Showing papers in "International Journal of Human-computer Studies \/ International Journal of Man-machine Studies in 2021"

The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI

Abstract: Artificial intelligence and algorithmic decision-making processes are increasingly criticized for their black-box nature. Explainable AI approaches to trace human-interpretable decision processes from algorithms have been explored. Yet, little is known about algorithmic explainability from a human factors’ perspective. From the perspective of user interpretability and understandability, this study examines the effect of explainability in AI on user trust and attitudes toward AI. It conceptualizes causability as an antecedent of explainability and as a key cue of an algorithm and examines them in relation to trust by testing how they affect user perceived performance of AI-driven services. The results show the dual roles of causability and explainability in terms of its underlying links to trust and subsequent user behaviors. Explanations of why certain news articles are recommended generate users trust whereas causability of to what extent they can understand the explanations affords users emotional confidence. Causability lends the justification for what and how should be explained as it determines the relative importance of the properties of explainability. The results have implications for the inclusion of causability and explanatory cues in AI systems, which help to increase trust and help users to assess the quality of explanations. Causable explainable AI will help people understand the decision-making process of AI algorithms by bringing transparency and accountability into AI systems.

The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots

Abstract: Over the last ten years there has been a growing interest around text-based chatbots, software applications interacting with humans using natural written language. However, despite the enthusiastic market predictions, ‘conversing’ with this kind of agents seems to raise issues that go beyond their current technological limitations, directly involving the human side of interaction. By adopting a Human-Computer Interaction (HCI) lens, in this article we present a systematic literature review of 83 papers that focus on how users interact with text-based chatbots. We map the relevant themes that are recurrent in the last ten years of research, describing how people experience the chatbot in terms of satisfaction, engagement, and trust, whether and why they accept and use this technology, how they are emotionally involved, what kinds of downsides can be observed in human-chatbot conversations, and how the chatbot is perceived in terms of its humanness. On the basis of these findings, we highlight open issues in current research and propose a number of research opportunities that could be tackled in future years.

My Chatbot Companion -a Study of Human-Chatbot Relationships

Abstract: There has been a recent surge of interest in social chatbots, and human–chatbot relationships (HCRs) are becoming more prevalent, but little knowledge exists on how HCRs develop and may impact the broader social context of the users. Guided by Social Penetration Theory, we interviewed 18 participants, all of whom had developed a friendship with a social chatbot named Replika, to understand the HCR development process. We find that at the outset, HCRs typically have a superficial character motivated by the users' curiosity. The evolving HCRs are characterised by substantial affective exploration and engagement as the users' trust and engagement in self-disclosure increase. As the relationship evolves to a stable state, the frequency of interactions may decrease, but the relationship can still be seen as having substantial affective and social value. The relationship with the social chatbot was found to be rewarding to its users, positively impacting the participants' perceived wellbeing. Key chatbot characteristics facilitating relationship development included the chatbot being seen as accepting, understanding and non-judgmental. The perceived impact on the users' broader social context was mixed, and a sense of stigma associated with HCRs was reported. We propose an initial model representing the HCR development identified in this study and suggest avenues for future research.

Almost human? A comparative case study on the social media presence of virtual influencers

Abstract: As virtual agents become prevalent in many domains, virtual influencers have gone live on social media platforms, integrating human networks and interacting with users. Building on research on human-computer interactions, the Uncanny Valley hypothesis, and Computers Are Social Actors paradigm, this paper aims to investigate (1) virtual agents’ similarity to humans in terms of behaviour in human networks and (2) reactions to human versus virtual agents in human networks where this interaction is publicly visible. We analyse the posting behaviour of and reactions to one human, one human-like virtual, and one anime-like virtual influencer active on a popular social media platform via text and emoji postings over an 11-month period. We find that, despite the general positive atmosphere of the platform, the human-like virtual influencer receives significantly lower positive reactions, providing evidence for the Uncanny Valley. Additional measures of negative reactions show a similar pattern. We discuss these results within the context of authenticity and social identity on social media, providing recommendations for the implementation of virtual influencers in human social networks.

Older adults' perspectives of smart home technology: Are we developing the technology that older people want?

