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Firms are beginning to use chatbots to collect information from users (e.g., online reviews), a task traditionally done through forms. We used two experiments to investigate how collecting reviews using chatbots in place of forms might impact user experience and, in turn, alter the nature of the reviews submitted by users. Study 1 compares chatbots to web forms, finding that chatbots may improve perceived efficiency but otherwise have no discernible effect on satisfaction and usage intention. At the same time, chatbot usage results in shorter, lower-quality reviews. While structured templates enhance form-based reviews, introducing structure to chatbot interactions does not positively impact satisfaction and usage intent, potentially making the process less efficient. Nonetheless, a structured chatbot approach yields longer reviews and mitigates declines in quality. Adding structure to a chatbot takes the chat out of the chatbot, turning the interaction from casual conversation to a formal process, as demonstrated in Study 2. Hence, while this structured approach for chatbots improves review quality without harming satisfaction and usage intent, it may not be the most effective method for enhancing the reviewer experience. Our research shows that the chatbot and structure pulled users towards contradicting genre rules (the social structures that guide technology use) and triggered users to subconsciously enact distinctly different thought patterns as they composed reviews. [ABSTRACT FROM AUTHOR]

Institutions in the GLAM (galleries, libraries, archives, and museums) sector serve the public as institutions for cultural heritage, identity, education, experience, and leisure. The goals of these institutions are becoming more closely aligned and they have shared a turn from being collection-centred to a focus on users and visitors. When these institutions also face similar challenges brought on especially by digitalization, there is increasing discourse in scholarly literature and among GLAM professionals about the drivers, impacts and opportunities of convergence for the sector. There remains however, scant empirical examination of user/visitor perception and experience of such convergence. This research addresses this knowledge gap and investigates a converged GLAM case institution, Te Ara 'Atea in New Zealand through a user/visitor survey. The survey findings suggest that users/visitors perceive and experience fewer issues with convergence relative to benefits. A novel insight from this research is that the concept of GLAM convergence can positively contribute to cultural and community wellbeing. As for perceived issue within the convergence space, of particular interest is the concept of unsanctioned touch of displayed objects/artworks, and the alleged unclear behavioral norms in converged environments. The findings also point to further considerations and implications regarding GLAM disciplinary boundaries. [ABSTRACT FROM AUTHOR]

Assistance systems have been widely adopted in the manufacturing sector to facilitate various processes and tasks in production environments. However, existing systems are mostly equipped with rigid functional logic and do not provide individual user experiences or adapt to their capabilities. This work integrates human factors in assistance systems by adjusting the hardware and instruction presented to the workers' cognitive and physical demands. A modular system architecture is designed accordingly, which allows a flexible component exchange according to the user and the work task. Gamification, the use of game elements in non-gaming contexts, has been further adopted in this work to provide level-based instructions and personalised feedback. The developed framework is validated by applying it to a manual workstation for industrial assembly routines. [ABSTRACT FROM AUTHOR]

The light field (LF) imaging technique can capture 3D scene information in 4D by recording both 2D intensity and 2D direction of incoming light rays. Due to this capability, LF has shown a great interest in virtual reality (VR) and augmented reality (AR) for enhanced immersion, improved depth perception and reconstruction of realistic 3D environments. This paper presents a comprehensive review of LF imaging technology and other approaches used for VR content creation. The applications of LF technology beyond VR and AR are also discussed. The challenges and limitations of other approaches for VR content creation are examined. State‐of‐the‐art research has focused on how VR experiences benefit from LF technology and identified the challenges to creating comfortable, immersive and realistic VR content such as (1) image size and resolution, (2) processing speed, (3) precise calibration and (4) depth reconstruction. Recommendations that can be considered for creating immersive VR content are provided to enhance user experience. These recommendations aim to contribute to developing more comfortable and realistic VR content, extending the potential applications of LF imaging technology in diverse fields. [ABSTRACT FROM AUTHOR]

Cross reality laboratories are widely used in education, yet research on the usability/user experience (UX) of these laboratories is still lacking. This study wants to start the discussion about challenges for usability/UX by interviewing practitioners from a large project for cross reality laboratories spanning multiple institutes in Germany. A total of 18 challenges were discovered, together with three target groups, namely, developers, maintainers, and learners. In addition, the tension between developers and usability is discussed. Open questions include the need to conduct further research with different target groups and how to increase usability for laboratory developers. [ABSTRACT FROM AUTHOR]

Background: Digital phenotyping, the in-situ collection of passive (phone sensor) and active (daily surveys) data using a digital device, may provide new insights into the complex relationship between daily behaviour and mood for people with type 2 diabetes. However, there are critical knowledge gaps regarding its use in people with type 2 diabetes. This study assessed feasibility, tolerability, and user experience of digital phenotyping in people with and without type 2 diabetes after participation in a 2-month digital phenotyping study in Ireland. At study completion, participants rated methodology elements from "not a problem" to a "serious problem" on a 5-point scale and reported their comfort with the potential future use of digital phenotyping in healthcare, with space for qualitative expansion. Results: Eighty-two participants completed baseline. Attrition was 18.8%. Missing data ranged from 9–44% depending on data stream. Sixty-eight participants (82.9%) completed the user experience questionnaire (51.5% with type 2 diabetes; 61.8% female; median age-group 50–59). Tolerability of digital phenotyping was high, with "not a problem" being selected 76.5%—89.7% of the time across questions. People with type 2 diabetes (93.9%) were significantly more likely to be comfortable with their future healthcare provider having access to their digital phenotyping data than those without (53.1%), χ2 (1) = 14.01, p = <.001. Free text responses reflected a range of positive and negative experiences with the study methodology. Conclusions: An uncompensated, 2-month digital phenotyping study was feasible among people with and without diabetes, with low attrition and reasonable missing data rates. Participants found digital phenotyping to be acceptable, and even enjoyable. The potential benefits of digital phenotyping for healthcare may be more apparent to people with type 2 diabetes than the general population. [ABSTRACT FROM AUTHOR]

AbstractIntelligent environments are rapidly gaining ground, propelled by a rich sensor infrastructure, the Internet of Things, sophisticated reasoning capabilities, and Artificial Intelligence. In this complex technological landscape, crafting usable intelligent environments and assessing the user experience (UX) demands a thorough understanding of the concepts involved and the parameters that need to be studied. This paper carries out a review of usability and UX evaluation methods and frameworks, elaborating on fundamental concepts and presenting in detail approaches and methods reported in the literature. It additionally examines evaluation approaches in adaptive and ubiquitous computing systems, which are closely associated with intelligent environments, and presents UX challenges and evaluation frameworks in intelligent environments. Finally, the findings are synthesized and consolidated to produce a comprehensive overview of the field and the challenges that lie ahead. [ABSTRACT FROM AUTHOR]

