Security Surveillance Bot for Remote Observation During Pandemics

Nayak, Asha Rashmi; Ayyar, Saishree.C.S.; Aiswarya, O.; Ch, Mahitha; Mohankumar, N.

doi:10.1109/icces48766.2020.9137935

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…The mobile robot based on Raspberry Pi microcontroller is also utilised for the surveillance inside the hospital [ 56 ]. The robot is controlled and moved by the user while transmitting the live video footage continuously.…”

Section: Robots In Covid-19mentioning

confidence: 99%

A literature survey of the robotic technologies during the COVID-19 pandemic

Wang

2021

Journal of Manufacturing Systems

195

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Since the late 2019, the COVID-19 pandemic has been spread all around the world. The pandemic is a critical challenge to the health and safety of the general public, the medical staff and the medical systems worldwide. It has been globally proposed to utilise robots during the pandemic, to improve the treatment of patients and leverage the load of the medical system. However, there is still a lack of detailed and systematic review of the robotic research for the pandemic, from the technologies’ perspective. Thus a thorough literature survey is conducted in this research and more than 280 publications have been reviewed, with the focus on robotics during the pandemic. The main contribution of this literature survey is to answer two research questions, i.e. 1) what the main research contributions are to combat the pandemic from the robotic technologies’ perspective, and 2) what the promising supporting technologies are needed during and after the pandemic to help and guide future robotics research. The current achievements of robotic technologies are reviewed and discussed in different categories, followed by the identification of the representative work’s technology readiness level. The future research trends and essential technologies are then highlighted, including artificial intelligence, 5 G, big data, wireless sensor network, and human-robot collaboration.

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Section: Robots In Covid-19mentioning

confidence: 99%

A literature survey of the robotic technologies during the COVID-19 pandemic

Wang

2021

Journal of Manufacturing Systems

195

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“…For that purpose, we use the chest X-Ray imaging method to study the case in detail where studies revealed that deeper analysis of chest X-Ray found the ability to identify the disease correctly, the specificity of which reaches 90% [9]. Big data, deep learning and artificial intelligence [43] are used in innumerable fields of study, analysis and prediction in the recent times, be it the healthcare sector [10,6,34], security [12,19], recommendation systems [3], surveillance bots [44] or dealing with microscopy images [14] and cardiovascular problems [41]. In this paper, we discuss the various deep learning models and build models that could distinctly classify an X-ray into regular pneumonia, Covid-19 pneumonia or a patient without pneumonia.…”

Section: Introduction Covid-19 (Corona Virus Disease 2019mentioning

confidence: 99%

Distinguishing Pneumonia and COVID-19: Utilizing Computer Vision to Mimic Clinician Efficacy

Saha

Gourisaria

Harshvardhan

2021

2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS)

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Corona Virus has caused a disruption to the normalcy in the world and require thorough patient management. Looking at the present scenario and the kind of pandemic that it has turned out to be, this paper aims at providing an enhancement to the RT-PCR way of testing and uses the chest X-Rays to detect the presence and severity of the corona virus in a body to successfully differentiate between pneumonia and the Covid-19. The main motive is to help doctors and medical experts with an advance aid to the nursing of critically affected patients. This is feasible because the X-Ray machines are widely available throughout the country and they can assist in the advanced detection of the disease. Around 6000 images of the three kinds of chest X-Rays of patients with pneumonia, Covid, as well as completely normal patients, were used in the process. The paper concludes with the explicit comparison of all the models and their results. Primarily, a simple CNN model was opted for the scrutiny and then later on VGG-16, VGG-19, ResNet50, MobileNet and MobileNetV2 pre-trained models were utilised for anatomizing Covid-19 with respect to pneumonia and normal cases. In case of CNN, the maximum accuracy that was attained was 95.30% whereas, for the VGG-16, VGG-19, ResNet50, MobileNet and MobileNetV2, the maximum accuracies in correctly predicting the diseases were 95.63% , 96.02% , 94.82% , 95.23% and 93.39% respectively.

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