Abstract: New technology and smart homes have the potential to improve quality of life, safety, and care for older people. However, we do not yet know how older people's perceptions of these technologies may vary, in particular how views based on experience of actual use may differ from those related to anticipated use. We also do not know how older people living independently might view technology that may be of future rather than current value to them. This paper explores older people's views of smart home monitoring technology and compares these between people with direct experience and those without. Four focus groups were conducted with six older people recruited from the community with no smart home experience and seven drawn from a large-scale Interdisciplinary Research Collaboration that is developing a sensor platform for health and lifestyle at home. For the seven participants, the sensor platform was installed and operated in their homes for eight to twelve months before the current study. The study found that participants in each group had some similar and some different understandings of smart home technologies. Among participants who had already tried the smart home monitoring technology, acceptance increased over time and with use. They expressed fewer concerns than non smart homes participants regarding privacy, trust, usability, and more concerns about utility. Non smart home participants focused on the extent to which this technology might increase household's vulnerability and they considered the technology somewhat intrusive and noticeable. It appeared that the more positive views of participants who had direct experience of smart homes related to the degree of trust between them and the researchers who installed and maintained the smart home system. Both groups of participants shared views about the technical feasibility, affordability, impact on relationships, and about the engagement and competencies of those who would view the monitoring data. They suggested that the technology would be more acceptable if it was possible to customize functionality and features. These findings have implications for development of smart home technologies so that they are appropriate and acceptable to older people who are living independently.

The Impact of Serious Games with Humanoid Robots on Mild Cognitive Impairment Older Adults

Abstract: The number of Mild Cognitive Impairment (MCI) older adults is increasing; thus, it becomes more and more important to provide them with support to avoid, or at least slow down, their cognitive decline. To this end, interactive serious games can play an important role. So far, most of them have been deployed through tablets, which represent a cost-effective solution, yet offering only limited possibilities for truly engaging such users in a multimodal manner. However, emerging humanoid robots, through their physical embodiment and human-like attributes, including facial expressions and body language, may open up new possibilities in more effectively engaging MCI older adults during repetitive cognitive training. We present a study aiming to better understand the impact of humanoid robots in supporting serious games for such users. In particular, we investigate how seniors with Mild Cognitive Impairments relate to and perceive serious games accessed through humanoid robots, as part of a training programme aimed to improve their cognitive status. For this purpose, two versions of a music-based memory game have been designed by a multi-disciplinary team, one for humanoid robots and one for tablets. We report on its use during a between-subject study that involved MCI seniors, and discuss their experience. The results show that the robot was received with more enthusiasm by the older adults, thus improving their level of engagement.

Human-centered XAI : Developing design patterns for explanations of clinical decision support systems

Abstract: Much of the research on eXplainable Artificial Intelligence (XAI) has centered on providing transparency of machine learning models. More recently, the focus on human-centered approaches to XAI has increased. Yet, there is a lack of practical methods and examples on the integration of human factors into the development processes of AI-generated explanations that humans prove to uptake for better performance. This paper presents a case study of an application of a human-centered design approach for AI-generated explanations. The approach consists of three components: Do main analysis to define the concept & context of explanations, Re quirements elicitation & assessment to derive the use cases & explanation requirements, and the consequential M ulti-modal i nteraction design & evaluation to create a library of design patterns for explanations. In a case study, we adopt the DoReMi-approach to design explanations for a Clinical Decision Support System (CDSS) for child health. In the requirements elicitation & assessment, a user study with experienced paediatricians uncovered what explanations the CDSS should provide. In the interaction design & evaluation, a second user study tested the consequential interaction design patterns. This case study provided a first set of user requirements and design patterns for an explainable decision support system in medical diagnosis, showing how to involve expert end users in the development process and how to develop, more or less, generic solutions for general design problems in XAI.

Introduction of human-centric AI assistant to aid radiologists for multimodal breast image classification

Abstract: In this research, we take an HCI perspective on the opportunities provided by AI techniques in medical imaging, focusing on workflow efficiency and quality, preventing errors and variability of diagnosis in Breast Cancer. Starting from a holistic understanding of the clinical context, we developed BreastScreening to support Multimodality and integrate AI techniques (using a deep neural network to support automatic and reliable classification) in the medical diagnosis workflow. This was assessed by using a significant number of clinical settings and radiologists. Here we present: i) user study findings of 45 physicians comprising nine clinical institutions; ii) list of design recommendations for visualization to support breast screening radiomics; iii) evaluation results of a proof-of-concept BreastScreening prototype for two conditions Current (without AI assistant) and AI-Assisted; and iv) evidence from the impact of a Multimodality and AI-Assisted strategy in diagnosing and severity classification of lesions. The above strategies will allow us to conclude about the behaviour of clinicians when an AI module is present in a diagnostic system. This behaviour will have a direct impact in the clinicians workflow that is thoroughly addressed herein. Our results show a high level of acceptance of AI techniques from radiologists and point to a significant reduction of cognitive workload and improvement in diagnosis execution.