Exercise therapy is an important rehabilitation method for knee osteoarthritis (KOA) patients. This paper developed an exercise game(exergame) system based on Azure Kinect to help KOA patients accurately perform exercise prescriptions and improve their compliance with active exercise. The proposed system digitizes the KOA exercise prescription formulated by the physician and designs a personalized exergame, guiding the KOA patients to perform the correct rehabilitation exercise. Besides, the system employs the Azure Kinect to collect the user's main joint position information and assist in recognizing the user's rehabilitation training actions. We recruited 3 rehabilitation physicians and 30 KOA patients (experimental group: 20, control group: 10) to conduct a clinical trial aimed at evaluating the usability of the system and user satisfaction. Based on the data recorded by the system and evaluations conducted by the physicians, we found that the experimental group demonstrated an impressive accuracy rate of 97.47% in executing actions, which was significantly higher than the control group. These results demonstrate the effectiveness of the system in guiding the rehabilitation training of KOA patients. The system achieved an accuracy of 95.42% for action recognition, demonstrating that the exergame is reliably interactive. According to the analysis of the UEQ-S questionnaire, the average satisfaction score of the experimental group with the system was (1.68±0.893). These results indicate that the system is capable of providing an exceptional user experience, ultimately increasing patients' willingness and compliance to engage in active exercises. The above experiments confirmed that the proposed exergame system has high usability and an ideal user experience and is suitable for home active exercise therapy for KOA patients. [ABSTRACT FROM AUTHOR]

AbstractObjectiveMethodsResultsConclusionHIV molecular epidemiology (HIV ME) is a tool that aims to improve HIV research, surveillance, and cluster detection and response. HIV ME is a core pillar of the U.S. initiative to End the HIV Epidemic but faces some challenges and criticisms from stakeholders. We sought to assess user experience to identify the current needs for HIV ME.Users of HIV ME, including researchers and public health practitioners, were engaged via a structured survey. Needs were assessed via open-ended questions about HIV ME. Data were analyzed using reflexive thematic analysis; the concordance of results was assessed semi-quantitatively.Of 90 possible HIV-ME end-users, 57 completed the survey (response rate = 63%), which included users engaged in research (n = 29) and public health (n = 28). Respondents identified current imperatives, challenges, and strategies to improve HIV ME. Imperatives included characterization of the virus, identification of HIV hotspots, and tailoring of HIV interventions. Challenges encompassed technological issues, ethical concerns, and implementation difficulties. Strategies to improve HIV ME involved improving data access and analysis, enhancing implementation guidance and resources, and fostering community engagement and support. Researchers and public health practitioners prioritized different imperatives, but similarly emphasized the ethical concerns with HIV ME.The imperatives identified by users underscore the necessity of HIV ME, while the challenges highlight the hurdles to be overcome, including ethical concerns which emerged as a shared emphasis across user groups. The strategies outlined offer a roadmap for overcoming these challenges. These insights, drawn from user experience, present a valuable opportunity to inform the development of guidelines for the ethical application of HIV ME in research, surveillance, and cluster detection and response. [ABSTRACT FROM AUTHOR]

The use of collaborative robots in industries is growing rapidly. To ensure the successful implementation of these devices, it is essential to consider the user experience (UX) during their design process. This study is aimed at testing the UX goals that emerge when users interact with a collaborative robot during the programming and collaborating phases. A framework on UX goals will be tested, in the geographical context of Portugal. For that, an experimental setup was introduced in the form of a laboratory case study in which the human–robot collaboration (HRC) was evaluated by the combination of both quantitative (applying the User Experience Questionnaire [UEQ]) and qualitative (semistructured interviews) metrics. The sample was constituted by 19 university students. The quantitative approach showed positive overall ratings for the programming phase UX, with attractiveness having the highest average value (M = 2.21; SD = 0.59) and dependability the lowest (M = 1.64; SD = 0.65). For the collaboration phase, all UX ratings were positive, with attractiveness having the highest average value (M = 2.46; SD = 0.78) and efficiency the lowest (M = 1.93; SD = 0.77). Only perspicuity showed significant differences between the two phases (t(18) = −4.335, p = 0.002). The qualitative approach, at the light of the framework used, showed that efficiency, inspiration, and usability are the most mentioned UX goals emerging from the content analysis. These findings enhance manufacturing workers' well‐being by improving cobot design in organizations. [ABSTRACT FROM AUTHOR]

Background: More efficient methods to detect and treat precancerous lesions of the cervix at a single visit, such as low-cost confocal microscopy, could improve early diagnosis and hence outcomes. We piloted a prototype smartphone-compatible confocal micro-endoscope (SCME) among women presenting to a public cervical cancer screening clinic in Kampala, Uganda. We describe the piloting of the SCME device at an urban clinic used by lower cadre staff. Methods: We screened women aged 18 and 60 years, who presented for cervical cancer screening at the Kawempe National Referral Hospital Kampala, and evaluated the experience of their providers (nurses). Nurses received a 2-day training by the study doctors on how to use the SCME, which was added to the standard Visual Inspection with Acetic acid (VIA)-based cervical cancer screening. The SCME was used to take colposcopy images before and after VIA at positions 12 and 6 O'clock if VIA negative, and on precancer-suspicious lesions if VIA positive. We used questionnaires to assess the women's experiences after screening, and the experience of the nurses who operated the SCME. Results: Between November 2021 and July 2022, we screened 291 women with a median age of 36 years and 65.7% were HIV positive. Of the women screened, 146 were eligible for VIA, 123 were screened with the SCME, and we obtained confocal images from 103 women. Of those screened with the SCME, 60% found it comfortable and 81% were willing to screen again with it. Confocal images from 79% of the women showed distinguishable cellular features, while images from the remaining 21% were challenging to analyze. Nurses reported a mean score of 85% regarding the SCME's usefulness to their work, 71% regarding their satisfaction and willingness to use it again, 63% in terms of ease of use, and 57% concerning the ease of learning how to operate the SCME. Conclusion: Our findings demonstrate the feasibility of using the SCME by lower cadre staff in low-resource settings to aid diagnosis of precancerous lesions. However, more work is needed to make it easier for providers to learn how to operate the SCME and capture high-quality confocal images. [ABSTRACT FROM AUTHOR]

