DEVELOPING REAL-TIME FACE MASK DETECTION WITH FACIAL TEMPERATURE MEASURE FOR COVID-19 INDOOR MONITORING SYSTEM

The coronavirus (COVID-19) is the latest pandemic that hit human health in 2019. Wear a face mask in public areas to decrease the spread of the coronavirus. This work presents real-time face mask detection with facial temperature measures for the COVID-19 indoor monitoring system. Detecting people using ultrasonic sensors, face mask detection, and facial temperature measure using Grid-Eye Sensor are three modules applied in the proposed system. We also evaluated the proposed monitoring system in the real environment and confirmed the accuracy of 98.8% of mask detection.

Covid-19 Indoor Monitoring System with Human Identification

Covid-19 has become the worst of all pandemics seen with over 63,549,184 active cases recorded globally as on January 16, 2021. India alone has 10,553,529 active cases till date. It has brought on a new era and new way of life-rather reality where in safety has become a must for survival. Form the statistics it is observed that the number of active cases and health rate have only increased abruptly post lockdown even after implementing the guidelines proposed by the government to tackle the problem of the hour. since fever is developed in most communicable diseases, body temperature monitoring is effective for such disease, body temperature monitoring is effective for the prevention of outbreak of such diseases. Covid-19 being one of such, temperature records of symptoms and it is checked prior to allowing a person in, In social stratum it is done by person one should hold the temperature gun at entrance, and the sensor records is often inaccurate. This method also involves two people standing close, which itself is a problem in social-distancing rule. In this paper to design and develop Covid-19 indoor safety Monitoring with human identification using iot based devices. social distancing- since we use hands-free temperature sensing method which records the accurate temperature as well as human identification. Keywords: IOT, Indoor-safety, Human Identification with high Temperature.

Improving the robustness of a service robot for continuous indoor monitoring: An incremental approach

To move out of the lab, service robots must reveal a proven robustness so they can be deployed in operational environments. This means that they should function steadily for long periods of time in real-world areas under uncertainty, without any human intervention, and exhibiting a mature technology readiness level. In this work, we describe an incremental methodology for the implementation of an innovative service robot, entirely developed from the outset, to monitor large indoor areas shared by humans and other obstacles. Focusing especially on the reliability of the fundamental localization system of the robot in the long term, we discuss all the incremental software and hardware features, design choices, and adjustments conducted, and show their impact on the performance of the robot in the real world, in three distinct 24-h long trials, with the ultimate goal of validating the proposed mobile robot solution for indoor monitoring.

An Ad Hoc Movement Monitoring Algorithm for Indoor Tracking During Examinations

Academic dishonesty includes cheating in examinations tests and any academic assignments, plagiarism, fabrication of information or citations, facilitation of acts of academic studies by others, unauthorized possession of examination materials, and tampering with the academic work of other researchers or scholars. These acts can be done before, during, and after the actual examination. This paper proposes a holistic algorithm for setting up an ad-hoc monitoring system to track candidates who leave the examination hall during the examination for either toilet breaks, medical breaks, or otherwise. Since most examinations are held indoors it is especially difficult to use existing GPS and GPRS based methods. We submit a proposal to deploy an indoor monitoring system based on the RSS of carefully placed receivers as they communicate with active tags held by the candidates. This paper outlines an algorithm that is used to place the receivers in such a way as to avoid any blind spots which may be utilized as cheating spots. The algorithm was deployed into a testbed system which was 100% foul proof. The proposed algorithm can be used to set up the deployment of the monitoring system in any environment where a clearly defined path is identified.

Indoor monitoring of heavy metals and NO2 using active monitoring by moss and Palmes diffusion tubes

Background Indoor pollution is a real threat to human health all over the world. Indoor pollution derives from indoor sources (e.g. smoking, gas stoves, coated furniture) as well as from outdoor sources (e.g. industries, vehicles). Long-term monitoring measurements in indoor environments are missing to a large extent due to a lack of simple to operate measuring devices. Mosses proved well as biomonitors in hundreds of studies. Nevertheless, indoor use has been extremely scarce. Therefore, this study aimed to determine indoor and outdoor pollution by active biomonitoring using moss as well as NO2 samplers to analyse outdoor and indoor levels of pollution. We exposed moss (Pleurozium schreberi) for 8 weeks indoors and outdoors in 20 households in the city of Girona, Spain. Al, Cr, Cu, Zn, Sn, Cd, Pb, Mo, and Sb were analysed by moss-samplers. Additionally, NO2 was measured with Palmes diffusion tubes. Results Compared to the pre-exposure analysis, concentrations of almost all elements both on indoor and outdoor mosses increased. Except for Cd, all metals and NO2 had, on average, higher concentrations in outdoor mosses than at corresponding indoor sites. However, some 20% of the samples showed inverse patterns, thus, indicating both indoor and outdoor sources. Indoor/outdoor correlations of elements were not significant, but highest for markers of traffic-related pollution, such as Sn, Sb, and NO2. The wide range of indoor–outdoor ratios of NO2 exemplified the relevance of indoor sources such as smoking or gas cooking. Though mostly excluded in this study, a few sites had these sources present. Conclusions The study at hand showed that moss exposed at indoor sites could be a promising tool for long-time biomonitoring. However, it had also identified some drawbacks that should be considered in future indoor studies. Increments of pollutants were sometimes really low compared to the initial concentration and therefore not detectable. This fact hampers the investigation of elements with low basic element levels as, e.g. Pt. Therefore, moss with real low basic levels is needed for active monitoring, especially for future studies in indoor monitoring. Cloned material could be a proper material for indoor monitoring yet never was tested for this purpose.

Trio-Sensor Wireless Networked System for Indoor Monitoring and Fire Detection in Human Habitats

Wireless sensor networks (WSN) in recent times have become a dominant technology in environmental monitoring and in early detection of conditions like fire outbreaks in order to save lives and properties. In this article, the design and development of a WSN fire detection system is presented. For accurate and reliable detection of fire, three sensors (i.e., MQ-2, DS18B20, and KY-026) are employed in addition to other WSN key components for the respective detection of gas, temperature, and infrared, which are major fire parameters. The system further has a battery monitoring scheme for the observation of the energy resource of the nodes and to ensure their viability. A developed graphic user interface is deployed for the remote monitoring of the entire system and its data. Results obtained from the different test conditions using combustible solid, inflammable liquid, and gas show that the system performed optimally. The developed system is recommended for residential and small office applications.