Humanoid robots as teaching assistants in an arab school

© 2019 Association for Computing Machinery. The proliferation of educational robots has led to an investigation of suitable roles that humanoids robots can take in the classroom. In the recent past, the focus has been on humanoids being used in student focused roles or as peer learners. Coupled with the seemingly absence of any case studies of educational robots in the United Arab Emirates (UAE) or the Arab world, we present a study where we employed the Nao robot as a teaching assistant in a local primary school in Abu Dhabi, UAE. The Nao robot was used to revise a topic in Mathematics and its efficacy in comparison to a human teaching assistant was measured through pre and post test scores, facial expressions and indirect verbal responses. Our results showed that while there no significant differences in test scores, the children were much more engaged when interacting with the Nao robot. We conclude with a positive outlook towards the implementation of humanoid robots in UAE classrooms

COVID-19 global risk : expectation vs. reality

Background and Objective: COVID-19 has engulfed the entire world, with many countries struggling to contain the pandemic. In order to understand how each country is impacted by the virus compared with what would have been expected prior to the pandemic and the mortality risk on a global scale, a multi-factor weighted spatial analysis is presented. Method: A number of key developmental indicators across three main categories of demographics, economy, and health infrastructure were used, supplemented with a range of dynamic indicators associated with COVID-19 as independent variables. Using normalised COVID-19 mortality on 13 May 2020 as a dependent variable, a linear regression (N = 153 countries) was performed to assess the predictive power of the various indicators. Results: The results of the assessment show that when in combination, dynamic and static indicators have higher predictive power to explain risk variation in COVID-19 mortality compared with static indicators alone. Furthermore, as of 13 May 2020 most countries were at a similar or lower risk level than what would have been expected pre-COVID, with only 44/153 countries experiencing a more than 20% increase in mortality risk. The ratio of elderly emerges as a strong predictor but it would be worthwhile to consider it in light of the family makeup of individual countries. Conclusion: In conclusion, future avenues of data acquisition related to COVID-19 are suggested. The paper concludes by discussing the ability of various factors to explain COVID-19 mortality risk. The ratio of elderly in combination with the dynamic variables associated with COVID-19 emerge as more significant risk predictors in comparison to socio-economic and demographic indicators

Personalized robot interventions for autistic children : an automated methodology for attention assessment

We propose a robot-mediated therapy and assessment system for children with autism spectrum disorder (ASD) of mild to moderate severity and minimal verbal capabilities. The objectives of the robot interaction sessions is to improve the academic capabilities of ASD patients by increasing the length and the quality of their attention. The system uses a NAO robot and an added mobile display to present emotional cues and solicit appropriate emotional responses. The interaction is semi-autonomous with minimal human intervention. Interaction occurs within an adaptive dynamic scenario composed of 13 sections. The scenario allows adaptive customization based on the attention score history of each patient. The attention score is autonomously generated by the system and depends on face attention and joint attention cues and sound responses. The scoring system allows us to prove that the customized interaction system increases the engagement and attention capabilities of ASD patients. After performing a pilot study, involving 6 ASD children, out of a total of 11 considered in the clinical setup, we conducted a long-term study. This study empirically proves that the proposed assessment system represents the attention state of the patient with 82.4% accuracy

Generation of Human-Like Movement from Symbolized Information

An important function missing from current robotic systems is a human-like method for creating behavior from symbolized information. This function could be used to assess the extent to which robotic behavior is human-like because it distinguishes human motion from that of human-made machines created using currently available techniques. The purpose of this research is to clarify the mechanisms that generate automatic motor commands to achieve symbolized behavior. We design a controller with a learning method called tacit learning, which considers system–environment interactions, and a transfer method called mechanical resonance mode, which transfers the control signals into a mechanical resonance mode space (MRM-space). We conduct simulations and experiments that involve standing balance control against disturbances with a two-degree-of-freedom inverted pendulum and bipedal walking control with humanoid robots. In the simulations and experiments on standing balance control, the pendulum can become upright after a disturbance by adjusting a few signals in MRM-space with tacit learning. In the simulations and experiments on bipedal walking control, the robots realize a wide variety of walking by manually adjusting a few signals in MRM-space. The results show that transferring the signals to an appropriate control space is the key process for reducing the complexity of the signals from the environment and achieving diverse behavior