Although live streaming is indispensable, live-streaming e-business requires accurate and timely sales-volume prediction to ensure a healthy supply–demand balance for companies. Practically, because various factors can significantly impact sales results, the development of a powerful, interpretable model is crucial for accurate sales prediction. In this study, we propose SaleNet, a deep-learning model designed for sales-volume prediction. Our model achieved correct prediction results on our private, real operating data. The mean absolute percentage error (MAPE) of our model's performance fell as low as 11.47% for a + 1.5-days forecast. Even for a 1-week forecast (+ 6 days), the MAPE was only 19.79%, meeting actual business needs and practical requirements. Notably, our model demonstrated robust interpretability, as evidenced by the feature contribution results which are consistent with prevailing research findings and industry expertise. Our findings provided a theoretical foundation for predicting shopping behavior in live-broadcast e-commerce and offered valuable insights for designing live-broadcast content and optimizing the user experience. [ABSTRACT FROM AUTHOR]

Introduction Traumatic brain injury (TBI) can impact language processing, necessitating language-tailored approaches. Telehealth may expand rural Veterans' access but has unknown feasibility for language preferences. This study explored telehealth/Veterans Video Connect satisfaction for Spanish/English TBI screening. Materials and Methods The study was approved by the VA Caribbean Healthcare System Institutional Review Board and the Research and Development Committee. Mixed methods evaluated telehealth satisfaction in Veterans receiving TBI assessments from October 2021 to October 2023. Surveys included the 16-item Clinical Video Telehealth (CVT) questionnaire on communication, technical factors, coordination, and overall satisfaction, and the 21-item Telemedicine Satisfaction and Usefulness Questionnaire (TSUQ) examining usefulness, ease of use, manners, quality, and satisfaction. Mean domain/item scores were calculated among 57 Veterans, 12 English, and 45 Spanish-speaking. Semi-structured interviews also elicited user experiences from 4 providers and 5 Veterans. Transcripts underwent qualitative coding for themes using Atlas.ti.8. Results On CVT (0-5 scale), overall satisfaction averaged 4.50 (English) and 4.69 (Spanish). Lowest scoring item for English users was easy video connection (4.25), while unclear expectations had the lowest Spanish score (3.60). For TSUQ, overall mean scores were 4.50 (English) and 4.67 (Spanish), with improved health post-telehealth having the lowest average (English 3.33, Spanish 3.67). Qualitatively, Veterans and providers noted strengths like access and communication but weaknesses around connectivity, care delays, and privacy. Differences emerged regarding convenience (Veterans) versus operational barriers (providers). There was a strong positive correlation for Spanish surveys and a moderate correlation for English surveys (r  = 0.71 Spanish surveys, r  = 0.69 English surveys) between TSUQ and CVT for individual respondents. Conclusions Patients conveyed positive experiences, but qualitative data revealed actionable targets for optimization like infrastructure and coordination improvements. Key limitations include small samples and lack of comparison to in-person care. Still, high satisfaction coupled with specific user feedback highlights telehealth's potential while directing enhancements. The results found high Veteran satisfaction with Spanish/English TBI telehealth, but mixed methods illuminated salient domains for better accommodating user needs, particularly regarding logistics and technology. Rigorously integrating experiences with metrics over expanded diverse samples and modalities can further guide refinements to enhance telehealth with a language-tailored approach. [ABSTRACT FROM AUTHOR]

The current transition to the electric car by all users is advancing steadily, and hence the design of these products occupies a prominent place in the car design sector. Likewise, the user experience is one of the determining aspects for the choice of a model. The main objective of this work is to determine, through eye tracking, which areas of an image (in this case, images of the front of cars) are more influential in the perception of the vehicle as either electric or combustion-powered, by segmenting the most important areas of a vehicle and users' perceptions thereof according to gender and other socio-demographic variables. This work provides an experimental design and a way to analyse and draw conclusions from an oculometry study for vehicles, as well as an initial design briefing for the generation of the visual identity of electric vehicles based on the perception of users for different elements of the vehicle. A methodology is proposed that includes the use of tools such as ANOVAs, eye-tracking, heat maps, tracking maps and fixation times. Through this data it is possible to develop optimal designs aligned with the user's perception. [ABSTRACT FROM AUTHOR]

The existing methods for sentiment classification normally ignore that the past experiences retrieved by users under particular situations would affect their sentiment expressions. Furthermore, related research may underutilize user personality to personalize the analysis of storage and retrieval of past experiences. Inspired by the cognition process of human memory, we propose a Personality-Driven Experience Storage and Retrieval (PDESR) model for sentiment classification. Concretely, we first selectively store the user's past experiences in her/his experience bank via personalized input and forget gates. We then adopt personalized output gate to retrieve past experiences from the experience bank. Finally, we integrate the current experience with the retrieved past experiences to classify user sentiment. Specifically, personality is used to personalize the control of which past experiences should be stored in experience bank and which past experiences should be retrieved from experience bank. The experimental results show that PDESR model outperforms the related models in accuracy. [ABSTRACT FROM AUTHOR]

Online vendors often deploy limited-time and limited-quantity cues on their e-commerce sites to influence consumers purchase decisions. Although these scarcity cues can reflect genuine restrictions in the availability of goods, they are increasingly considered as ill-intentioned nudges or 'dark patterns' due to their omnipresence and success in persuading consumers. In an online experiment (N = 202), we examined the effects of limited-time and limited-quantity cues on perceived task load, perceived benevolence, and user experience. Results suggest that participants associated scarcity cues with a lack of benevolence from online vendors. E-commerce site design without scarcity cues provided participants with a superior hedonic and pragmatic user experience. In the case of limited-time scarcity cues, participants reported frustration-related negative emotions. We discuss the implications of these findings from the perspectives of dark pattern researchers, designers, and online vendors. [ABSTRACT FROM AUTHOR]

As artificial intelligence (AI)-powered technology enables the efficient processing of large volumes of comments, more companies, including news publications, are experimenting with and adopting AI moderation to manage their commenting platforms. However, the resulting user experiences have been largely underexplored in relation to these technical advances in comments section management. This study used an experiment to examine the impact of AI filters on individuals' perceptions about comments sections (i.e., bias, credibility, positive and negative affect, and use intention). The findings indicate that AI moderation had statistically significant impacts on perceived credibility and use intention. Beyond the main effects, priming with a deceptive comment issue moderated the impacts of comment filtering on user perceptions as well. [ABSTRACT FROM AUTHOR]

Usability—the extent to which a system can be operated to achieve specified goals with effectiveness, efficiency, and satisfaction—is a hallmark of many successful systems. Identifying usability concerns with existing measures, however, can be problematic. We use biased competition theory (BCT) to explain how interfaces with low usability create attentional interference, which can be measured through mouse movement efficiency (MME). We tested our hypotheses in four studies. Study 1 manipulates attentional interference and examines its influence on MME using eye tracking to validate BCT as an appropriate theoretical lens for our research. Study 2 manipulates usability and shows similar markers of attentional interference in MME on a website with two different populations. Study 3 is a field test that compares the reliability of MME in ranking the usability of different components of a commercial web application to rankings produced by a common perceptual measure of usability: perceived ease of use. Collaborating with a Fortune 100 software vendor, Study 4 further demonstrates the efficacy of MME in ranking the usability of various system components compared to a standard industry measure of usability across three different products. The results consistently show that MME can help identify usability differences among parts of systems. The results have implications for facilitating more efficient and temporally precise usability research and mass-deployable usability testing. [ABSTRACT FROM AUTHOR]

