Surveillance Rover to Maintain Social Distancing in Crowded Areas

COVID-19 is making a huge impact both in terms of the economy and human lives. Many lost their lives due to COVID-19 which is found in most of the nations. The number of positive symptoms is increasing rapidly all over the world. To safeguard us from the virus, some protocols have been addressed by WHO in which people has to wear a mask and make a social distancing when moved in public. Therefore, social distancing places an important role in preventing us from the spread of the diseases. The minimum distance between to be maintained is informed at 6 feet informed by the health organizations. When people gathered on a group social distancing could not be maintained even if manual or any kind of technology implemented. Temperature measurement on mass gathering was also a tedious process where the monitoring is essential. Multiple methods such as thermal cameras, temperature sensors for monitoring the personnel has not been efficient. In the proposed work to monitor the social distancing between the persons an ultrasonic sensor is placed to detect the obstacle and an IR sensor to make the rover move. An encoder is used to calculate the distance based on the rpm of the wheel. Based on this input the distance is checked within this limit the obstacle is detected, an alert signal is made using the buzzer. A thermal sensor is used to measure the temperature of the person and an LCD display shows the temperature of the person and distance between obstacles. The proposed system has resulted in identifying the distance and helps in reducing the spread during the pandemic situation. © 2023 IEEE.

Monitoring Suspects of COVID-19 in Compact Residential Area Using Machine Learning Based on Lockdown Analytical Impact

The most prominent symptom in corona-infected person is increased body temperature. Population in India is very high, and it is very difficult to provide medical services to everyone at a time. This paper presents how can we reduce the pressure on medical service providers in current pandemic situation and stop the community spreading. Initially in the paper, impact of lockdown on COVID-19 situation is observed using the Python language. According to that two lockdown applied in the country at initial level, the growth rate of pandemic was under control. But as the lockdown rules were relaxed, quick hike in corona cases was noted. On the basis of this result proposed model is designed, if one scans the temperature of different domain areas at regular intervals from above the surface using zero touch technology, i.e., drone camera installed with thermal sensor, the increased temperature reading can help screening the infected hot spots. The proposed idea is to collect thermal data through thermal sensors and process these data using visualization of images/videos and quantitative methods. Machine learning and artificial intelligence are used to process biometric features of corona suspects and collect personal detail of corona suspect from the databases. The residential area of infected person is marked with red color, and monitoring the movement of the infected person is done with help of machine learning. With the taken of timely action, not only the spreading can be controlled but also the patients can get appropriate attention and treatment on time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Healthcare Monitoring Based on Cloud Computing: Case Study (Corona Virus)

COVID-19 (Corona Virus) is a very transmissible infection that has the world’s attention lastly. This virus can be transmitted from any patient to other people by direct contact,it could be spread if the person sneezes or coughs. It is also spread by touching a surface that contains a virus.In this paper, the intelligent healthcare system is proposed, that integrates cloud computing technologies. Its architecture utilizes smart sensors, microcontrollers, C ++ and the Php programming languages to program the Arduino and cluad. The temperature sensor, with an accuracy of 0.5 degrees, and the sound sensor are used in this paper. These sensors will send the data of the patient to the SQL database and cloud after encryption it by RSA algorithm. Finally the data transport to the monitor and the doctor or nurse can monitor the patients from web browser from any device such as smart phones, ipads, or PC computers from any place remotely without touching the patients. This architecture can reduce the transmission of infections by touch, in addition to monitoring all cases in a hospital without potential. The system tracks the state of patients and delivers timely, reliable, and high quality healthcare with minimum cost. The presented architecture will be useful in diagnosis research and healthcare systems.The computing cloud is necessary for highly challenging architectures like intelligent healthcare. The concept of intelligent architecture healthcare systems was to provide patient surveillance at all times, in addition to real time connectivity. In addition, the sensors are required for this architecture to be applied to any patient afflicted with any disease. Therefore, by adding the sensors are required.

Design and implementation of a Social Distance Vest for Covid19 prevention (SODIV-COP)

According to research, it is discovered that amongst the Covid19 preventive measures, social distance is easily neglected especially in a public setting such as markets, trading centers, social and political gatherings. Furthermore, according to World Health Organization (WHO), not observing social distance is one the major ways that the corona virus is being transmitted. Hence, working on a vest that can help to remind individuals and alert them in cases where they are not observing social distance. The Social Distance Vest for Covid19 Prevention, is based on Arduino Uno microcontroller board, D6T-44L-06 thermal sensor which detects the presence of a person, HC-SR04 Ultrasonic sensor that calculates the distance from where the person is standing and an alert/warning system that is composed of a Light Emitting Diode and a buzzer. Finally, the whole system is mounted on a reflective vest. The prototype vest works perfectly, in that it is able to detect a person which was not possible in the previous covid 19 distance vests which had only messages, and it is able to calculate the distance from where humans are standing and finally, triggers an alarm in a case where the person is standing at a distance of less than 1 m. The varying temperature ranges were in an array form and from 35 to 38° Celsius it detected the obstacle to be a human and had some ranges of distance 0.334 m measured by the ultrasonic sensor. Key applications of the prototypes are in crowded places like stadiums hospitals and schools. © 2022 The Authors

