Playing cards have long been a central feature in board games, despite their physical limitations, such as fragility, and repetitive content. Yet, while HCI research increasingly explores augmented board games, interactive playing cards remain underexplored. In this work, we investigate the use of e-paper displays as interactive playing cards. Based on a formative study with eight game designers, we designed and implemented InkStack—a prototype consisting of e-paper cards, a dedicated programmer, and a web application for card customisation. We then evaluated InkStack in a within-subject study with n = 20 participants, comparing its use across four board game mechanics against traditional paper cards and a smartphone. Results show that InkStack is preferred for more complex mechanics, whereas paper and smartphones are sufficient for simpler tasks. The findings also highlight how customisation and versatility can enhance gameplay and enable novel forms of interaction.

While technology is widely recognised as a tool to enhance wellbeing, there is no clear consensus on the best practices for design. Persuasive strategies, such as nudging, are often seen as effective but raise ethical concerns. Reflection-based approaches offer a potential alternative. However, a clear understanding of the balance between these strategies is crucial for designing effective and ethically sound wellbeing technologies. We conducted two studies with a total of n = 163 participants comparing nudging and reflection techniques using contrasting prototypes of fitness wearables. We verified that the prototypes represented key nudging and reflection strategies through expert feedback. Our mixed-methods inquiry shows that certain nudging strategies are perceived as more malicious and more likely to provoke rumination, i.e., negative thought cycles, compared to other wellbeing strategies. We contribute insights to inform design decisions for wellbeing technologies, balancing effectiveness with ethical considerations.

Figure 1: In this study, we explore the potential of mobile desks to provide flexible and adaptive workspace solutions for promoting healthier office habits.We designed a mobile desk capable of autonomous movement and evaluated it in three configurations: manual control using a crank, control via a physical control panel, and fully automated adaptive operation.

This paper presents a novel approach to visual attention training for novice tram drivers by combining a tram driving simulator with eye-tracking-enabled Augmented Reality (AR) glasses. Based on previous eye-tracking studies and interviews with tram driving instructors, we identified issues related to the attention distribution of novice drivers. That is, we found that driver field-of-view areas critical to tram-driving safety, are often neglected by novices. For instance, such areas enable drivers to check the right-side mirror before turning right. The training system proposed here aims to direct novice visual attention to critical areas by highlighting overlooked regions, displaying directional arrows, or providing auditory cues from the left or right side. Integration of real-time eye tracking into AR glasses supports continuous monitoring of trainee gaze patterns, offering instructors valuable information on trainee visual behavior. Such real-time data allows for dynamic adaptation of training cues, ensuring that visual attention guidance is both relevant and personalized. The proposed system has the potential to enhance situational awareness among tram drivers.

What if the future of assistive technology isn’t built in labs, but by our memories of science fiction? Departing from traditional design fiction approaches that rely on curated prompts, we asked non-expert designers to prototype low-fidelity assistive systems for planetary settlements based on their own remembered cultural references. Across two workshops (n = 21) and a group interview (n = 5), participants prepared prototypes. Reflexive thematic analysis of their designs revealed five core themes: cultural templates as design scaffolds, speculative mobility logics, affective support, ethical concerns around system autonomy, and adaptive technologies responsive to alien conditions. These findings suggest that science fiction serves as aesthetic inspiration and an embedded framework. We argue that cultural memory can surface user expectations of assistive agents, informing the design of future human-agent interactions.

Recent technological advances popularized the use of image generation among the general public. Crafting effective prompts can, however, be difficult for novice users. To tackle this challenge, we developed PromptMap, a new interaction style for text-to-image AI that allows users to freely explore a vast collection of synthetic prompts through a map-like view with semantic zoom. PromptMap groups images visually by their semantic similarity, allowing users to discover relevant examples. We evaluated PromptMap in a between-subject online study ($n=60$) and a qualitative within-subject study ($n=12$). We found that PromptMap supported users in crafting prompts by providing them with examples. We also demonstrated the feasibility of using LLMs to create vast example collections. Our work contributes a new interaction style that supports users unfamiliar with prompting in achieving a satisfactory image output.

Many interactive systems that support tabletop games either augment the experience with additional elements or transform game components into digital counterparts, e.g., using mixed reality. However, as many users prefer tangible game elements, digital augmentations can disrupt the immersion they seek to enhance, often due to the complexity of the hardware used. Responding to this challenge, we designed a screenless interactive tabletop system with capacitive sensing. The system is suitable for novice players and provides automatic score-keeping. Our method eliminates the need for external sensors and retains all original game pieces intact. We evaluated our system in a study with a forest planting game (n = 20). Gameplay with our system exhibited shorter turn duration, and participants adopted more effective strategies than in traditional gameplay. These results underscore the potential of screenless interactive tabletops to amplify the gaming experience without causing distractions.