Effects of gender and personality differences on students’ perception of game design elements in educational gamification

Abstract: While many studies have reported the effectiveness of gamification in motivating students and making learning more fun, some others have reported contradictory findings regarding the potential of implementing game elements in an online gamified course. It is recognized that designing a successful gamification is a challenging process. Previous studies have shown that students’ individual differences may impact their gamification experiences. This study complements the available body of research by examining the effect of gender and personality differences on students’ perception of gamification in education. An experiment was conducted in a public university with 189 undergraduate students who took three online gamified courses, based on the self-determination theory, during two academic years. The results showed that gender and personality can affect students’ perception of specific game elements. For instance, females are more likely to find feedback useful than males. Additionally, students low in extraversion are more likely to find a progress bar useful than students high in extraversion. The results also showed that gender moderates the effect of personality on students’ perception of the implemented game elements. For instance, males low in extraversion are more likely to perceive badges’ usefulness in gamified courses than males high in extraversion, whereas females low in conscientiousness are more likely to enjoy feedback than females high in conscientiousness. The findings of this study can help designers and educators personalize their gamified courses’ design based on personality and gender.

Talking datasets: Understanding data sensemaking behaviours

Abstract: The sharing and reuse of data are seen as critical to solving the most complex problems of today. Despite this potential, relatively little attention has been paid to a key step in data reuse: the behaviours involved in data-centric sensemaking. We aim to address this gap by presenting a mixed-methods study combining in-depth interviews, a think-aloud task and a screen recording analysis with 31 researchers from different disciplines as they summarised and interacted with both familiar and unfamiliar data. We use our findings to identify and detail common patterns of data-centric sensemaking across three clusters of activities that we present as a framework: inspecting data, engaging with content, and placing data within broader contexts. Additionally, we propose design recommendations for tools and documentation practices, which can be used to facilitate sensemaking and subsequent data reuse.

Physiological indicators of task demand, fatigue, and cognition in future digital manufacturing environments

Abstract: As Digital Manufacturing transforms traditionally physical work into more system-monitoring tasks, new methods are required for understanding people’s mental workload and prolonged capacity for focused attention. Many physiological measures have shown promise for detecting changes in cognitive state, and recent advances in sensor technology offer minimally-invasive ways to monitor our cognitive activity. Previous research in functional near-infrared spectroscopy, for example, has observed changes in cerebral hemodynamic response during periods of high demand within tasks. This work investigated the relationships among task demand, fatigue, and attention degradation in a sustained attention task, and their effect on heart rate, breathing rate, nose temperature and hemodynamic response in the prefrontal cortex and middle temporal gyrus. Analysis revealed a small but significant effect of fatigue on heart rate relative to baseline, breathing rate and hemodynamic response. Task demand had a small but significant effect on breathing rate and nose temperature, both relative to baseline, but no difference between levels of demand was observed in heart rate or hemodynamic response. Our results provide insight into what physiological data can tell us about cognitive state, ability to focus, and the impact of fatigue over time.

Measuring Mental Workload Variations in Office Work Tasks using fNIRS

Abstract: The motivation behind using physiological measures to estimate cognitive activity is typically to build technology that can help people to understand themselves and their work, or indeed for systems to do so and adapt. While functional Near Infrared Spectroscopy (fNIRS) has been shown to reliably reflect manipulations of mental workload in different work tasks, we still need to establish whether fNIRS can differentiate variety within common office-like tasks in order to broaden our understanding of the factors involved in tracking them in real working conditions. 20 healthy participants (8 females, 12 males), whose work included office-like tasks, took part in a user study that investigated a) the sensitivity of fNIRS for measuring mental workload variations in representations of everyday reading and writing tasks, and b) how representations of natural interruptions are reflected in the data. Results supported fNIRS measuring PFC activation in differentiating between workload levels for reading tasks but not writing tasks in terms of increased oxygenated haemoglobin (O2Hb) and decreased deoxygenated haemoglobin (HHb), for harder conditions compared to easier conditions. There was considerable support for fNIRS in detecting changes in workload levels due to interruptions. Variations in workload levels during the interruptions could be understood in relation to spare capacity models. These findings may guide future work into sustained monitoring of cognitive activity in real-world settings.

