Capacity Estimation from Environmental Audio Signals Using Deep Learning

Estimating the capacity of a room or venue is essential to avoid overcrowding that could compromise people’s safety. Having enough free space to guarantee a minimal safety distance between people is also essential for health reasons, as in the current COVID-19 pandemic. Already existing systems for automatic crowd counting are mostly based on image or video data, and some of them, using deep learning architectures. In this paper, we study the viability of already existing Deep Learning Crowd Counting systems and propose new alternatives based on new network architectures containing convolutional layers, exclusively based on the use of environmental audio signals. The proposed architecture is able to infer the actual capacity with a higher accuracy in comparison to previous proposals. Consequently, conclusions from the accuracy obtained with out approach are drawn and the possible scope of deep learning based crowd counting systems is discussed. © 2022, Springer Nature Switzerland AG.

Design of an IoT Safety Distance Monitoring Device for COVID-19

The COVID-19 pandemic arises in the year 2020 that leads to almost more than 100,000,000 confirmed cases happened in worldwide and yet, the cases are continuing to rise now. One of the common methods that used to prevent the COVID-19 is through keeping the social distancing between each other. However, people do not aware of the importance of applying social distancing rules. A low-cost solution is urgently needed for contact tracing and remind the users to keep the social distancing with the others. An IoT-based safety distance monitoring (ISDM) device is proposed to monitor safety distance, remind people to always stay alert and keep the data of the users that violated the social distancing rules for contact tracing. ISDM is a lightweight wearable wristwatch. Bluetooth Low Energy technique is used to send and receive information such as body temperature and contact tracing information. Fever is the most common symptom of COVID-19 patients. ISDM can operate either online or offline. The health and contact tracing information are stored in a local Micro-SD card storage when offline and data will be uploaded to the cloud server for further analysis once ISDM is connected. The detailed contact tracing information can be used to trace the potential COVID-19 patients with close contact with the COVID-19 patient. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Distansya: An IoT Platform to Physical Distancing Provision Notification

The research paper proposes a cheap implementation of social distancing measures that could be developed through cheap IOT sensors and existing pathogen blocking devices such as plastic face shields to combat the spread of COVID-19. The prototype system is fitted into the hard-plastic face shield with IoT sensors such as ultrasonic sensors, buzzer, and a battery controlled through an Arduino to be able to alert the surroundings of the area through the connected buzzer which activates if the user's interactions with others are detected to be too physically close and not in observance with the one-meter safety distance protocol appropriate for COVID-19 practice of social distancing. © 2021 ACM.

High-performance Detection and Predication Safety System using HUAWEI Atlas 200 DK AI Developer Kit

The problem that the world has faced in 2020 is adapting to the current situations. A sense of safety is compromised with our inclusion in public places and social activities. People's participation in activities has decreased, while students and workers have learned how to work from a distance, allowing obstacles to arise. With the movement towards life before COVID-19 comes a challenge, which is being reassured that going back to our older habits keeps us safe. The proposed system provides a real-time video feed captured by a connected camera used to monitor people in a given environment. If the distance between any two detected people is below the safe distance limit, it recognizes a violation. The system also checks whether any person is not wearing a face mask or has it lowered. To alert the responsible person with any of the previous scenarios, a notification is sent to a webpage with the image of the violation. Closed places like universities, schools, factories, and offices can make use of this real-time solution to make sure people are complying and to offer assurance of the safety of the place. This can improve their participation in activities such as resuming their work. © 2022 IEEE.

Unreachable The physical impossibility of digital displays

"Unreachable" is an artwork that explores the feelings caused by the absence of human closeness and social gathering, and the forced distancing imposed by the spread of the highly contagious virus COVID-19. Digital technologies allowed the continuity of some activities adapted to this new reality. Nevertheless, although we can see and hear through them, they do not fulfill the human need for touch nor replace the sensations of being physically close to other people and of live cultural and leisure moments. Through the display of images and videos that avoid the participants when they cross the minimum safety distance, this artwork addresses the feelings of the impossibility of physically reaching something important and meaningful through digital devices, even though these may simulate a sense of closeness. © 2021 Copyright held by the owner/author(s).