Assistive technologies are vital for individuals with physical limitations, and understanding the diverse impact of physical pain is crucial for designing effective assistive robotic solutions. Through an iterative study, we aim to develop comprehensive design recommendations that cover personalization, concealment, compatibility, ease of use, preventive measures, skin-friendliness, affordability, adaptability, and multimodal features. These recommendations prioritize user experience and acceptance, ultimately contributing to innovative and user-centric assistive robotic technologies.

Augmented reality (AR) is emerging in visual search tasks for increasingly immersive interactions with virtual objects. We propose an AR approach providing visual and audio hints along with gaze-assisted instant post-task feedback for search tasks based on mobile head-mounted display (HMD). The target case was a book-searching task, in which we aimed to explore the effect of the hints together with the task feedback with two hypotheses. H1: Since visual and audio hints can positively affect AR search tasks, the combination outperforms the individuals. H2: The gaze-assisted instant post-task feedback can positively affect AR search tasks. The proof-of-concept was demonstrated by an AR app in HMD and a comprehensive user study (n=96) consisting of two sub-studies, Study I (n=48) without task feedback and Study II (n=48) with task feedback. Following quantitative and qualitative analysis, our results partially verified H1 and completely verified H2, enabling us to conclude that the synthesis of visual and audio hints conditionally improves the AR visual search task efficiency when coupled with task feedback.

While the neck-worn garments are an integral part of fashion around the world, few interactive systems utilize them as form factors. As neck is located beyond the natural field of view, it offers wide opportunities for subtle haptic interaction. While tactile acuity of the neck is well studied in clinical research, these results are difficult to apply for interactive systems. In this paper, we present HaptiCollar - a research prototype for studying tactile acuity of the neck. We conducted two experimental studies comparing different numbers of actuators on the neck, in two contextual scenarios simulating stationary and mobile use. We found that pattern actuations are recognised more effectively than single-point vibrations, independently of use scenario. Our analysis shows that introducing a primary cognitive task and motion does not impair recognition accuracy, while highly increasing the cognitive load. Our work contributes empirical insights for designing haptic interaction for future neck-worn interfaces.

Abstract Remote health monitoring (RHM) provides various benefits to older adults, but its use is still limited. Remote monitoring may help in avoiding emergencies and prolong users’ independence. To understand how to design systems which support older adults, we studied a large-scale remote health monitoring system. The system used fitness-grade smartwatches to monitor the vital signs of more than 2000 users constantly. To probe the lived experience of using RHM, we conducted an explorative interview study (N = 41) with operators, carers, and users of the RHM system. Our thematic analysis reveals that personalisation of care ecology is crucial for developing users’ confidence and trust in the system. We found that participation in RHM may catalyse positive changes in older adults’ lifestyles. Based on our findings, we formulate five recommendations for designing future health monitoring systems. Our work contributes to insights into the lived experience and stakeholder ecology of health monitoring systems.

As a cutting edge technique requiring high-precision equipment, augmented reality (AR) and its users are influenced by the ambient environment. With the tremendous effect brought by COVID-19 pandemic, most people have shifted from on-site working to remote working. In this study, we propose an initial solution to explore the impact of COVID-19 pandemic on AR users working in these two situations. We develop a prototype application facilitated with gamification process in which users are requested to play an AR game in headset both in on-site and remote working environments. This game, which is highly dependent on the ambient environment, enables people to memorize, distinguish, and place virtual objects when immersing themselves into different surroundings with distinct distractors. We envision to conduct more user studies investigating how COVID-19 affects AR users, which could lead to more in-depth studies in the future.

A rapid shift towards online education witnessed in recent years, re-shaped the necessary skill set of a modern teacher. Delivery of classes in remote and hybrid format became an integral part of education. However, providing feedback on lecturing technique at scale still remains a challenge. We present the design and implementation of Speecoach – an online tool for lecturers that analyses the audio recordings of lectures and provides feedback concerning their style of speech delivery. Our system analyses the pitch, speech rate and volume dynamics of a recording and compares it against the reference model based on a data set of professional-grade talks. Our tool facilitates teachers’ self-development by providing contextualised feedback on the parameters of their speech and comparing it to different forms of public addresses. Our work contributes a prototype tool facilitating public speaking and envisions future developments and studies in life-long learning of education professionals.