Drivers’ acceptance of mobile navigation applications: An extended technology acceptance model considering drivers’ sense of direction, navigation application affinity and distraction perception

Abstract: This study proposes an integrated technology acceptance model to investigate the factors that affect drivers’ usage intention of mobile navigation applications. The proposed model adds three new constructs (drivers’ sense of direction, navigation application affinity and distraction perception) to the original technology acceptance model based on the features of mobile navigation applications. First, a questionnaire was developed and administered, and data from 384 drivers were collected via an online survey. Second, confirmatory factor analysis was conducted to examine the reliability and validity of the developed scale based on the collected data. Third, a structural equation model was constructed to investigate the interrelationships among these constructs in the conceptual research model and to identify the key factors that affect drivers’ acceptance of mobile navigation applications. The proposed model explained 60.50% of the variance in the intention to use mobile navigation applications. In addition to attitude and perceived usefulness, navigation application affinity and distraction perception also significantly affected drivers’ intention to use mobile navigation applications. Navigation application affinity and distraction perception affected not only drivers’ intention to use but also their perceptions. Sense of direction was a significant individual trait that affected drivers’ navigation application affinity, distraction perception, perceived ease of use and perceived usefulness. These findings imply that relevant developers should continually optimize the incorrect and inappropriate use of navigation information and that they should attach importance to the amount and intelligibility of navigation information. Furthermore, the prompt form of navigation information should satisfy the demands and expectations of drivers with different senses of direction. Overall, this study improves our understanding of drivers’ acceptance of mobile navigation applications and provides some important practical implications to improve mobile navigation services.

Bridging narrative and locality in mobile-based augmented reality educational activities: Effects of semantic coupling on students’ immersion and learning gains

Abstract: Despite the rapid development of mobile-based Augmented Reality (AR) apps, little is yet known about how we can facilitate immersion and learning in AR contexts. This study investigated the hypothesis that greater coupling between physical space and the narrative of the AR learning activity can result in increased levels of immersion and students’ conceptual learning gains. Prior studies investigating the coupling effect are limited, inconclusive and primarily focused on adult populations. The present study investigated the effects of two types of coupling (strong/loose) on middle school students’ experienced immersion and conceptual learning gains, in the context of a narrative-based AR science education intervention. Forty-five middle school students participated in this study: Students in Condition 1 (n = 22) participated in an AR activity with strong coupling between narrative and the physical space, while students in Condition 2 (n = 23) participated in a loose coupling version of the activity. The data corpus consisted of baseline data, questionnaires investigating students’ immersion and conceptual learning gains, and post-activity interviews. Findings showed higher conceptual learning gains and increased immersion for the students participating in the strong coupling condition than for the students in the loose coupling condition. We discuss these findings and their implications, and we highlight questions for future research.

Automated prediction of visual complexity of web pages: Tools and evaluations

Abstract: Understanding visual complexity as it relates to websites has been an emergent area for many years. However, predicting the visual complexity of a website as perceived by users has been a real challenge. Perception is important because it influences user engagement, dictating if they will find it dull, engaging, or too complex. While others have suggested solutions to certain levels of success, here we propose a simple but accurate model that generates a Visual Complexity Score (VCS) based on common aspects of an HTML Document Object Model (DOM). We created our model based on a statistical analysis of 3300 ratings of 55 users on 30 web pages. We then implemented this prediction model in an open source Eclipse framework called ViCRAM that both predicts and visualises the complexity of web pages in the form of a pixelated heat map. Finally, we evaluated this model and the tool prediction with another user study of 6240 ratings of 104 users on 30 web pages. This study shows that our tool can predict the perceived complexity with a strong correlation to users’ perceived complexity.

Social interactions and relationships with an intelligent virtual agent

Abstract: The current study explores how humans interact with an intelligent virtual agent (IVA), Siri, on their mobile phones and how such experiences may impact their trust, social presence, and comfort level toward the IVA as a coworker, supervisor, and friend. A two (male- vs. female-voice of Siri) by two (functional vs. social task) by two (matched vs. ummatched with participants’ gender) between-subjects experiment was performed with 163 participants. Higher levels of trust in cognitive dimensions were associated with functional tasks assisted by Siri, and one interaction between participants’ gender and Siri's gendered voice was observed in an affective dimension of trust, faith. Copresence dimension of social presence was associated with technical competence, understandability, faith, and personal attachment dimension of trust whereas psychological involvement was related with reliability and technical competence. Copresence, alongside faith and personal attachment, both affective dimensions of trust, seemed to facilitate comfortable feelings toward Siri imagined as various relational partners.