"Oxygen Pheriwala:" An innovative model for SARS-CoV-2 screening in resource-limited settings.

The second wave of SARS-CoV-2 infection came as a hypoxic emergency and situation became worse in rural India, where undiagnosed COVID-19 patients died without any diagnosis or intervention. The primary aim of this innovative model was the early diagnosis of suspected SARS-CoV-2 cases, providing empirical treatment and timely referral to appropriate COVID care facilities. Fever was measured with infrared thermometer and oxygen saturation level with pulse oximeter. A total of 8203 people were screened, of which 274 persons were febrile and 69 (25%) were hypoxic too. Sixty-four out of 69 (93%) patients turned COVID-19 positive on reverse transcription-polymerase chain reaction. At the end of 3 weeks, 48/64 (75%) patients were successfully discharged. This model can be easily implemented in resource-limited regions to identify and prioritize the patients not only in this pandemic but also in outbreak of other communicable diseases.

Spatial effect of anti-COVID measures on land surface temperature (LST) in urban areas: A case study of a medium-sized city

This case study investigates the magnitude and nature of the spatial effect generated by the anti-COVID measures on land surface temperature (LST) in the city of Targu Mures (Marosvasarhely), Romania. The measures were taken by the Romanian government during the state of emergency (March 16 - May 14, 2020) due to the SARS-CoV-2 coronavirus pandemic. The study shows that - contrary to previous studies carried out on cities in China and India in most of the urban areas of Marosvasarhely LST has increased in the period of health emergency in 2020 concerning the large average of the years 2000-2019. Remote sensing data from the MODIS and the Landsat satellites show. that MODIS data, having a moderate spatial (approximately 1 km) but good temporal resolution (daily measurements), show a temperature increase of +0.78 degrees C, while Landsat data, having better spatial (30 m) but lower temporal resolution, show an even greater increase, +2.36 degrees C in the built-up areas. The difference in temperature increase is mainly due to the spatial resolution difference between the two TIR band sensors. The LST anomaly analysis performed with MODIS data also shows a positive anomaly increase of 1 degrees C. However, despite this increase, with the help of the hotspot-coldspot analysis of the Getis-Ord Gi* statistic we were able to identify 46 significant coldspots that showed a 1- 2 degrees C decrease of LST in April 2020 compared to the average of the previous years in April. Most of these coldspots correspond to factory areas, public transport epicenters, shopping centers, industrial polygons. and non-residential areas. This shows that anti-COVID measures in the medium-sized city of Marosvasarhely had many effects on LST in particular areas that have links to the local economy, trade. and transport. Paired sample t-test for areas identified with LST decrease shows that there is a statistically significant difference in the average LST observed before and after anti-COVID measures were applied. MODIS-based LST is satisfactory for recognizing patterns and trends at large or moderate geographical scales. However, for a hotspot-coldspot analysis of the urban heat islands, it is more suitable to use Landsat data.

Development of Contactless Individual Management System with Covid - 19 Preemptive Measures

The significance of social distancing and non-contact habits was emphasized by the Covid-19 pandemic. Even after the pandemic, everyone should adhere to the same hygiene procedures. Preventive measures must be implemented prior to the individuals' return. These include identifying people's presence and monitoring their health. This research focuses on using sensor fusion and deep learning technology to create a contactless individual management system. It is capable of carrying out the attendance routine without compromising the precautionary measures. Persons can be identified without removing the mask by employing random Quick Response (QR) code recognition. While recognising the QR, the system will double-verify the individual by identifying the Media Access Control (MAC) address of the user's mobile Bluetooth at the backend. Then the system employs a pre-trained deep learning model to detect masks. The Convolutional Neural Network (CNN) technique produces a deep learning model that can distinguish between Faces with and without masks. The system then monitors the body temperature with an Infrared (IR) temperature sensor followed by dispensing sanitizer. The response for the entire procedure will be updated in both the person's mobile application and the Management Authority. © 2022 IEEE.

