United States

As the field of robotics develops, so do the methods of interfacing with and controlling those robots. Many modern robots can be communicated with using commands that require neither speech nor touch. The two main motivations behind this trend are the assistance of people with disabilities and the desire for more natural Human-Robot Interaction (HRI). In allowing nonverbal communication with robots, accessibility of the systems increases to allow more people to interact with and benefit from robotics systems. Additionally, nonverbal communication provides more natural communication, which can lead to many benefits in HRI. This paper
provides an overview of existing technologies in touchless and nonverbal human-robot interfaces; it presents the most prevalent interface methods, their uses, and limitations. Discussions are provided on future directions for research.

PETRA 2021

Interfacing with Robots without the use of Touch or Speech

**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.

workshop paper

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

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.

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

Over the past few years, the ubiquitous usage of internet to broadcast information worldwide, has proved to be one of the best methods in making people aware about their surroundings. This has also led towards storage of vast amount of data in user interactive websites. Several news channels apart from live streaming, are using internet in such ways to convey their information. And these methods have not only benefited people to acquire regular updates but have also impacted their lives in many ways during the world awakening pandemic like COVID-19. Currently, in this pandemic situation, many leaders including federal and state government officials have shown great concern towards society by providing statements such as preventive measures, which were regularly recorded and elaborated in the form of stories by the news channels in their own websites. But, in general, the type of statement given by a leader will always affect people’s opinion and behavior, which also happened during this pandemic. There were variations in number of cases of COVID-19 based upon a region leader’s statements as per timeline and the people’s modulating lifestyle there. This kind of relationship has motivated us towards analyzing the COVID-19 data based on sentiment and emotion involved in leader’s statements, giving rise to an idea of web scraping to obtain their statements and stories from news channels. The main aim of this paper is to enlighten about different techniques and libraries used for web scraping, some challenges while designing a web scraper and finally arrive at an efficient methodology using them to extract news headlines and stories and create a new dataset that can be used in sentiment and emotion analysis. In future work, we will present the results of our experiments with the downloaded datasets.


Web Scraping of COVID-19 News Stories to Create Datasets for Sentiment and Emotion Analysis

The world’s population is ageing rapidly while cognitive impairment in older ages is more evident than ever. Hence, the health and wellbeing among the elderly is an issue of paramount importance. The speedy growth of technology and its application within healthcare has advanced computerized cognitive interventions, which aim to delay the onset and potential progression of cognitive decline. Consequently, the traditional cognitive interventions have made a swift to novel multimedia systems to promote independent, active and healthy ageing. The primary objective of the present study is to introduce an innovative computerized cognitive gamified training and social activation program for health and mildly cognitive impaired older adults. The platform is designed as an extension and adaptation of a previous computerized cognitive training program called ‘SOCIABLE’. Evaluation of the newly designed platform through a single-arm feasibility study followed by a full randomized double-blind controlled trial is recommended as future steps for this piece of work.

Design, method and apparatus of a computerized cognitive gamified training program targeting to maintain and improve cognitive health in older adults

A highly user-centered design approach for developing a mobile health app for pain management (PainApp)

Over the course of the coming decades, the societal goal of maximizing the number of older adults who experience positive trajectories of ageing has been established. To this end, innovative technologybased interventions tailored to older adults’ functional levels and focused on healthy lifestyles are considered imperative. The purpose of this work is to present a cluster of digital solutions aims at supporting and maintaining healthy and independent living for older adults, who face permanently or temporarily reduced functionality and capabilities.

Assistive Technologies for Supporting Wellbeing of Older Adults

Inferring the emotional state of an individual by viewing his/her facial expression seems to be present in all human cultures. Numerous
studies have shown that various changes in facial muscles determine the resulting facial expression. The analysis of images of faces
expressing emotional states promises to contribute to the quantification
of the claimed observations. Here, we use a suite of AI (artificial
intelligence) algorithms, along with ML (maximum likelihood) estimated distributions to quantify the shift in facial expression from
“neutral”→“fear” and “pain”→“pleasure”. The images are single
frames of five emotional states (neutral, fear, pain, pleasure, laugh)
expressed by actors and actresses in BDSM videos. We extract a feature vector for each image, dimension-reduce these feature vectors
by mapping them onto a two-dimensional manifold and calculate
the norms of the normalized displacement vectors for each emotional pair. We then find that the ML distributions of the norms are
Gamma-distributed and that the modes for each pair are different
for both males and females. We use Wilks lambda to determine
significance. We find that the distributions for the females are significantly different, but not for the males. The methodology we present
here has widespread applications: monitoring the emotional states
of humans in various settings; among these: determining whether
participants in BDSM and similar videos are indeed volunteering
their participation or are victims of criminal activity.

Visual Analysis of Emotions Using AI Image-Processing Software: Possible Male/Female Differences between the Emotion Pairs “Neutral”–“Fear” and “Pleasure”–“Pain”

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.

Interfacing with Robots without the use of Touch or Speech

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

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Interfacing with Robots without the use of Touch or Speech

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Next from PETRA 2021

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

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Reflecting upon Participatory Design in Human-Robot Collaboration for People with Motor Disabilities: Challenges and Lessons Learned from Three Multiyear Projects

Stay up to date with the latest Underline news!

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