Observing and predicting knowledge worker stress, focus and awakeness in the wild

Abstract: Knowledge workers face many challenges in the workplace: work is fragmented, disruptions are constant, tasks are complex, and work hours can be long. These challenges can affect knowledge workers’ stress, focus and awakeness, and in turn their interaction with the digital environment, the quality of work performed and their productivity in general. We report on a field study with 14 knowledge workers over an eight-week period in which we investigated, using experience sampling, how the workers experience stress and awakeness over time. During this field study, we also collected biometric data including heart- and skin-related measures, which we then used to investigate if it is possible to predict stress, focus and awakeness, in the moment. We observed and report on various trends in knowledge worker stress and awakeness levels over several weeks, finding that people tend to have certain “baseline” levels for these aspects. Moreover, we found that days with high levels of stress tend to cluster, similarly as the days with low awakeness. We further show that machine learning models can be built from the data of a single minimally invasive device to predict stress, focus, and awakeness. Overall, we found that our models were capable of large improvements in precision and recall in comparison to a random classifier for stress (25.9% increase over random for precision, 4.2% for recall) and awakeness (52.4% increase in precision, 40.8% in recall). The abstract concept of focus proved to be the hardest to predict (26.0% increase in precision, 27.8% decrease in recall).

Experimenters' Influence on Mental-Imagery based Brain-Computer Interface User Training

Abstract: Context: Motor Imagery based Brain-Computer Interfaces (MI-BCIs) enable their users to interact with digital technologies, e.g., neuroprosthesis, by performing motor imagery tasks only, e.g., imagining hand movements, while their brain activity is recorded. To control MI-BCIs, users must train to control their brain activity. During such training, experimenters have a fundamental role, e.g., they motivate participants. However, their influence had never been formally assessed for MI-BCI user training. In other fields, e.g., social psychology, experimenters gender was found to influence experimental outcomes, e.g., behavioural or neurophysiological measures. Objective: Our aim was to evaluate if the experimenters gender influenced MI-BCI user training outcomes, i.e., performances and user-experience. Methods: We performed an experiment involving 6 experimenters (3 women) each training 5 women and 5 men (60 participants) to perform right versus left hand MI-BCI tasks over one session. We then studied the training outcomes, i.e., MI-BCI performances and user-experience, according to the experimenters’ and subjects’ gender. Results: A significant interaction between experimenters and participants’ gender was found on the evolution of trial-wise performances. Another interaction was found between participants tension and experimenters gender on the average performances. Conclusion: Experimenters gender could influence MI-BCI performances depending on participants gender and tension. Significance: Experimenters influence on MI-BCI user training outcomes should be better controlled, assessed and reported to further benefit from it while preventing any bias.

Shocking advantage! Improving digital game performance using non-invasive brain stimulation

Abstract: As digital gaming has grown from a leisure activity into a competitive endeavor with college scholarships, celebrity, and large prize pools at stake, players search for ways to enhance their performance, including through coaching, training, and employing tools that yield a performance advantage. Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that is presently being explored by esports athletes and competitive gamers. Although shown to modulate cognitive processing in standard laboratory tasks, there is little scientific evidence that tDCS improves performance in digital games, which are visually complex and attentionally demanding environments. We applied tDCS between two sessions of the Stop-Signal Game (SSG; Friehs, Dechant, Vedress, Frings, & Mandryk, 2020). The SSG is a custom-built infinite runner that is based on the Stop-Signal Task (SST; Verbruggen et al., 2019). Consequently, the SSG can be used to evaluate response inhibition as measured by Stop-Signal Reaction Time (SSRT), but in an enjoyable 3D game experience. We used anodal, offline tDCS to stimulate the right dorsolateral prefrontal cortex (rDLPFC); a 9 cm² anode was always positioned over the rDLPFC while the 35 cm² cathode was placed over the left deltoid. We hypothesized that anodal tDCS would enhance neural processing (as measured by a decrease in SSRT) and improve performance, while sham stimulation (i.e., the control condition with a faked stimulation) should lead to no significant change. In a sample of N = 45 healthy adults a significant session x tDCS-condition interaction emerged in the expected direction. Subsequent analysis confirmed that the statistically significant decrease in SSRT after anodal tDCS to the rDLPFC was not due to a general change in reaction times. These results provide initial evidence that tDCS can influence performance in digital games.

