United States

In this paper we report on CO2 Meter, a do-it-yourself carbon dioxide measuring device for the classroom. Part of the current measures for dealing with the SARS-CoV-2 pandemic is proper ventilation in indoor settings. This is especially important in schools with students coming back to the classroom even with high incidents rates. Static ventilation patterns do not consider the individual situation for a particular class. Influencing factors like the type of activity, the physical structure or the room occupancy are not incorporated. Also, existing devices are rather expensive and often provide only limited information and only locally without any networking. This leaves the potential of analysing the situation across different settings untapped. Carbon dioxide level can be used as an indicator of air quality, in general, and of aerosol load in particular. Since, according to the latest findings, SARS-CoV-2 can be transmitted primarily in the form of aerosols, carbon dioxide may be used as a proxy for the risk of a virus infection. Hence, schools could improve the indoor air quality and potentially reduce the infection risk if they actually had measuring devices available in the classroom. Our device supports schools in ventilation and it allows for collecting data over the Internet to enable a detailed data analysis and model generation. First deployments in schools at different levels were received very positively. A pilot installation with a larger data collection and analysis is underway.

PETRA 2021

CO2 Meter: A do-it-yourself carbon dioxide measuring device for the classroom

carbon dioxide monitoring

do-it-yourself

information systems

embedded hardware

classroom

data analysis

sensor networks

education

The 2020 AAPT Virtual Summer Meeting brought together physics educators from around the globe to learn about the latest in physics theory, research methodology and education from leaders in their field. The program featured plenary speakers, sessions, workshops and many special activities.

For more information please visit the 2020 AAPT Virtual Summer Meeting page at: https://www.aapt.org/Conferences/SM2020/index.cfm.


AAPT SM 20

The 2020 AAPT Virtual Summer Meeting brought together physics educators from around the globe to learn about the latest in physics theory, research methodology and education from leaders in their field. 

workshop paper

**About**
 The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

 PETRA (which means “stone” in Greek) addresses important human living needs. From a technological perspective, PETRA motivates and inspires new scientific thrusts, innovations and discoveries to improve living environments, a particularly important issue, in an increasingly crowded and complex world where the need for inclusiveness and connectivity with the rest of the world is key. For the elderly in particular, PETRA addresses the fact that, as people grow older; they will increasingly rely on technology to be able to prolong staying in their homes and enjoy an independent life.

 The PETRA 2021 proceedings will be published in the ACM Digital Library as part of the ACM ICPS program. The ISBN number assigned to PETRA '21 is 978-1-4503-8792-7

The PErvasive Technologies Related to Assistive Environments (PETRA) conference is a highly interdisciplinary conference that focuses on computational and engineering approaches to improve the quality of life and enhance human performance in a wide range of settings, in the workplace, at home, in public spaces, urban environments, and other. Outcomes of this conference have a broad impact in application areas that include, manufacturing, transportation, healthcare, energy systems, security and safety, robotics, biomedicine, environment and conservation, and many others. PETRA brings together very different types of technologies to also address important social and healthcare issues for sensitive populations, such as the elderly, persons suffering from chronic conditions such as Alzheimer’s, Parkinson’s, Cerebral Palsy, and other disabilities or traumas.

COVID-19 pandemic has affected nearly every aspect of life. Observing online the spread of the virus can offer a complementary view to the cases that are daily officially recorded and reported. In this article, we present an approach that exploits information available on social media to predict whether a patient has been infected with COVID-19. Our approach is based on a Bayesian model that is trained using data collected online. Then the trained model can be used for evaluating the possibility that new patients are infected with COVID-19. The experimental evaluation presented shows the high quality of our approach. In addition, our model can be incrementally retrained, so that it becomes more robust in an efficient way.

Prediction of COVID-19 infection based on symptoms and social life using machine learning techniques

Currently, spatial geographic data can be collected for many applications
that involve data on the planet earth. These collected
data typically have coordinates (x, y), or longitude and latitude in
map space, and thus can be located and displayed on maps. Data
alone represents facts and has no meaning on its own but becomes
meaningful when it is associated with application knowledge, such
as elections, crimes, disease, etc. For example, there is no meaning
behind those numbers (1, 23, 125, 355, . . .), yet they are data that
can get meaning when correlated with the total number of cases of
COVID-19 in Texas per day starting on a certain date. Many devices
can provide sequences of object location data over time (GPS in
vehicles or mobile devices, etc.). However, no device can visualize or
display them on its own without a visualization App. Both numeric
and location data are raw data that need to be pre-processed and
cleaned to become meaningful. Currently, collected data is a very
valuable source of information which, after collection, can be processed,
stored, analyzed, and visualized. In this paper, the available
techniques of spatial data visualization will be overviewed. Moreover,
a case study of COVID-19 spatio-temporal data visualization,
using one of the techniques will be demonstrated. The COVID-19
data will be spatially visualized when data on a specific date is
queried for analysis. On the other hand, spatio-temporal visualization
will be displayed when a time series of COVID-19 data is
queried for analysis.