There is a certain failure rate in traditional glaucoma surgery because of the lack of depth information in microscope images. In this work, we present a digital microscope-integrated optical coherence tomography (MIOCT) system and several custom-made OCT-compatible instruments for glaucoma surgery. Sixteen ophthalmologists were asked to perform trabeculectomy and canaloplasty on live porcine eyes using the system and instruments. After surgery, a subjective feedback survey about the user experience was taken. The experiment results showed that our system can help surgeons easily locate important tissue structures during surgery. The custom-made instruments also solved the shadowing problem in OCT imaging. Surgeons preferred to use the system in their future practice. [ABSTRACT FROM AUTHOR]

Objective: examine the prevalence of driver distraction in naturalistic driving when implementing European New Car Assessment Program (Euro NCAP)-defined distraction behaviours. Background: The 2023 introduction of Occupant Status monitoring (OSM) into Euro NCAP will accelerate uptake of Driver State Monitoring (DSM). Euro NCAP outlines distraction behaviours that DSM must detect to earn maximum safety points. Distraction behaviour prevalence and driver alerting and intervention frequency have yet to be examined in naturalistic driving. Method: Twenty healthcare workers were provided with an instrumented vehicle for approximately two weeks. Data were continuously monitored with automotive grade DSM during daily work commutes, resulting in 168.8 hours of driver head, eye and gaze tracking. Results: Single long distraction events were the most prevalent, with.89 events/hour. Implementing different thresholds for driving-related and driving-unrelated glance regions impacts alerting rates. Lizard glances (primarily gaze movement) occurred more frequently than owl glances (primarily head movement). Visual time-sharing events occurred at a rate of.21 events/hour. Conclusion: Euro NCAP-described driver distraction occurs naturalistically. Lizard glances, requiring gaze tracking, occurred in high frequency relative to owl glances, which only require head tracking, indicating that less sophisticated DSM will miss a substantial amount of distraction events. Application: This work informs OEMs, DSM manufacturers and regulators of the expected alerting rate of Euro NCAP defined distraction behaviours. Alerting rates will vary with protocol implementation, technology capability, and HMI strategies adopted by the OEMs, in turn impacting safety outcomes, user experience and acceptance of DSM technology. [ABSTRACT FROM AUTHOR]

Panoramic video multimedia technology has made significant advancements in recent years, providing users with an immersive experience by displaying the entire 360° spherical scene centered around their virtual location. However, due to its larger data volume compared to traditional video formats, transmitting high-quality videos requires more bandwidth. It is important to note that users do not see the whole 360° content simultaneously, but only a portion that is within their viewport. To save bandwidth, viewport-based adaptive streaming has become a significant technology that transmits only the viewports of interest to the user in high quality. Therefore, the accuracy of viewport prediction plays a crucial role. However, the performance of viewport prediction is affected by the size of the prediction window, which decreases significantly as the window size increases. In order to address this issue, we propose an effective self-attention viewport prediction model based on distance constraint in this paper. Firstly, by analyzing the existing viewport trajectory dataset, we find the randomness and continuity of the viewport trajectory. Secondly, to solve the randomness problem, we design a viewport prediction model based on a self-attention mechanism to provide more trajectory information for long inputs. Thirdly, in order to ensure the continuity of the predicted viewport trajectory, the loss function is modified with the distance constraint to reduce the change in the continuity of prediction results. Finally, the experimental results based on the real viewport trajectory datasets show that the algorithm we propose has higher prediction accuracy and stability compared with the advanced models. [ABSTRACT FROM AUTHOR]

Object manipulation is one of the most fundamental tasks in 3D user interfaces. Compared with interacting with objects within reach, remote object manipulation is often more challenging, which is often an iterative process that need clutching. However, while there are many interaction techniques designed for manipulating remote objects, it is not well studied the effects of clutching mechanism on remote object manipulation tasks. In this paper, we present a controlled experiment to evaluate the effects of two clutching mechanisms (Normal Clutching vs. Easy Clutching) on 3-DOF translation, 3-DOF rotation and 6-DOF docking tasks under various tasks settings. The results show that easy clutching can significantly save more completion time and clutching time compared with Normal Clutching. In addition, the results also reveal that easy clutching can be more effective especially when dealing with hard tasks and long user-target distance. We also find that users prefer easy clutching over Normal Clutching, indicating that well designed clutching mechanism can effectively improve user experience in manipulation tasks. [ABSTRACT FROM AUTHOR]

Usability and user experience (UX) are emerging concerns around not only application development but everything designed to be used by people. Evaluation of the UX is, by nature, intensely subjective and time-consuming. The article focuses mainly on Eye Tracking, Usability, and User Experience from a general point of view, with an emphasis on automatic data processing. In recent years, new technological approaches have been emerging to quantify usability testing data and improve process automation. Eye tracking technology is a great way to analyze users' interaction with the product, allowing researchers to discover usability issues and even leverage the power of machine learning to recognize various kinds of emotions linked to users' interactions. Existing research concerned with these three main topics has been methodically explored. For this review, we extensively searched 1988 theme-related articles. One hundred and forty-four articles were selected based on meticulous screening, from which 90 were included in this systematic review. The outcomes reveal a significant shift toward a more technologically advanced evaluation of user experience and usability in various areas. The review proposes several opportunities for future research and missing areas connecting user experience, eye tracking, and machine learning into more products focused on problem pattern identification. [ABSTRACT FROM AUTHOR]

In an industrial production context, when a robotic arm is assigned a new task, it must be re-programmed by specialized personnel with the necessary skills and knowledge. In addition, the robot remains offline while being re-programmed, negatively affecting productivity. Mixed reality opens up the opportunity for a production worker with no programming skills to teach a robot by interacting with the hologram of its digital twin, thus not interfering with the physical robot during re-programming. We describe the design and implementation of a mixed-reality interface to instruct trajectories for a robot end-effector by moving the hologram of its digital twin by hand. The interface supports the integrality of this interaction so that the user can configure the end effector location and orientation simultaneously. We also report a user study (n = 14) to characterize this interface in terms of usability, user experience, and estimated temporal efficiency that it can provide when programming robot trajectories. [ABSTRACT FROM AUTHOR]

