Touchless Hand Sanitizer Mobile Robot Application: Survey and Dataset Preparation for Deep Learning Recognition

Abstract

The COVID-19 pandemic spreads throughout the world, many sanitizer dispenser devices included robots designed to minimize human contact and as less as possible to minimize the speared of this pandemic or any germs among humans. In many positions, these sanitizers dispensers are important to sterilize and help people from infection such as patients and medical staff in hospitals, teachers, and students in schools or universities, etc. In this paper, many studies of sanitizer dispenser devices including mobile robot applications for touchless hand sanitizers especially in the coronavirus pandemic have been explained. The major aim of this survey is to compare and contrast many previous survey methodologies. This study covered the introduction to robot applications, hand sanitizers dispenser device including a mobile robot, and object detection-based deep learning techniques that recognizes people. Object detection with the deep learning method is discussed because it is the most important feature of the hand sanitizer device, which is based on a mobile robot application. Furthermore, in this work, the proposed block diagram of dataset processing has been instigated using the mini-computer Raspberry Pi version 4, Raspberry Pi camera system, and LabelImg graphical tool. These dataset outputs files of each image (label.xml and image.jpg) are prepared and classed into 7 classes to use as inputs to the next future work of deep learning. In future work, we want to create a mobile robot hand sanitizer that recognizes the people using a deep learning approach. The python programing language has been used. The contribution of this work is to create, annotate and prepare the dataset for Deep Learning while the previous related works depended on existing dataset that taken from website .