Comparative Analysis of Different Systematic Odometry Error Correction Methods on a Differential Drive Indoor Service Robot

The COVID-19 pandemic has led to a surge of interest in research work involving the development of robotic systems that reduce human-to-human interaction, as such a technology can greatly benefit healthcare industries in preventing the spread of highly infectious diseases. An indoor service robot is built and equipped with wheel odometry and a 2D LiDAR. However, the presence of the systematic odometry errors is evident during field testing. Hence, the possibility of minimizing systematic odometry errors is inspected using various methods of calculation, namely: UMBmark, Lee's and Jung's. The methods all use the Bidirectional Square Path test, performed together with ROS. It is found that Jung's method is the most appropriate method showing a 20.4% improvement compared to the uncalibrated dead reckoning accuracy. Moreover, it is found that the presence of slippage, a nonsystematic error, greatly affects the return position errors of the robot. Consequently, it is recommended to improve the design of the wheelbase to minimize the effects of nonsystematic errors. © 2022 IEEE.

Argumentation and Causal Models in Human-Machine Interaction: A Round Trip

In the field of explainable artificial intelligence (XAI), causal models and argumentation frameworks constitute two formal approaches that provide definitions of the notion of explanation. These symbolic approaches rely on logical formalisms to reason by abduction or to search for causalities, from the formal modeling of a problem or a situation. They are designed to satisfy properties that have been established as necessary based on the study of human-human explanations. As a consequence they appear to be particularly interesting for human-machine interactions as well. In this paper, we show the equivalence between a particular type of causal models, that we call argumentative causal graphs (ACG), and argumentation frameworks. We also propose a transformation between these two systems and look at how one definition of an explanation in the argumentation theory is transposed when moving to ACG. To illustrate our proposition, we use a very simplified version of a screening agent for COVID-19. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Implications from Responsible Human-Robot Interaction with Anthropomorphic Service Robots for Design Science

Accelerated by the COVID-19 pandemic, anthropomorphic service robots are continuously penetrating various domains of our daily lives. With this development, the urge for an interdisciplinary approach to responsibly design human-robot interaction (HRI), with particular attention to human dignity, privacy, compliance, and transparency, increases. This paper contributes to design science, in developing a new artifact, i.e., an interdisciplinary framework for designing responsible HRI with anthropomorphic service robots, which covers the three design science research cycles. Furthermore, we propose a multi-method approach by applying this interdisciplinary framework. Thereby, our finding offer implications for designing HRI in a responsible manner. © 2022 IEEE Computer Society. All rights reserved.

Addressing loneliness in the workplace through human-robot interaction: Development and evaluation of a social office robot concept

New work has been a topic for a few years now and the COVID-19 pandemic has brought this trend more into focus, i.e., working remotely became more popular. However, besides various advantages, there is the risk of loneliness in employees, which can negatively affect their work performance and mental health. Research in different domains suggests that social robots could reduce loneliness. Since we were interested in whether and how such findings are transferable to the office context, we developed and tested a concept for a social office robot. More specifically, we first conducted a cultural probes study with white-collar workers to gain information about workplace loneliness and its drivers. Second, we explored design possibilities for a social office robot in a focus group. Based on the results, we created a concrete concept, Luca, which we finally evaluated and optimized with the help of interviews with participants from various industries. The present work contributes to HRI research and practice, e.g., by providing design recommendations for the implementation of a social office robot. Future research could investigate the effectiveness of a social office robot intervention in field studies. Next to implications for research and practice, potential limitations are discussed. © 2023 the author(s), published by De Gruyter, Berlin/Boston 2023.

SanitizerBot: How Human-in-the-Loop Social Robots Can Playfully Support Humans

This paper evaluates a robot that distributed hand-sanitizer over an eight month period (October 2020-June 2021) in public places on the Oregon State University campus. During COVID times, many robots have been deployed in public places as social distancing enforcers, food delivery robots, UV-sanitation robots and more, but few studies have assessed the social situations of these robots. Using the context of robot distributing hand sanitizer, this work explores the benefits that social robots may provide to encouraging healthy human activities, as well as ways in which street-performance inspired approaches and a bit of humor might improve the quality and experience of functional human-robot interactions. After gaining human-in-the-loop deployment experience with a customized interface to enable both planned and improvized responses to human bystanders, we run two sub-studies. In the first, we compare the performance of the robot (moving or still) relative to a traditional hand sanitizer dispenser stick (N=2048, 3 week data collection period). In the second, we evaluate how varied utterance strategies further impact the interaction results (N=185, 2 week data collection period). The robot dramatically outperforms the stick dispenser across all tracked behavioral variables, cuing high levels of positive social engagement. This work finds the utterance design is more complex socially, and offer insights to future robot designers about how to integrate helpful and playful speech into service robot interactions. Finally, across both sub-studies, the work shows that people in groups are more likely to engage with the robot and each other, as well as sanitize their hands. © 2022 IEEE.