The new norm : Computer Science conferences respond to COVID-19

The disruption from COVID-19 has been felt deeply across all walks of life. Similarly, academic conferences as one key pillar of dissemination and interaction around research and development have taken a hit. We analyse an interesting focal point as to how conferences in the area of Computer Science have reacted to this disruption with respect to their mode of offering and registration prices, and whether their response is contingent upon specific factors such as where the conference was to be hosted, its ranking, its publisher or its original scheduled date. To achieve this, we collected metadata associated with 170 conferences in the area of Computer Science and as a means of comparison; 25 Psychology conferences. We show that conferences in the area of Computer Science have demonstrated agility and resilience by progressing to an online mode due to COVID-19 (approximately 76% of Computer Science conferences moved to an online mode), many with no changes in their schedule, particularly those in North America and those with a higher ranking. Whilst registration fees have lowered by an average of 42% due to the onset of COVID-19, conferences still have to facilitate attendance on a large scale due to the logistics and costs involved. In conclusion, we discuss the implications of our findings and speculate what they mean for conferences, including those in Computer Science, in the post-COVID-19 world

Emotion and memory model for social robots: a reinforcement learning based behaviour selection

In this paper, we propose a reinforcement learning (RL) mechanism for social robots to select an action based on users’ learning performance and social engagement. We applied this behavior selection mechanism to extend the emotion and memory model, which allows a robot to create a memory account of the user’s emotional events and adapt its behavior based on the developed memory. We evaluated the model in a vocabulary-learning task at a school during a children’s game involving robot interaction to see if the model results in maintaining engagement and improving vocabulary learning across the four different interaction sessions. Generally, we observed positive findings based on child vocabulary learning and sustaining social engagement during all sessions. Compared to the trends of a previous study, we observed a higher level of social engagement across sessions in terms of the duration of the user gaze toward the robot. For vocabulary retention, we saw similar trends in general but also showing high vocabulary retention across some sessions. The findings indicate the benefits of applying RL techniques that have a reward system based on multi-modal user signals or cues

Exoskeletons with virtual reality, augmented reality and gamification for stroke patients' rehabilitation : systematic review

Background: Robot-assisted therapy has become a promising technology in the field of rehabilitation of post-stroke patients with motor disorders. Motivation during the rehabilitation process is a top priority for a majority of stroke survivors. With the advancement in technology, there has been the introduction of Virtual Reality, Augmented Reality, customizable games or a combination thereof that aid robotic therapy in retaining or increasing the interests of patients to keep performing the exercises. However, there are gaps in evidence regarding the transition from clinical rehabilitation to home-based therapy and it calls for an updated synthesis of literature showcasing this trend. The present review proposes a categorization of these studies according to technologies used by them and also details research in upper limb and lower limb applications. Objective: The goal of this work was to review the practices and technologies implemented for the rehabilitation of post-stroke patients. It aims to assess the effectiveness of exoskeleton robotics in conjunction with any of the three technologies, Virtual Reality, Augmented Reality or Gamification for improving activity and participation in post-stroke survivors. Methods: A systematic search of the literature on exoskeleton robotics applied with any of the three technologies, Virtual Reality, Augmented Reality or Gamification, was performed in the databases namely; MEDLINE (Medical Literature Analysis and Retrieval System Online, or MEDLARS Online), EMBASE (Excerpta Medica database), Science Direct & The Cochrane Library. Exoskeleton based studies that did not include any VR, AR or gamification elements were excluded and publications from the year 2010 to 2017 were included. Results in the form of improvements in patients were also recorded and taken into consideration in finding the effectiveness of therapy on patients. Results: Thirty studies were identified based on the inclusion criteria that included randomised controlled trials as well as explorative research pieces. There was a total of around 385 participants across the studies. Use of technologies such as Virtual Reality/Augmented Reality/Gamification based Exoskeletons are capable of filling the transition from clinical to home-based settings. Our analysis showed that there were in general improvements in the motor deficiency for patients using the novel interfacing techniques with exoskeletons. This categorization of studies helps in understanding the scope of rehabilitation therapies that can be successfully arranged for home-based rehabilitation. Conclusions: Future studies are necessary to explore various types of customizable games required to retain or increase the motivation of patients going through the therapy individually