This study investigates how feedback from different modalities affect window navigation tasks performed on a touchscreen by younger and older users. Thirty-two younger and 32 older users moved a window toward one of four or eight target areas with no feedback or visual, tactile or visual–tactile feedback. Visual feedback produced significantly higher accuracy and shorter task completion times than tactile feedback. Generally, visual–tactile feedback did not produce statistically higher accuracy or shorter task completion time than visual feedback. However, visual–tactile feedback prevented both younger and older users from overshooting the windows. Additionally, when users attempted to move the window to one of the eight target areas, the visual–tactile feedback helped younger users perform better. According to theories about human processing, the reason for the subtle advantage of visual–tactile feedback is that the unintuitive tactile force induces a higher cognitive load than straightforward visual information during window navigation. Nevertheless, visual–tactile feedback elicited the best user experience, as measured by questionnaires on emotion and fatigue, gesture usability and preference. Furthermore, more older users prefer visual–tactile feedback than younger users. Therefore, visual–tactile feedback should be considered, and the tactile feedback should be modified to be more intuitive in window navigation tasks. Research Highlights Visual feedback improved task performance more than tactile feedback in window navigation task. Visual–tactile feedback did not improve task performance compared to visual feedback. Visual–tactile feedback prevented users from overshooting the window. More older users than younger users prefer visual–tactile feedback. [ABSTRACT FROM AUTHOR]

Essays are one of the most used exams to assess students. Universitas Terbuka Indonesia (Open University) conducts three online essay exams within a week for each first-year course, accounting for 30% of the mid-test score. The university has over 500.000 students and hundreds of courses. However, the limited number of correctors resulted in a time-consuming and ineffective process of checking and scoring each student's essay response. Even score results can be subjective, unfair, lack detailed feedback from students, miss contextual and creative aspects, and be less reliable with complex or non-standard writing. This research proposes a hybrid approach of deep learning models and natural semantic grammar to improve and optimize an AES system, with the following steps: First, the datasets are collected from hundreds of students' answers, each representing a single question. The datasets are pre-processed and augmented to enhance the quantity and variety of the original data and scores. Second, the BERT approach was utilized to transform each text dataset into vector feature spaces using pre-trained model weights. Finally, a prediction score model was generated using the BiLSTM method. The experiment's results show that the model had an average Cohen's Kappa score of 0.749 and the highest Cohen's Kappa score of 0.91. This BERT-BiLSTM optimization model also has a better Cohen's Kappa Score average (0.820) than the ATT-CNN-LSTM, BERT-XLNET, R2BERT, and CNNBiLSTM models. After conducting a test on 46 lecturers, the results showed that the average time taken to examine one course for each student decreased to 1 minute and 2 seconds. Additionally, 92.75% of the lecturers found the process of checking responses to be fairer and more objective. In the results of the trial with 200 students, the mean percentage of question indications related to UI/UX, fairness, responsiveness, rubric, feedback, and transparency across students was 93.72%. [ABSTRACT FROM AUTHOR]

While various combinations of business eco-systems (desktop, laptop, tablet, mobile, smartwatch, and TV) and platforms (apps, websites, and social media pages) can be used to shop online, little is known about consumers' preferences to choose one over another. The user experience on each eco-touchpoint and eco-channel is not the same. By comparing four groups of respondents (595 observations) using two different systems (Brand Website vs. Brand Facebook page) and two different channels (PC vs. Smartphone), this research examines how utilitarian values, hedonic values, satisfaction, and flow experience, might differ across these various business ecosystems for those booking a vacation. Participants were randomly guided to one of the four experimental groups based on their device. The results show that flow experience, utilitarian values, and hedonic values influence booking intentions. These effects differ from digital platform to digital platform and from one e-channel to another. This research deepens our theoretical understanding of the online user experience across business ecosystems and gives practical insights into how e-retailers could enhance consumers' shopping experiences through the different e-channel and digital platforms that contribute, in a different way, to the online experience. [ABSTRACT FROM AUTHOR]

Bicycle sharing systems are becoming highly popular and there have accumulated a large volume of users' trajectory data. In a bicycle sharing system, most users' borrowing and returning behaviors are arbitrary. Importantly, the bicycle sharing system is affected by weather, time as well as other dynamic factors under non-classical conditions. These factors make the scheduling of shared bicycles imbalanced, which is a typical machine learning problem under non-classical conditions. In addition, imbalanced bicycle scheduling impacts user experiences and causes huge economic losses. In this study, we propose a three-in-one dynamic shared bicycle demand forecasting model under non-classical conditions. The proposed model contains three essential and specific techniques: (1) Clustering stations based on the idea of data field. The activity degree of a station is calculated by constructing a bicycle transition network. The stations' geographical locations and the bicycle transition patterns are taken into consideration. Then, nearby stations with similar transition patterns are aggregated into a cluster based on data field. (2) Modeling dynamic factors under non-classical conditions. The Pearson correlation coefficient is used to choose the most relevant dynamic weather features from real data. These features are transformed into a three-dimensional vector by taking into account the historical demand for bicycles in the station cluster. (3) Predicting bicycle demand using a LSTM (Long Short-Term Memory) network with multiple features that learns the features from the three-dimensional vector. The bicycle demand in each cluster is forecasted in a real-time and dynamic fashion every ten minutes. These three techniques collectively achieve one goal: accuracy prediction with low errors in real-time response. Extensive experiments are conducted on real Citi Bicycle data to compare the proposed model with traditional machine learning algorithms as well as the state-of-the-art approaches. The results demonstrate the superior performance of the proposed model in different evaluation measurements. [ABSTRACT FROM AUTHOR]

Communication technology plays a crucial role in facilitating remote collaborative work. This study investigated sex differences in Perceived Participation Equality and User Experience across different communication formats, i.e., face-to-face communication, conventional video conferences, and Virtual Reality (VR). An empirical study was conducted involving 15 groups, each comprising three participants, who engaged in a decision-making task. A research model was developed to evaluate the interplay between perceived participation equality, empathy, and immersion. This model was employed across three communication conditions and included both male and female participants. These findings on sex differences in user experience could help create a connected, cohesive, and productive remote collaborative work environment. [ABSTRACT FROM AUTHOR]

