In this paper, we propose a method for the automatic compliance evaluation of the hand washing procedure performed by healthcare providers. The ideal cleaning procedure, as defined by the guidelines of the World Health Organization (WHO), is split into a sequence of ten distinct and specific hand gestures which have to be performed in the proper order. Thus, the conformance verification problem is formulated as the problem of recognizing that at a given time instant a specific gesture is carried out by the subject. The considered recognition problem is faced through a deep neural network inspired to AlexNet that classifies each image providing as output the guessed gesture class that the subject is performing. Images are captured by a depth camera mounted in a top-view position. The performance of the proposed approach has been assessed on a brand new dataset of about 131.765 frames obtained from 74 continuous recording from trained personnel. Preliminary evaluation confirms the feasibility of the approach with a recognition rate at the frame level that is about 77%, and is about 98% when using a mobile window of 1 s. The developed system will be deployed for training students of the medicine course on the surgical hand-washing procedure.
|Titolo:||A System for Controlling How Carefully Surgeons Are Cleaning Their Hands|
|Data di pubblicazione:||2019|
|Appare nelle tipologie:||4.1.1 Proceedings con DOI|