Augmented reality (AR) is today becoming more widely utilized as it allows for interacting with the virtual objects. In this study, we propose an head-mounted display (HMD) AR system supporting book searching with visual cues. The visual cue is represented as a light-green blob hinting users for task completion, which significantly strengthens the overall performance. The system is implemented by using Microsoft HoloLens 2. The proof-of-concept version of the proposed solution is demonstrated in a pilot user study (n=8) comprising an experimental group (with visual cues, n=4) and a control group (without visual cues, n=4), followed by quantitative analysis of task completion time (TCT) and NASA task load index (TLX). The results show that our proposed HMD AR solution improved the task performance and had cognitive benefits for book searching tasks.

Human perception lacks the capabilities to accurately assess distance. The recent Covid-19 pandemic outbreak rendered this ability particularly important. Augmenting our sense of distance can help maintain safe separation from others when required. To explore how systems can help users maintain physical distance, we designed, implemented and evaluated Gapeau—a head-mounted system for augmenting the sense of distance. Our system uses proximity sensors and thermal sensing to detect and measure the distance to other people. We conducted a validation protocol, an experiment, in which we compared different feedback modalities, and an in-the-wild study to evaluate Gapeau’s performance and suitability for use in social contexts. We found that our system enabled users to more accurately determine whether they were maintaining a safe distance from others. Vibration and auditory feedback were found most effective and usable. Gapeau was perceived as socially acceptable. Our work contributes insights for augmented sensing systems with social relevance.

Due to recent technological advancements, governments can exercise an unprecedented amount of power over their citizens. It is vital to understand how governments impose restrictions on citizens through digital technologies, especially if those restrictions can save lives. Here, we look at the case of Home Quarantine—a Polish government-mandated smartphone application whose use has become mandatory during the COVID-19 crisis. Users in quarantine are legally required to use the application, reporting location and take selfie photographs. We conducted an autoethnographic diary study of one author using the app during quarantine and interviews with 23 users. We found that the app assisted in creating quarantine life routines and affected social interactions. Users connected with the world outside quarantine through selfies. We also uncovered key reservations our users had about using this app. Our work broadens the understanding of location-based apps and practices around surveillance technologies.

Due to multimodal approach, the virtual reality experiences become increasingly more immersive and entertaining. New control modalities, such as brain-computer interfaces (BCIs), enable the players to engage in the game with both their bodies and minds. In our work, we investigate the influence of employing BCI-driven mechanics on player’s in-game immersion. We designed and implemented an escape room-themed game which employed player’s mental states of focus and relaxation as input for selected game mechanisms. Through a between-subject user study, we found that controlling the game with mental states enhances the in-game immersion and attracts the player’s engagement. At the same time, using BCIs did not impose additional cognitive workload. Our work contributes qualitative insights on psychocognitive effects of using BCIs in gaming and describing immersive gaming experiences.

Although robots have become an everyday companion for medical personnel in various domains, few solutions have been crafted to facilitate therapy of psychological disorders. Treatment of phobias is one of the areas which could highly benefit from introducing device-mediated interactions. To address the challenges of arachnophobia therapy, we designed SpiderHand - robot-based system for quasi-direct interaction with unpleasant creatures. We first identified the challenges of cognitive-behavioral therapy through interviews with a professional therapist and elicited functional requirements through experimental inquiry. Then, we designed and implemented an operational research prototype of motion-controlled robotic manipulator including remote touch features. We contribute a complete implementation toolkit for constructing DIY robot manipulator and provide insights on designing interactive robots for psychological therapy.

While the sport of golf offers unique opportunities for people of all ages to be active, it also requires mastering complex movements before it can be fully enjoyed. Extensive training is required to begin achieving any success, which may demotivate prospective players away from the sport. To aid beginner golfers and enable them to practice without supervision, we designed Subtletee---a system that enhances the bodily awareness of golfers by providing feedback on the position of their feet and elbows. We first identified key problems golf beginners face through expert interviews. We then evaluated different feedback modalities for Subtletee---visual, tactile and auditory in an experiment with 20 beginner golfers. We found that providing proprioception feedback increased the users' performance on the driving range, with tactile feedback providing the most benefits. Our work provides insights on delivering feedback during physical activity that requires high precision movements.