BALANCING THE COVID-19 PANDEMIC IMPACT ON THE MAINTENANCE OF HAEMODIALYSIS VASCULAR ACCESS

BACKGROUND AND AIMS: The new coronavirus disease, named by World Health Organization (WHO) as COVID-19 brought great challenges to patients with endstage renal disease (ESRD). In general, ESRD patients have a higher number of comorbidities and are at risk for the severe presentation of this disease. As a lifeline of haemodialysis patients, vascular access (VA) care has a profound impact on the patient's quality of dialysis and life but the ideal management of VA during the pandemic is currently unknown. Many centres differed on their approach and referral criteria to minimize COVID-19 risk but the impact on VA and patient survival is unknown. In this multicentre retrospective cross-sectional study, we analysed the impact of the pandemic on VA maintenance in Vascular Access Centres of Nephrocare Portugal. METHOD: The authors collected VA data from haemodialysis patients treated at three Vascular Access Centers of NeproCare Portugal from January 2019 to July 2021 and compared the year before and after the pandemic. RESULTS: Of the 14 352 haemodilaysis patients included, with a mean age of 68 ± 14 years, 7.161 procedures were analysed. A total of 4086 endovascular procedures and 3075 surgeries were performed from January 2019 to July 2021 in the three national vascular access centres of Nephrocare. Blood flow decrease measured by the blood temperature sensor BTMΔ (Blood Temperature Monitor), Fresenius Medical Care, Bad Homburg, Germany was the most frequent motive of referral to an endovascular procedure before and after the pandemic (P .221). During both waves, physical examination and clinical signs were the most affected motives of referral, followed by a rebound significantly increase (P .058). Thrombosis remained stable during the lockdown followed by a non-significant trend to increase. New vascular access creation was the most frequent motive to send a patient to surgery before and after the pandemic (P .480). Fistula and prosthesis thrombosis also didn't significantly increase as a motive of referral to a VAC (P .221 and 1.0 respectively). Angioplasty without stent followed by thrombolysis was the most frequent types of endovascular procedures before and during the pandemic without significant differences (P .430). Surgical thrombectomy followed by fistula creation were the most frequent types of surgical procedures before and during the pandemic without significant differences (P .683). During the first wave, there was a decrease in procedures without possibility to intervention (P .037) with posterior significant rebound increase. Although there was a trend to a decrease in intervention, the number and types of procedures didn't significantly change before and during the pandemic even after separating different centres. Additionally, the number of hospital admissions related to vascular access also didn't significantly change (P .368). CONCLUSION: With the implementation of proactive infection control measures, it was possible to maintain proper monitoring, surveillance and VA care without significantly increasing the rate of thrombosis and minimizing related hospital admissions of haemodialysis patients.

An artificial intelligence based algorithm for prevention of Covid

The goal to promote human limits is for Artificial Intelligence (AI). It takes a posture on public administrations, represents the increasing availability of regaining clinical data and the rapid creation of intelligent strategies. The need to stress the need to use AI in the fight against the COVID-19 crisis. The paper outlines the main role played by Ai technologies in this unprecedented war and introduces a survey of AI methods used for multiple purposes in the fight against the outbreak of COVID-19. This paper also explains how the body temperature and coughing of the incoming person are assessed and whether the incoming person has not a protective facial mask. Should either of the above tests disqualify the participant, an alarming device invokes the local officials;the entrant may otherwise enter the premises after his/her hand has been sanitized. © 2022 Author(s).

Real-Time Mild and Moderate COVID-19 Human Body Temperature Detection Using Artificial Intelligence

Day by day, COVID-19 cases are increasing all over the world. Without a proper vaccine to control the disease, the only solution so far is social distancing and identifying the disease at an early stage. In more than 80% of confirmed cases there are only mild symptoms, like fever;therefore, we have to check the body temperature of people in public places like shopping malls, hotels, airports, schools and universities, etc. In this chapter we propose contactless temperature (CT) measurement utilizing thermal (TS), RGB, and 3D sensors. We also propose a fever location camera (FLC) which gives high-quality estimates from up to 2 or 3 meters away. Using cutting-edge technology, the fever location framework (FLF) estimates the internal heat level of individuals in groups of three or four by checking and filtering their face temperatures. If a high temperature is identified, the framework sounds an alarm or cautioning message, which has propelled face recognition technology. The framework, which is based on the investigation of face temperature, guarantees high-quality estimations. Using facial recognition (FR) likewise limits false readings;for example, an individual carrying a hot beverage. Using a devoted programming stage, a signal can be set to inform us of unusual temperatures. It can precisely recognize the facial temperature (FT) of numerous individuals quickly, with an exactness of ≤ 0.3 °C. Temperature recognition range can be set with the ideal location of up to 3 meters in the framework highlighted by a bi-directional double-channel (infrared light + visible light) camera utilizing a heated sensor and low level interference signals. © 2021 Scrivener Publishing LLC.