UX information in the daily work of an agile team: A distributed cognition analysis

Abstract: The integration of agile software development and user experience (UX) design has been a topic of investigation for practitioners and researchers for many years, and agile teams have become increasingly aware of the importance of UX design. Most studies have focused so far on the integration of UX theories and methods with agile practices. The objective of this research is to investigate whether and how UX information is embedded in the daily work of an agile team. We conducted an ethnographic study of an agile team based in the UK. We performed a qualitative analysis using different data sources and three complementary analytical lenses: Distributed Cognition of Teamwork, Garrett's set of UX elements and planes, and Hassenzahl's content-oriented model of UX. This combination provided an understanding of the different types of UX information available to the agile team through artefacts and face-to-face meetings, how the information flowed within and around the agile team, and the type of engagement they have with UX information. The findings reveal that: (1) agile team members were consumers of UX information not producers; (2) the most common type of UX information found in the system related to how the user interacts with the product rather than to user goals or needs; (3) information focusing on the user perspective appears in verbal communication rather than being captured in artefacts; and, (4) the flow of UX information around the team is complex. In combination, these factors indicate a potential breakdown in the communication of UX information. We argue that these findings have relevance for other agile teams because the artefacts and methods used by this team are commonly used by other agile teams. To improve the situation, we suggest a number of recommendations to engage agile team members in UX work, and reduce the complexity of UX information flow.

Gender in the making: An empirical approach to understand gender relations in the maker movement

Abstract: In this paper we explore gender issues in the maker movement using four different methods of data analysis: standardised questionnaires, analysis of makerspaces’ social media, statistical analysis of machine use and the coding of interviews and focus groups. The objective is to give a voice to female makers and makerspace managers, looking at the maker movement from an inside perspective. The paper demonstrates how gendered stereotypes are still reproduced within the maker movement. Makerspaces still attract considerably more males than females and exhibit a primarily “male” culture, reflected in the interior design of places, or by the language and attitudes of their members. Females in makerspaces, however, often have a background in communications, arts or design, as opposed to the coding or engineering background of the males. Previous research has shown how machines and materials also take on gendered connotations. The “genderisation” of objects refers to an attributed gender-specific use of machines. An example here could be that 3D printing, which is more often used by female makers compared to male members of makerspaces. Our research also identifies promising approaches for tackling the issue of gender imbalance. The maker movement has significant potential to improve gender equality as younger generations bring societal change to makerspaces and break with stereotypes. One of the findings our paper puts forward is the lack of female role models, especially in leading positions. Our data suggest that the maker scene still has to develop a tradition in reflecting questions around gender stereotypes or their role in partly reproducing them. Hence, we argue that the maker community should be more attentive to this issue and should make a concerted effort to become more diverse. Overall, our case research did not reveal any explicit animosity to gender questions; on the contrary, male makers and most maker communities showed great interest in avoiding gender stereotyping. We suggest applying the Bechdel-Test as a simple tool for clarifying gender topics and encouraging self-reflection among makers. Overall, our paper aims to support makerspaces that want to make diversity part of their strategy for future growth.

Virtual Reality Art-Making for Stroke Rehabilitation: Field Study and Technology Probe

Abstract: How can we better understand the process of therapeutic art-making for stroke rehabilitation, and what are design opportunities for virtual reality art-making for people with stroke-related impairments? We investigated this question in a two-part study with 14 amateur artists with disabilities resulting from stroke: a three-week field study and a technology probe consisting of experiential virtual reality interviews. We uncovered what participants made, the aesthetics of the materials and the process of making. The field study revealed inspirations around identity, situatedness of choices for tools in the social and physical environment, and a breadth of application techniques (e.g., dripping paint or use of tape) that varied in need for fine motor control. The experiential virtual reality interviews highlighted the need for control, the affordances of the medium, and the challenges in viewing and reflecting on work. Emergent art reflected qualities of the 3D paint and free-form gesture. Virtual reality and traditional art-making contrasted in the speed and finality of application, opportunities for iteration and reflection, and in the need for dexterity. We discuss strengths, weaknesses and implications for design of virtual reality art-making for those with stroke-related impairments.