While we usually have no trouble with orientation, our sense of direction frequently fails in the absence of a frame of reference. Open-water swimmers raise their heads to look for a reference point, since disorientation might result in exhaustion or even drowning. In this paper, we report on Clairbuoyance - a system that provides feedback about the swimmer's orientation through lights mounted on swimming goggles. We conducted an experiment with two versions of Clairbuoyance: Discrete signals relative to a chosen direction, and continuous signals providing a sense of absolute direction. Participants swam to a series of targets. Proficient swimmers preferred the discrete mode; novice users the continuous one. We determined that both versions of Clairbuoyance enabled reaching the target faster than without the help of the system, although the discrete mode increased error. Based on the results, we contribute insights for designing directional guidance feedback for swimmers.

This paper presents an overview of what Big Data can bring to the modern industry.Through following the history of contemporary Big Data frameworks the authors observe that the tools available have reached sufficient maturity so as to be usable in an industrial setting.The authors propose the concept of a system for collecting, organising, processing and analysing experimental data obtained from measurements with process tomography.Process tomography is used for noninvasive flow monitoring and data acquisition.The measurement data is collected, stored and processed to identify process regimes and process threats.Further general examples of solutions that aim to take advantage of the existence of such tools are presented as proof of viability of such approach.As the first step in the process of creating the proposed system, a scalable, distributed, containerisation-based cluster has been constructed, with consumer-grade hardware.

This paper presents a new approach to analyzing measurement records from industrial processes. The proposed methodology is based on the model of contextual processing and uses big data from experimental process tomography datasets. Electrical capacitance tomography is used for monitoring noninvasive flow and for data acquisition. The measurement data are collected, stored, and processed to identify process regimes and process threats. A specific physical modification was introduced into the pneumatic conveying flow rig in order to study flow behavior under extreme conditions, extending the available knowledge base. A support vector machine was applied for data classification. This study illustrates how contextual processing can facilitate data interpretation and opens the way for the development of methods for detecting pre-emergency flow patterns.

This paper presents an approach towards aiding the rehabilitation exercises in amblyopia care using mixed reality technology.The Lazy Eye Syndrome is tackled here through an interactive holographic application implemented on Microsoft HoloLens device.It provides an entertaining way for the handicapped eye workout as it is based on a simple game of skill.The game is designed in a way that the majority of awarerequiring objects and events are displayed for the cured eye only, remaining the other eye responsible for background and additional information perception.Such disproportion forces an increased activity of the lazy eye, which is to perform more movements and impose the brain to process the sight more extensively.The proposed prototype is an extension of a novel approach towards treating amblyopia, employing software-based stimulation techniques, which could be easily adapted to various age and ability correlated needs of the user, with minimal requirements regarding the exercise setting and preparation.

This work covers deployment of contextual processing of measurement data in application to temporal modeling of pneumatic conveying industrial process.Electrical capacitance tomography (ECT) used as a non-invasive process monitoring tool is supported by data mining for regularization of nonlinear inverse problem solution.Processing of a larger number of archived experimental datasets enables extracting additional constraints for inference.Contextual data processing model (CDPM) extracts demanded information from the data in order to incorporate it as an expert knowledge about the process temporal behavior.Then it is incorporated into the Bayesian inference framework.Comparative analysis with previous work and domain expert prepared baseline to the proposed approach is demonstrated.Additionally, simplified parameterization is tested and verified by the quantitative experimental analysis.

This paper presents an outline of novel approaches and emerging trends related to Human-Computer Interaction (HCI). These trends have been present in the MIDI 2017 conference organized within FedCSIS series. In particular on-line services and mobile applications, often operated by voice-based interfaces and voice-driven assistants, are special areas of interest as to their prospective developments. Also the use of Artificial Intelligence (AI) in user interfaces seems to offer a breakthrough towards more intuitive and error-tolerant interaction needed especially in the most common, mobile context of use.

The paper describes application of the crowdsourcing system to pre-process X-ray tomography images.In this paper, we show the analysis of the crowdsourcing system applied to process tomography imaging to investigate granular flow with aid of tracking particles.Applying crowdsourcing approach coupled with a proper system interface design enhances the performance of the workers and elevates the attitude.We show here how analysis of the proposed systems in terms of user adoption and performance.Proposed interface features are designed based on previous work evaluation in order to reduce the cognitive and physical workload demands.

