual review is to critically examine the full gamut of literature on key topics in nursing practice, including nursing theory, care delivery, nursing education, and the professional aspects of nursing. Past volumes of ARNR have addressed critical issues such as: •Pediatric care •Complementary and alternative health •Chronic illness •Geriatrics •Alcohol abuse •Patient safety •Rural nursing •Tobacco use
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