Police investigation in real-life crime scenes is an essential aspect of forensic science education. However, the practicality of bringing young investigators to actual crime scenes is often hindered by the costs and challenges involved. In order to overcome these obstacles, new technologies such as mixed reality (MR) are being explored as potential solutions. MR technology offers an interactive and cost-effective way to simulate real-life crime scenes, providing a valuable training experience for young investigators. This paper presents a novel design of a MR system using Microsoft HoloLens 2.0, which is tailored to work in a spatial 3D scanned and reconstructed crime scene using FARO point cloud 3D scanner X130 blended with photogrammetry techniques. The system was developed through the lens of Experiential Learning Theory and designed using a participatory approach, providing a cost-effective solution to help trained Kuwaiti police officers enhance their investigative skills. In order to evaluate the system’s user experience and user interaction, the Questionnaire of User Interaction Satisfaction and User Experience Questionnaire were utilised. Forty-four young police officers evaluated the system. Police students showed positive levels of satisfaction with user interaction and overall user experience with minimal negative feedback. Female students showed higher satisfaction with the overall impression compared to male students. Based on the positive feedback regarding the system expansion, the system will be taken into the commercialisation stage in the future to be provided as an essential tool for crime scene education and investigation practices. [ABSTRACT FROM AUTHOR]

With advancements in autonomous driving technology, the variety of activities that can be performed in a vehicle has increased. This improves the possibility of watching virtual reality (VR) content on a head-mounted display (HMD). However, unlike VR used in stationary environments, in-car VR can lead to discomfort and motion sickness due to the vehicle movements. Additionally, the obstruction of the outside view during driving may cause user anxiety. In this study, we investigated, for the first time, the effect of dynamic road environments, such as turns, stops, and speed bumps, on the in-car VR experience. Based on our findings, we included situational awareness (SA) cues in the in-car VR content to help users perceive their surroundings and improve the user experience. We conducted a user study with thirty participants to validate the impact of these cues. Consequently, we discovered that the Dynamics cue, which provides SA information while maintaining the context of the VR content, improves user immersion and trust while easing VR motion sickness. [ABSTRACT FROM AUTHOR]

With the increase of Internet users, the traditional music recommendation model cannot meet the increasing personalized needs of users. The single deep cross network model has some defects in music recommendation, such as poor stability and inability to process complex data. To overcome the shortcomings of existing models, a new hybrid music recommendation model combining CatBoost algorithm and deep cross network is constructed to improve the recommendation performance and better meet the individual needs of users. Then the performance of the hybrid model is compared with other algorithms. The results showed that the accuracy of the proposed hybrid algorithm was up to 92.7%, which was superior to the comparison algorithm. In comparison with other single model and hybrid model, it is found that the proposed model was more than 0.05% higher than other models in the four indices of AUC area, accuracy, precision and recall. The above results showed that the proposed hybrid music recommendation model could efficiently process data information and provide users with accurate personalized music recommendation. This study not only promoted the development of music consumption and creation, but also found that the CatBoost-DCN hybrid model was significantly effective in improving recommendation performance. This finding provides a more efficient recommendation strategy for music platforms and has far-reaching significance for improving user experience and satisfaction. [ABSTRACT FROM AUTHOR]

A new energy vehicle cab control platform based on human-computer interaction is proposed from control device design and control signal acquisition design. A driving mode conversion algorithm based on the threshold is combined with the existing intelligent vehicle driving mode conversion. The MATLAB and Python hybrid program is used to realize the simulation experiment of the driving control algorithm of new energy vehicles. The system can conduct interactive simulations of various typical driving scenarios. Data acquisition and dynamic display functions can present the operating status of new energy vehicles most directly and efficiently. The system can integrate and intelligently process vehicle driving data, road environment data and external data from multiple dimensions of the "vehicle-road-network". The experimental results show that the system enhances the user experience of new energy vehicles and improves the efficiency of human-computer interaction. [ABSTRACT FROM AUTHOR]

The intense competition amongst travel apps, regardless of their popularity, has made repurchase intention a critical financial challenge for travel providers. By combining the Stimulus-Organism-Response (S-O-R) framework and the New Economic Geography (NEG) theory, this research examined how city tier disparities influence the experiential features of travel apps and user repurchase intentions. Survey data was acquired from 739 travel app users in China. The findings indicate that a set of context-based experiential features significantly influences utilitarian and hedonic values, and consequently leading to a notable increase in repurchase intentions. In addition, the outcomes of a multigroup analysis revealed that China's users located in tier 1 city (i.e. Beijing) and tier 3 city (i.e. Kunming) had distinct effects on the hypothesised relationships. Theoretically, the research provides extensive implications to information systems and tourism literatures; whilst offering some actionable insights to travel practitioners. [ABSTRACT FROM AUTHOR]

In response to the burgeoning interest in the Metaverse—a virtual reality-driven immersive digital world—this study delves into the pivotal role of AI in shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, and potential future developments, our research draws from a comprehensive analysis grounded in meticulous methodology. The study, informed by credible sources including SD, Scopus, IEEE, and WoS, encompasses 846 retrieved studies. Through a rigorous selection process, 54 research papers were identified as relevant, forming the basis for a specific taxonomy of AI in the Metaverse. Our examination spans diverse dimensions of the Metaverse, encompassing augmented reality, virtual reality, mixed reality, Blockchain, Agent Systems, Intelligent NPCs, Societal and Educational Impact, HCI and Systems Design, and Technical Aspects. Emphasizing the necessity of adopting trustworthy AI in the Metaverse, our findings underscore its potential to enhance user experience, safeguard privacy, and promote responsible technology use. This paper not only sheds light on the scholarly interest in the Metaverse but also explores its impact on human behavior, education, societal norms, and community dynamics. Serving as a foundation for future development and responsible implementation of the Metaverse concept, our research identifies and addresses seven open issues, providing indispensable insights for subsequent studies on the integration of AI in the Metaverse. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The rapid development of virtual reality technology and the Internet of Things (IoT) has provided new possibilities for user experience, and a variety of new products have emerged, especially in the field of painting, where the combination of these two provides a new platform for innovative artistic expression. OBJECTIVES: This study takes IoT products in the art field as an example to analyze the user experience in virtual reality environments and the impact of technological innovations on IoT products, as well as to explore the potentials and limitations of this emerging form of products and forms of painting. METHODS: In this study, the author constructed a virtual reality painting environment, utilized IoT technology to collect data from the user's painting process, and combined quantitative and qualitative analysis methods to assess user experience and technological innovation comprehensively. RESULTS: In the virtual reality environment, the user experience was significantly improved, and the users were more immersed in the painting process and felt more robust creativity and expression. Meanwhile, the application of Internet of Things (IoT) technology also provides more possibilities for drawing; for example, using smartpens makes the drawing process more smooth and natural. CONCLUSION: IoT painting with user experience and technological innovation in a virtual reality environment can provide a new creative platform for artists and bring a richer artistic experience to the audience, showing the feasibility and broad prospect of IoT products based on a virtual reality environment. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications. Design/methodology/approach: This article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process. Findings: The authors' analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples. Originality/value: While there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans. [ABSTRACT FROM AUTHOR]