Proper break taking during office work iss necessary to prevent musculoskeletal disorders and reduce the risk of heart disease.We present APOEW -an avatar for preventing continuous office work without taking breaks.APOEW is a system that uses a personalized robot avatar to encourage proper break behaviour during office work.The avatar signals the need for a break by stooping.The system was designed to be unobtrusive and blend well with the office environment.The avatars are customisable in order to enable users to design their work environment freely.We conducted a user study where we observed developers working in front of their computers next to the avatar.Preliminary results indicate it has no negative impact on the work environment and users are intrigued by the system.Moreover, a survey on attitude to our concept reveals interesting and positive feedback that will help to develop an APOEW system further.

In this article, we present a novel application domain for human computation, specifically for crowdsourcing, which can help in understanding particle-tracking problems. Through an interdisciplinary inquiry, we built a crowdsourcing system designed to detect tracer particles in industrial tomographic images, and applied it to the problem of bulk solid flow in silos. As images from silo-sensing systems cannot be adequately analyzed using the currently available computational methods, human intelligence is required. However, limited availability of experts, as well as their high cost, motivates employing additional nonexperts. We report on the results of a study that assesses the task completion time and accuracy of employing nonexpert workers to process large datasets of images in order to generate data for bulk flow research. We prove the feasibility of this approach by comparing results from a user study with data generated from a computational algorithm. The study shows that the crowd is more scalable and more economical than an automatic solution. The system can help analyze and understand the physics of flow phenomena to better inform the future design of silos, and is generalized enough to be applicable to other domains.

This paper covers design, implementation and evaluation of a system that may be used to predict future stock prices basing on analysis of data from social media services.The authors took advantage of large datasets available from Twitter micro blogging platform and widely available stock market records.Data was collected during three months and processed for further analysis.Machine learning was employed to conduct sentiment classification of data coming from social networks in order to estimate future stock prices.Calculations were performed in distributed environment according to Map Reduce programming model.Evaluation and discussion of results of predictions for different time intervals and input datasets proved efficiency of chosen approach is discussed here.

A new method to solve the inverse problem of electrical capacitance tomography is proposed. Our method is based on artificial neural network to estimate the radius of an object present inside a pipeline. This information is useful to predict the distribution of material inside the pipe. The capacitance data used to train and test the neural network is simulated on Matlab using the electrical capacitance tomography toolkit ECTsim. The provided accuracy is promising and shows efficiency to solve the inverse problem in a simple manner and on reduced computational time about 120 times when compared to the existing Landweber iterative algorithm for tomographic image reconstruction that can be encouraging for dynamic industrial applications.

This paper describes the design of a system providing room occupancy information in a university campus. The system and its possible influence on the human environment are designed to enhance the work environment through pervasive information delivery. The work is motivated by recent advances in the domain of ubiquitous computing. The authors describe a case study of delivering a solution to university students informing them where they can find free spaces in rooms designated for quiet study. The data is available on the web and through ambient displays. The proof-of-concept study presents the methodology applied, discusses the design issues that were encountered by the project team and reflects on the devices and techniques used in the implementation. An application of standard image processing techniques to provide a contextualized, pervasive user-centered service is also depicted.

This papers shows attempt of using prior knowledge in order to learn about the industrial process system behaviour. The system being studied here is the pneumatic conveying of solids monitored with electrical capacitance tomography (ECT). The prior knowledge is the non-invasive measurement information derived on the basis of intentionally introduced structural change in the experimental process flow rig. The focus is on the examination of pseudo-emergency states of bulk flow. Real case pneumatic transport failure usually consists in pipeline blockage. Such blockage typically develops starting as material slug build up. In contrast, our experiments investigate artificially induced flow pre-blockage states monitored with ECT measurement system. Collected measurement data are contextually processed in order to detect the pipeline state in sense of flow regime and flow behaviour. ECT raw data analysis strives to uncover a relation between measured values and flow change tendency. This research is aimed at giving rise to conclusions about possibility of contextual data processing in order to properly adjust pneumatic conveying control and a possible role of ECT records in this aspect. Authors show here that for now it is possible to detect abnormal process state such as blockage signal and the presented methodology coupled with nowadays computational techniques are anticipated to lead to abnormal states forecasting such as blockage threat recognition.

Advanced statistical modelling such as Bayesian framework is a powerful methodology and gives great flexibility in terms of physical phenomena modelling. Unfortunately it is usually associated with very time and resource consuming computing. Therefore it was avoided by engineers in the past. Nowadays, rapid development of computer capabilities enables use of such methods. Algorithms reported here are based on Markov chain Monte Carlo (MCMC) methods applied to Bayesian modelling. The important factor is highly iterative approach enabling direct desired parameters estimation, hence omitting the phase of image reconstruction. This property has an important feature of making feasible implementation of automatic industrial process control systems based on process tomography.