Global and temporal COVID-19 risk evaluation

The COVID-19 pandemic has caused unprecedented crisis across the world, with many countries struggling with the pandemic. In order to understand how each country is impacted by the virus and assess the risk on a global scale we present a regression based analysis using two pre-existing indexes, namely the Inform and Infectious Disease Vulnerability Index, in conjunction with the number of elderly living in the population. Further we introduce a temporal layer in our modeling by incorporating the stringency level employed by each country over a period of 6 time intervals. Our results show that the indexes and level of stringency are not ideally suited for explaining variation in COVID-19 risk, however the ratio of elderly in the population is a stand out indicator in terms of its predictive power for mortality risk. In conclusion, we discuss how such modeling approaches can assist public health policy

Technology, privacy, and user opinions of COVID-19 mobile apps for contact tracing : systematic search and content analysis

Background: Many countries across the globe have released their own COVID-19 contact tracing apps. This has resulted in the proliferation of several apps that used a variety of technologies. With the absence of a standardized approach used by the authorities, policy makers, and developers, many of these apps were unique. Therefore, they varied by function and the underlying technology used for contact tracing and infection reporting. Objective: The goal of this study was to analyze most of the COVID-19 contact tracing apps in use today. Beyond investigating the privacy features, design, and implications of these apps, this research examined the underlying technologies used in contact tracing apps. It also attempted to provide some insights into their level of penetration and to gauge their public reception. This research also investigated the data collection, reporting, retention, and destruction procedures used by each of the apps under review. Methods: This research study evaluated 13 apps corresponding to 10 countries based on the underlying technology used. The inclusion criteria ensured that most COVID-19-declared epicenters (ie, countries) were included in the sample, such as Italy. The evaluated apps also included countries that did relatively well in controlling the outbreak of COVID-19, such as Singapore. Informational and unofficial contact tracing apps were excluded from this study. A total of 30,000 reviews corresponding to the 13 apps were scraped from app store webpages and analyzed. Results: This study identified seven distinct technologies used by COVID-19 tracing apps and 13 distinct apps. The United States was reported to have released the most contact tracing apps, followed by Italy. Bluetooth was the most frequently used underlying technology, employed by seven apps, whereas three apps used GPS. The Norwegian, Singaporean, Georgian, and New Zealand apps were among those that collected the most personal information from users, whereas some apps, such as the Swiss app and the Italian (Immuni) app, did not collect any user information. The observed minimum amount of time implemented for most of the apps with regard to data destruction was 14 days, while the Georgian app retained records for 3 years. No significant battery drainage issue was reported for most of the apps. Interestingly, only about 2% of the reviewers expressed concerns about their privacy across all apps. The number and frequency of technical issues reported on the Apple App Store were significantly more than those reported on Google Play; the highest was with the New Zealand app, with 27% of the reviewers reporting technical difficulties (ie, 10% out of 27% scraped reviews reported that the app did not work). The Norwegian, Swiss, and US (PathCheck) apps had the least reported technical issues, sitting at just below 10%. In terms of usability, many apps, such as those from Singapore, Australia, and Switzerland, did not provide the users with an option to sign out from their apps. Conclusions: This article highlighted the fact that COVID-19 contact tracing apps are still facing many obstacles toward their widespread and public acceptance. The main challenges are related to the technical, usability, and privacy issues or to the requirements reported by some users