Impact of Distance and Movement Speed on the Acceptance of Human-Robot Interaction – Method and First Evaluation

The number of scenarios where an interaction between humans and robots is part of the everyday life increased constantly during the last years. Therefore, it is important to focus on a good interaction between both parts, the humans and the robots, as well as the absence of negative emotions. Especially, emotions like fear and anxiety are of great interest. The presented study focuses on a first concept of measuring these emotions and the acceptance through a multidimensional approach. A simple handover task was chosen for the collaboration. Different motion speeds of the robot as well as distances between the robot and the human were considered. Moreover, the impact of two different interaction heights, at face level or at chest level, was examined. In addition to the subjective assessment of the participants, psychophysiological parameters (cardiovascular and electrodermal activity) were recorded during the human-robot interaction. The concept was first evaluated with a number of four participants, limited by governmental restrictions due to the current COVID-19 pandemic situation. The results proof the success of the chosen procedure. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Snapshot of Research Issues in Service Robots

A service (social) robot is defined as the Internet of Things (IoT) consisting of a physical robot body that connects to one or more Cloud services to facilitate human-machine interaction activities to enhance the functionality of a traditional robot. Many studies found that anthropomorphic designs in robots resulted in greater user engagement. Humanoid service robots usually behave like natural social interaction partners for human users, with emotional features such as speech, gestures, and eye-gaze, referring to the users’ cultural and social background. During the COVID-19 pandemic, service robots play a much more critical role in helping to safeguard people in many countries nowadays. This paper gives an overview of the research issues from technical and social-technical perspectives, especially in Human-Robot Interaction (HRI), emotional expression, and cybersecurity issues, with a case study of gamification and service robots. © 2022, Springer Nature Switzerland AG.

Human-Machine Interaction for Monitoring COVID-19 Internet Data in Russia and the World

We develop a human-machine interaction via dashboard for COVID-19 data visualization in the regions of Russia and the world. In particular, it includes an adaptive-compartmental multi-parametric model of the epidemic spread, which is a generalization of the classical SEIR models;and a module for visualizing and setting the parameters of this model according to epidemiological data, implemented in a dashboard. Data for testing have been collected since March 2020 on a daily basis from open Internet sources and placed on a “data farm” (an automated system for collecting, storing and pre-processing data from heterogeneous sources) hosted on a remote server. The combination of the proposed approach and its implementation in the form of a dashboard with the ability to conduct visual numerical experiments and compare them with real data allows most accurately tune the model parameters thus turning it into an intelligent system to support a decision-making. That is a small step towards Industry 5.0. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Distributed Gesture Controlled Systems for Human-Machine Interface

This paper presents the design flow of an IoT human-machine touchless interface. The device uses embedded computing in conjunction with the Leap Motion Controller to provide an accurate and intuitive touchless interface. Its main function is to augment current touchscreen devices in public spaces through a combination of computer vision technology, event-driven programming, and machine learning. Especially following the COVID-19 pandemic, this technology is important for hygiene and sanitation purposes for public devices such as airport, food, and ATM kiosks where hundreds or even thousands of people may touch these devices in a single day. A prototype of the touchless interface was designed with a Leap Motion Controller housed on a Windows PC exchanging information with a Raspberry Pi microcontroller via internet connection. © 2022 IEEE.

Human-Robot Interaction in Bengali language for Healthcare Automation integrated with Speaker Recognition and Artificial Conversational Entity

The research study presents an architecture of HumanRobot Interaction (HRI) based Artificial Conversational Entity integrated with speaker recognition ability to avail modern healthcare services. Due to the Covid-19 pandemic, the situation has become troublesome for health workers and patients to visit hospitals because of the high risk of virus dissemination. To minimize the mass congestion, our developed architecture would be an appropriate, cost-effective solution that automates the reception system by enabling AI-based HRI and providing fast and advanced healthcare services in the context of Bangladesh. The architecture consists of two significant subsections: Speaker Recognition and Artificial Conversational Entities having Automatic Speech Recognition in Bengali, Interactive Agent, and Text-to-Speech-synthesis. We used MFCC features as the linguistic parameters and the GMM statistical model to adapt each speaker's voice and estimation and maximization algorithm to identify the speaker's identity. The developed speaker recognition module performed significantly with 94.38% average accuracy in noisy environments and 96.27% average accuracy in studio quality environments and achieved a word error rate (WER) of 42.15% from RNN based Deep Speech 2 model for Bangla Automatic Speech Recognition (ASR). Besides, Artificial Conversational Entity performs with an average accuracy of 98.58% in a small-scale real-time environment. © 2021 IEEE.

A call for open data to develop mental health digital biomarkers.

Digital biomarkers of mental health, created using data extracted from everyday technologies including smartphones, wearable devices, social media and computer interactions, have the opportunity to revolutionise mental health diagnosis and treatment by providing near-continuous unobtrusive and remote measures of behaviours associated with mental health symptoms. Machine learning models process data traces from these technologies to identify digital biomarkers. In this editorial, we caution clinicians against using digital biomarkers in practice until models are assessed for equitable predictions ('model equity') across demographically diverse patients at scale, behaviours over time, and data types extracted from different devices and platforms. We posit that it will be difficult for any individual clinic or large-scale study to assess and ensure model equity and alternatively call for the creation of a repository of open de-identified data for digital biomarker development.