ObjectivesMethodsResultsConclusionsClinical ImplicationsThis qualitative study examines the experiences of older adults with Empower@Home, an older adult-centered, layperson-supported internet-based cognitive behavioral therapy (iCBT) program for depression, with a focus on engagement-related factors.Adults aged 50 or older with at least mild depressive symptoms were recruited from multiple resources. A random subset of participants was invited for a semi-structured qualitative interview during a post-intervention assessment conducted over the phone (N = 148). Interviews were recorded and transcribed verbatim. Transcripts were coded in a multi-phase, team-based approach utilizing inductive coding techniques, followed by thematic analysis to identify key themes.Six key themes regarding engagement emerged: the importance of a structured, skills-based, and self-paced approach; the essential role of weekly coaching for accountability, web interface’s ease of use and accessibility; narrative engagement dynamics; enhanced learning through multimodal instruction; and tailoring challenges in iCBT.Older adult-centered, layperson-supported, and multimodal iCBT can effectively engage older adults in managing their mental health.iCBT programs can be integrated into various community aging settings. By leveraging each setting’s strengths and involving a range of professionals, these programs can play an important role in enhancing older adults’ mental health. [ABSTRACT FROM AUTHOR]

AbstractWomen are increasingly turning to mHealth apps as primary sources for breastfeeding education and support. The rising prevalence of mHealth apps presents a unique opportunity to explore their impact on women’s breastfeeding experiences. Understanding user perspectives of these apps, especially focusing on their persuasive design aspects, can offer valuable insights for enhancing maternal and child health outcomes. These insights can optimize these technologies to align with user values and promote sustained healthy behaviors. This study aimed to undertake an empirical exploration of users’ experiences with the persuasive design features employed in the Milky Way mHealth app and align the findings with human values within the value-sensitive design (VSD) framework. Semi-structured interviews were conducted with a group of women who had used the Milky Way app. Interview data were analyzed using an inductive thematic approach. To provide insight into users’ values mediated by the app, a deductive analysis complemented by a data-driven inductive approach was performed. Data analysis was supported by NVivo data-management software. Findings from the study demonstrate that the persuasive design elements in the Milky Way app significantly influence users’ experiences. The analysis provided four primary themes: Informative, Easy to use, Credible, and Social connection. Overall, participants praised the app’s trustworthy educational content and support features as valuable resources for addressing breastfeeding challenges and obtaining reliable support. Participants expressed concerns about information overload and emphasized the importance of personalized support and evidence-based tailored recommendations. The results demonstrated that users’ values were expressed and embedded within the app. The analysis indicated an alignment between the app design features and the values and opinions of the users within the VSD framework. This study interprets women’s perspectives utilizing the Milky Way app and highlights the significance of its evidenced-based educational and support features. Further investigation into potential user values like privacy, remains crucial for a comprehensive user-centered app design. The empirical insights gained from this study contribute to future research, informing the development and design of ethically considerate persuasive mHealth apps to maintain their relevance in promoting positive user experiences and facilitating meaningful behavior change. [ABSTRACT FROM AUTHOR]

Background: The principles of dynamic consent are based on the idea of safeguarding the autonomy of individuals by providing them with personalized options to choose from regarding the sharing and utilization of personal health data. To facilitate the widespread introduction of dynamic consent concepts in practice, individuals must perceive these procedures as useful and easy to use. This study examines the user experience of a dynamic consent-based application, in particular focusing on personalized options, and explores whether this approach may be useful in terms of ensuring the autonomy of data subjects in personal health data usage. Methods: This study investigated the user experience of MyHealthHub, a dynamic consent-based application, among adults aged 18 years or older living in South Korea. Eight tasks exploring the primary aspects of dynamic consent principles–including providing consent, monitoring consent history, and managing personalized options were provided to participants. Feedback on the experiences of testing MyHealthHub was gathered via multiple-choice and open-ended questionnaire items. Results: A total of 30 participants provided dynamic consent through the MyHealthHub application. Most participants successfully completed all the provided tasks without assistance and regarded the personalized options favourably. Concerns about the security and reliability of the digital-based consent system were raised, in contrast to positive responses elicited in other aspects, such as perceived usefulness and ease of use. Conclusions: Dynamic consent is an ethically advantageous approach for the sharing and utilization of personal health data. Personalized options have the potential to serve as pragmatic safeguards for the autonomy of individuals in the sharing and utilization of personal health data. Incorporating the principles of dynamic consent into real-world scenarios requires remaining issues, such as the need for powerful authentication mechanisms that bolster privacy and security, to be addressed. This would enhance the trustworthiness of dynamic consent-based applications while preserving their ethical advantages. [ABSTRACT FROM AUTHOR]

Background: While mobile-linked point-of-care diagnostics may circumvent geographical and temporal barriers to efficient communication, the use of such technology in community settings will depend on user experience. We conducted a scoping review to systematically map evidence on user experiences of mobile-linked point-of-care diagnostics in community healthcare settings published from the year 2016 up to the year 2022. Methodology: We conducted a comprehensive search of the following electronic databases: Scopus, Web of Science, and EBSCOhost (Medline, CINAHL, Africa-wide, Academic Search Complete). The inter-reviewer agreement was determined using Cohen's kappa statistic. Data quality was appraised using the mixed method appraisal tool version 2018, and the results were reported according to the preferred reporting items for systematic reviews and meta-analyses for scoping reviews (PRISMA-ScR). Results: Following the abstract and full article screening, nine articles were found eligible for inclusion in data extraction. Following the quality appraisal, one study scored 72.5%, one study scored 95%, and the remaining seven studies scored 100%. Inter-rater agreement was 83.54% (Kappa statistic = 0.51, p < 0.05). Three themes emerged from the articles: approaches to implementing mobile-linked point-of-care diagnostics, user engagement in community-based healthcare settings, and limited user experiences in mobile-linked point-of-care diagnostics. User experiences are key to the sustainable implementation of mobile-linked point-of-care diagnostics. User experiences have been evaluated in small community healthcare settings. There is limited evidence of research aimed at evaluating the usability of mobile-linked diagnostics at the community level. Conclusion: More studies are needed to assess the user experience of mobile-linked diagnostics in larger communities. This scoping review revealed gaps that need to be addressed to improve user experiences of mobile-linked diagnostics, including language barriers, privacy issues, and clear instructions. [ABSTRACT FROM AUTHOR]