Map Visualization using Spatial and Spatio-Temporal Data: Application to COVID-19 Data

We develop deep learning-based classifiers for Audio Event Detec-tion (AED), attacking them next with some white noise disturbances. We show that an attacker can use such simple disturbances to poten-tially fully avoid detection by AED systems. Prior work has shown that attackers can mislead image classification tasks, however this work focuses on attacks against AED systems, by tampering the audio and not image. This work brings awareness to the designers and manufacturers of AED systems and devices, as these solutions are becoming more ubiquitous by the day.

Attacking Audio Event Detection Deep Learning Classifiers with White Noise

Manifolk is a tool to visualize the output of dimensionality reduction algorithms like t-SNE, PCA etc. One of this tool's main uses is that it de-clutters graphs by plotting data points pertaining to a subset of labels. The subset of labels to be plotted can be selected using the provided checkboxes. A case study on data from a publicly available action recognition dataset like UCF101 shows how this tool can help find outliers. With the rise in self-supervised methods for training deep neural networks, this tool helps researchers better visualize the embeddings learned by the model.

Manifolk: A 3D TSNE Visualizer

"This paper showcases a developed end-user framework for a human-robot collaborative system for common tasks, such as pick and
place. The system is designed for semi-automated pick and place
tasks as well as manual operation making it flexible for multiple
use-case scenarios. The goal of the system is to make the robot
multi-functional, easy to use with a graphical user interface and
to perform common tasks with the help of a human teammate.
Integration with object recognition neural network (YoloV3) and
an RGB-Depth camera helps automate pick and place tasks with a
wide variety of objects"

End-User Framework for Robot Control

Difficulties with appropriate access to public or semi-public toilets for older or disabled people lead to less participation in social activities within this group. We present a smart AI-based toilet concept that uses 3D depth data to automatically pre-configure the height and tilt of a motorized toilet seat to support standing-up and sitting down without the necessity of user interaction. This has the potential to lead to increased autonomy and quality of life.

Smart toilet seat configuration for more autonomy using an AI-based 3D depth sensor

According to the CDC, COVID-19 most commonly spreads during close contact. Although the cleaners are disinfecting the public places where they are expected to have been contacted, it is still difficult to know exactly which areas have been touched and wiped out. We devised combined methods of detecting touched and wiped areas by people, using both thermal and imaging cameras. It detects the changes of temperature when people touched or wiped in about 3 seconds. To improve the accuracy, we utilize object recognition for humans from imaging cameras as a method of filtering. All marks are tracked and calculated, to disclose which areas are still remaining as touched areas, revealing the information to anyone who wants to know.

Methods of identifying touched areas have been wiped properly

Due to COVID-19, online gaming has grown, and in turn, ""toxicity” and cyberbullying have risen. Exhibiting toxicity can impact mood and relationships continuing into post-gaming
sessions. Moreover, experiencing constant harassment from toxic players can lead to depression in others. About 50 million gamers in the United States are kids under the age of 18,
and on these gaming platforms, there are over 150 million adults potentially influencing younger gamers with explicit language and behaviors. One of the greatest problems is that
gamers struggle with self-monitoring and recognizing their state of mind. To combat this issue, Tempr provides a unique approach through self-awareness and positive reinforcement to
help decrease verbal aggression and cyberbullying over time.

Natural Language Processing and Sentiment Analysis for Verbal Aggression Detection; A Solution for Cyberbullying during Live Video Gaming

The amount of people living in severely hazardous air quality conditions is rapidly increasing due to climate change, wildfires and pollution. Many affected people can not afford air purifiers to protect themselves and those that can are unaware of when is the optimal time to activate theirs. In response to these issues, we create an affordable air purifier that alerts users when the optimal time is to activate their filters and is universally accessible.

CO2 Meter: A do-it-yourself carbon dioxide measuring device for the classroom

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Prediction of COVID-19 infection based on symptoms and social life using machine learning techniques

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CO2 Meter: A do-it-yourself carbon dioxide measuring device for the classroom

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Next from PETRA 2021

Prediction of COVID-19 infection based on symptoms and social life using machine learning techniques

Similar lecture

Understanding COVID-19 by modeling it

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