Automated and Personalized Nutrition Health Assessment, Recommendation, and Progress Evaluation using Fuzzy Reasoning

Abstract: Establishing a healthy lifestyle has become a very important aspect in people's lives. The latter requires maintaining a healthy nutrition by considering the type and quantity of consumed foods. It also requires maintaining an active lifestyle including the necessary amount of physical exercise to regulate one's intake and consumption of calories and nutrients. As a result, people reach out for nutrition experts to perform health assessment, whose services are costly, time consuming, and not readily available. While various e-nutrition solutions have been developed, yet most of them perform meal planning without performing health state assessment or evaluation (traditionally provided by human experts). To our knowledge, there is no existing automated solution to perform nutrition health assessment, recommendation, and progress evaluation, which are central pre-requites to the meal planning task. In this study, we introduce a novel framework titled PIN for Personalized Intelligent Nutrition recommendations. PIN relies on the fuzzy logic paradigm to simulate human expert health assessment capabilities, including weight, caloric intake, and exercise recommendations as well as progress evaluation and recommendation adjustments. It includes three essential and complementary modules: i) Weight Assessment and Recommendation (WAR), ii) Caloric Intake and Exercise Recommendation (CIER), and iii) Progress Evaluation and Recommendation Adjustment (PERA). This underlines the first computerized solution for nutrition health assessment. We have conducted a large battery of experiments involving 50 patient profiles and 11 nutrition expert evaluators to test the performance of PIN and evaluate its health assessment quality. Results show that PIN’s assessment and recommendations are on a par with and sometimes surpass those of human nutritionists.

Teleoperated mobile robot with two arms: the influence of a human-machine interface, VR training and operator age

Abstract: The article presents a method of supporting work by using virtual reality techniques to control a two-armed mobile robot's movement. The robot developed for the study can carry a 20 kg load (10 kg in each arm), and its price is low enough to enable mass implementation in industry (e.g., in warehouses). The results of a study conducted with 81 participants (63 young and 18 older active workers), divided into five study groups, are discussed. Three main factors are considered: human-machine interface, training in virtual reality and operator's age. The operator's work effectiveness was examined. Subjective indicators, such as usability, acceptance of technology, or the operator's fatigue and stress level were also investigated. Our results indicate, among others, that interface without walking is considered the most useful, short VR training does not increase the operator's efficiency, and older operators are much less willing to accept this kind of technology. The robot's description and how it is controlled are presented in the paper.

Enhanced cognitive workload evaluation in 3D immersive environments with TOPSIS model

Abstract: Research puts forward perception-based cognitive workload evaluation methods to help VR developers and users measuring their workload when playing with a VR application. Approaches to measure workload based on biosensors have progressed significantly, while evaluation based on subjective methods still rely on standard questionnaires such as the NASA-TLX table, the Subjective Workload Assessment Technique and the Modified Cooper Harper scale. The pre-defined questions enable operators to carry out experiments and analyse the data more easily than with biofeedback. However, the subjective evaluation process can bias the results because of unperceived internal changes and unknown factors among users. It is therefore necessary to have a method to handle and analyse this uncertainty. We propose to use the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) model to analyse the NASA-TLX table for measuring the overall user workload instead of using the classical weighted sum method. To show the advantage of the TOPSIS approach, we performed a user experiment to validate the approach and its application to VR, considering factors including the VR platform and the scenario density. Three different weighting methods, including the fuzzy Analytic Hierarchy Process (AHP) from fuzzy logic, the classical weighting based on pairwise comparison and the uniform weighting method, were tested to see the applicability of the TOPSIS model. The results from TOPSIS were consistent with those from other evaluation methods; a significant reduction in the coefficient of variation (CV) was observed when using the TOPSIS model to analyse the NASA-TLX scores, indicating an enhanced precision of the workload evaluation by the TOPSIS method. Our work has a potential application for VR designers and experimenters to compare cognitive workload among conditions and to optimize the settings.

Immersive virtual reality news: A study of user experience and media effects

Abstract: Recently, the concept of “immersive VR news” has been proposed in the news media field. However, unlike traditional video news, the user experience and media effects of immersive VR news have yet to be verified. To examine the influence of VR technology on the news media field, we designed an immersive VR news product to further discern differences in user experience and media effects among traditional video news, VR news without interaction, and VR news with interaction. The results show that traditional video news excelled in terms of empathy and nervousness, whereas VR news with interaction was superior in terms of immersion, interest, accuracy, and credibility. VR news without interaction performed the poorest in all categories. Based on the observed user behaviors during the experiment, we present a detailed discussion of the reasons for these differences and offer guidelines for the design and application of immersive VR news.