Background: Monitoring the recovery trajectory during and after hospitalization can be a valuable method to observe whether additional care is needed to optimize recovery. Hand grip strength tests are commonly used to measure an individual's physical condition. Eforto® is a system to monitor hand grip strength and grip work as measures of recovery. We examined the feasibility of daily repeated hand grip tests measured with Eforto® in geriatric inpatients, during hospitalization and at home after discharge. Methods: Geriatric inpatients (n = 191) were evaluated for grip strength and grip work with Eforto®, twice daily during their admission. We calculated attempt and success rates. Participants were divided into complete, high, moderate, and low attempt/success rate groups to study differences in patient characteristics. Reasons for non-attempt and unsuccessful tests were categorized and analyzed. Nine participants were interviewed about acceptability and user experience within the hospital setting. Four out of twenty participants accepted the invitation to continue the measurements after discharge at home for 4 weeks and were interviewed about acceptability and user experience. Results: Across the 191 participants, the attempt rate was 85% and 86% of the attempted tests was successful. The main reasons for non-attempt were that the patient felt physically unwell (41%), and that the patient was otherwise engaged, for example receiving care or undergoing medical tests (40%). Measurements were unsuccessful mostly because of the patient not having enough strength to reach the 80% threshold needed for the grip work test (60%). Participants in the complete and high attempt/success rate groups had a shorter length of stay (p<0.05) and a lower mortality (p<0.05) than participants in the moderate/low groups. The interview data showed good acceptability and user experience during hospitalization. The acceptability was strengthened by experienced usefulness. Self-monitoring at home resulted in low inclusion rate (20%) and low success rate (25%), with the uncertain time after discharge from the hospital as the main barrier. Conclusions: For most patients, the tests were feasible in the supervised hospital setting. At-home testing with Eforto® is challenging, primarily because of the uncertain time after discharge from the hospital. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the potential contribution of affective touch to the user experience and robot performance in human-robot interaction, with an in-depth look into upper-limb prosthesis use as a well-suited example. Research on providing haptic feedback in human-robot interaction has worked to relay discriminative information during functional activities of daily living, like grasping a cup of tea. However, this approach neglects to recognize the affective information our bodies give and receive during social activities of daily living, like shaking hands. The discussion covers the emotional dimensions of affective touch and its role in conveying distinct emotions. In this work, we provide a human needs-centered approach to human-robot interaction design and argue for an equal emphasis to be placed on providing affective haptic feedback channels to meet the social tactile needs and interactions of human agents. We suggest incorporating affective touch to enhance user experience when interacting with and through semi-autonomous systems such as prosthetic limbs, particularly in fostering trust. Real-time analysis of trust as a dynamic phenomenon can pave the way towards adaptive shared autonomy strategies and consequently enhance the acceptance of prosthetic limbs. Here we highlight certain feasibility considerations, emphasizing practical designs and multi-sensory approaches for the effective implementation of affective touch interfaces. [ABSTRACT FROM AUTHOR]

AbstractSmart Predict is a novel two-tablet application developed to improve conversational efficiency for people who use augmentative and alternative communication (AAC) devices. The Smart Predict system consists of two distinct applications that were built for use with two Android tablets and/or phones with Bluetooth® capability. One application is referred to as the AAC User app and the second application is called the Partner app. Smart Predict integrates vocabulary supplementation from communication partners in real-time while persons who rely on AAC generate text and maintain full control to choose words within a word prediction line. Using single-case research designs with adapted alternating treatments under two counterbalanced treatment conditions (AAC User app alone versus AAC User app + Partner app), we examined message efficiency with persons who rely on switch scanning for on-screen keyboards. User experience questionnaires were administered to determine satisfaction and workload, and to provide feedback for future development efforts. Three adults with complex communication needs and motor impairments conversed with research staff about fourteen movie trailers. Characters per minute and switch selections per character produced by the person with complex communication needs and motor impairments were measured during 15-minute interviews to represent communication speed and effort. Results indicate that message efficiency increased with the dual-tablet Smart Predict, indicating faster message generation with the vocabulary supplementation system. User satisfaction increased and perceived effort decreased under the Smart Predict conditions. Given these results within a research and development framework, the Smart Predict concept is a viable feature that could be considered within smart AAC technologies. [ABSTRACT FROM AUTHOR]

In the vision of Industry 5.0, collaborative robots (or cobots) play a central supporting role in various industries, especially manufacturing. Close interaction with cobots requires special attention to user experience to fully exploit the benefits of this paradigm. Consequently, understanding the impact of a cobot's physical size on user experience becomes critical to optimizing human–robot collaboration (HRC). This research aims to investigate the relationship between cobot size (UR3e – small cobot vs. UR10e – large cobot) and user experience in HRC contexts, in conjunction with other factors (i.e., cobot movement speed and product assembly complexity). Through a series of controlled experiments involving 32 participants, user experience data were obtained by collecting physiological measures (i.e., electro-dermal activity, heart activity, eye-tracking metrics) and subjective responses with questionnaires (i.e., perceived workload, interaction quality, and affective state). Results showed that the large cobot was generally perceived to be safer, more natural, efficient, fluid, and trustworthy. With the large cobot, there was a decrease in dominance; however, it was offset by the learning effect. Perceived workload was mainly influenced by product complexity. No clear difference in terms of mental strain emerged from the physiological data comparing the cobot sizes. In addition, the interaction term between cobot size and cobot movement speed never emerged as significant. The results of this research can offer practical insights to improve the effectiveness and acceptance of cobots during the implementation phase. [ABSTRACT FROM AUTHOR]

Real walking is considered as the best locomotion metaphor to explore virtual environments in terms of user experience. In addition to being intuitive for the user, walking captures the true feelings of motion since the visual and proprioceptive sensations are harmonized well. The major disadvantage of choosing walking over other locomotion metaphors involves the physical constraints of the available space, which is usually considerably smaller than the virtual environment. To address this issue, redirected walking (RDW) introduces slight mismatches between a user's visually perceived path and their actual walking pattern, compelling them to subconsciously compensate for the inconsistency by adjusting their walking trajectory. As a result, users are steered to a certain degree, and expansive virtual environments are effectively compressed into smaller physical spaces. Among others, particularly predictive RDW offers immense potential for growth since it unites various algorithmic systems, whereas many approaches from literature depend on drastic restrictions like single-user constraints or architectural limitations to ensure real-time performance. This work presents two novel predictive RDW systems that allow multiple physically colocated users to explore independent and unconstrained virtual environments. The systems rely on two new implementations of prediction systems based on clothoid trajectory generation combined with a cost-based planning concept built on nonharmonic artificial potential fields (APFs), which inherently allow non-convex and dynamic physical environments. Using the APFs, three additional RDW conditions popular in the literature are implemented for comparison purposes. The five RDW concepts are then validated in an extensive user study with 150 participants conducted in 75 pairs. The results indicate that the novel predictive RDW systems outperform the three systems from literature, except for particular sections of the virtual environment with specific architectural traits. [ABSTRACT FROM AUTHOR]