Performance Evaluation of Infrared Thermal Sensors based on Distance, Room Temperature, and Physical Activity on Objects

Fever has a sensitivity of 54% and specificity of 67% against SARS-CoV-2 so it can be used to determine whether a person is infected with COVID-19. To prevent the spread of the virus in public places, a body temperature screening process using an infrared thermometer is required. The two sensors that are commonly used as infrared thermometers are the MLX90614 and AMG8833 because of their high temperature range and good accuracy. However, the measurement results can be affected by the measuring distance, room temperature, and physical activity in the human body. Therefore, an infrared thermometer with two sensors arranged in overlay was designed to evaluate the performance of infrared thermal sensors based on measuring distances (15, 30, 40, and 60 cm), 2 rooms (26.4 oC and 30 oC), and physical activity on the object. The results in the 30 oC room at 15 – 40 cm show measured temperature above 36 oC for both sensors, while in the 26.4 oC room it decreased up to 35.32 oC. At 15 cm in a 26.4 oC room, the measured temperature results are the closest to the reference values with a difference of less than 0.3 oC for the MLX90614 sensor, while at 60 cm, the results are the furthest from the reference values also it has larger difference value, which is 0.21 oC for the MLX90614 and 1.01 oC for the AMG8833. In conclusion, the MLX90614 sensor is better than the AMG8833 sensor because its outputs are closer to the reference values. ©2021 IEEE

A Wearable Non-Contact Temperature Detector for Early Detection of Fever

The world is currently facing a pandemic attack of Coronovirus disease (COVID-19), which is an infectious disease causing mild to moderate respiratory illness. One of the most common and early symptoms of COVID-19 is fever which is the reaction to a disease-specific stimulus causing the increase of the human body temperature. To slow down the transmission of the COVID-19 virus, the public is required have their body temperature measured when entering any premises. The current common method of monitoring the human body temperature uses the application of non-contact infrared thermometer (NCIT) and is only limited for stationary conditions within short distances and mostly suitable for indoor premises. The available technology to detect human body temperature for longer distances uses the thermal camera which is costly and large. Thus, it is challenging to detect anyone with high body temperature is non-stationary conditions, at longer distances, especially outdoor. The project proposes an innovation to the current practice, for a wearable noncontact temperature detector device which is portable. The wearable non-contact temperature detector embeds a thermal sensor and a microcontroller to a normal hat. It is able to detect objects with higher temperature (37.5°C) within 1 meter radius of 60° angle view in stationary and non-stationary conditions. The wearable device communicates via Bluetooth to a mobile device to display the detected temperature and notifies the user via alert message and alarm for high temperature detection. Display of the object's thermal image is also available with a resolution of 8 times 8 pixel. The wearable non-contact temperature detector is able to achieve 99% accuracy of temperature measurement for detection distance of up to 70 cm for indoor and within 20 cm for outdoor when tested with normal temperature subject and high temperature object and compared with the actual temperature detected via a commercial NCIT device. © 2021 IEEE.

Thermochromic Polymeric Films for Applications in Active Intelligent Packaging-An Overview.

The need for passive sensors to monitor changes in temperature has been critical in several packaging related applications. Most of these applications involve the use of bar codes, inks and equipment that involve constant complex electronic manipulation. The objective of this paper is to explore solutions to temperature measurements that not only provide product information but also the condition of the product in real time, specifically shelf-life. The study will explore previously proposed solutions as well as plans for modified approaches that involve the use of smart polymers as temperature sensors.

Towards human distance estimation using a thermal sensor array.

Human distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people's privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of ± 0.2  m in continuous-based estimation and 96.8 % achieved-accuracy in discrete distance estimation.

Thermal Sensors for Contactless Temperature Measurements, Occupancy Detection, and Automatic Operation of Appliances during the COVID-19 Pandemic: A Review.

The worldwide spread of COVID-19 has forced us to adapt to a new way of life made of social distancing, avoidance of physical contact and temperature checks before entering public places, in order to successfully limit the virus circulation. The role of technology has been fundamental in order to support the required changes to our lives: thermal sensors, in particular, are especially suited to address the needs arisen during the pandemic. They are, in fact, very versatile devices which allow performing contactless human body temperature measurements, presence detection and people counting, and automation of appliances and systems, thus avoiding the need to touch them. This paper reviews the theory behind thermal detectors, considering the different types of sensors proposed during the last ten years, while focusing on their possible employment for COVID-19 related applications.