Developing Future Wearable Interfaces for Human-Drone Teams through a Virtual Drone Search Game

Abstract: Autonomous robotic vehicles (i.e., drones) are potentially transformative for search and rescue (SAR). This paper works toward wearable interfaces, through which humans team with multiple drones. We introduce the Virtual Drone Search Game as a first step in creating a mixed reality simulation for humans to practice drone teaming and SAR techniques. Our goals are to (1) evaluate input modalities for the drones, derived from an iterative narrowing of the design space, (2) improve our mixed reality system for designing input modalities and training operators, and (3) collect data on how participants socially experience the virtual drones with which they work. In our study, 17 participants played the game with two input modalities (Gesture condition, Tap condition) in counterbalanced order. Results indicated that participants performed best with the Gesture condition. Participants found the multiple controls challenging, and future studies might include more training of the devices and game. Participants felt like a team with the drones and found them moderately agentic. In our future work, we will extend this testing to a more externally valid mixed reality game.

Information flow and cognition affect each other: Evidence from digital learning

Abstract: In the context of learning systems, identifying causal relationships among information presented to the user, their behavior and cognitive effort required/exerted to understand and perform a task is key to building effective learning experiences, and to maintain engagement in learning processes. An unexplored question is whether our interaction with presented information affects our cognitive effort (and behaviour), or vice-versa. We investigate causal relationship between information presented and cognitive effort (and behaviour) in the context of two separate studies (N = 40, N = 98), and study the effect of instruction (active/passive task). We utilize screen-recordings and eye-tracking data to investigate the relationship among these variables. To investigate the causal relationships among the different measurements, we use Granger’s causality. Further, we propose a new method to combine two time-series from multiple participants for detecting causal relationships. Our results indicate that information presentation drives user focus size (behaviour), and that cognitive load (a measure of cognitive effort exerted) drives information presentation. This relationship is also moderated by instruction type and performance-level (high/low). We draw implications for design of educational material and learning technologies.

Social domestication of service robots: The secret lives of Automated Guided Vehicles (AGVs) at a Norwegian hospital

Abstract: This paper explores how robots that are not designed for being social can still act and be perceived as social and what form this social interaction takes It does so through a case study of Automated Guided Vehicles (AGVs) at a Norwegian hospital that interact with patients, nurses, caregivers and other machinery These robots are primarily tasked with moving goods such as medical equipment, food and garbage and are programmed to be automated, eg, taking hospital elevators by themselves Although the robots are unanthropomorphized, our research shows a strong perception of autonomy of the AGVs, specifically in relation to how voices and appearances of robots can make the robots more acceptable through appearing more “alive” They take part in an intricate domestication process as non-human actors relating to the human actors that also frequent the hospital corridors, making them part of the digitalization infrastructure at the hospital This is particularly tied to their usage of the local Norwegian dialect and a projection of clumsiness, which gives them a sense of personality, or an impression of being friendly animal-like creatures one can enjoy observing without interacting with This is framed theoretically through three dimensions of understanding the domestication of social robots as healthcare technology The first dimension is Practical Domestication, where using voice as a "human factor" in unanthropomorphized robots can be of great value, if done well, by making them more approachable A non-standardized voice can be an effective tool to give the robot a sense of personality The second dimension is Symbolical Domestication, seeing how unanthropomorphized robots present novel ways of achieving trust from the public When people get to know the non-perfect robot in itself, not masked as a person or animal, there is interest and trust in the machine The last dimension is Cognitive Domestication, seeing how human practices change through the interaction with technology Additionally, we suggest that there is a fourth dimension, which we term Social Domestication, at work

Derivation of young children's interaction strategies with digital educational games from gaze sequences analysis

Abstract: Digital Education Games (DEGs) have been used to support children's learning in various domains. A number of existing studies on DEGs has focused on whether they could improve children's learning performance. However, only a few of them have attempted to address the critical question of how young children interact with DEGs. Bridging this gap was the main motivation underpinning this research study. With the use of eye-tracking technology, we explored our research goal by evaluating a bespoke DEG on numeracy and its cardboard version that we developed based on the UK Early Years Foundation Stage (EYFS) framework. A between-subject experiment study involving 94 five-year-olds was conducted. The research protocols and instruments were pilot tested and ethically approved. In analysing the eye-tracking data, we refined the Gaze Sub-sequence Marking Scheme to infer children's interaction strategies. Results showed that the difference in the learning effect between the digital and cardboard game was insignificant, that the children's interaction strategies varied significantly with their achievement level, and that children's gender was not a significant factor in determining the impact of learning with the DEG. Implications for rendering eye-tracking technology more child-friendly and designing DEGs for young children are drawn.