Virtual reality has been effectively used for eliciting emotions, yet most research focuses on the intensity of affective responses rather than on how interaction influences those experiences. To address this gap, we advance a validated VR emotion-elicitation dataset through two key extensions. First, we add a new high-arousal, high-valence scene and validate its effectiveness in a within-subject study (N=24). Second, we incorporate interactive elements into each scene, creating both interactive and non-interactive versions to examine the impact of interaction on emotional responses. We evaluate interaction through a multimodal approach combining subjective ratings and physiological signals to capture both conscious and unconscious affective responses. Our evaluation study (N=84) shows that interaction not only amplifies emotions but modulates them in context, supporting coping in negative scenes and enhancing enjoyment in positive scenes. These findings highlight the potential of scene-tailored interaction for different applications, where regulating emotions is as important as eliciting them.

Eye strain presents a significant challenge in human-information interaction in virtual reality (VR), as prolonged exposure contributes to various eye problems. This study introduces a new gaze redirection method called EyeXRciser, which passively activates eye movements to help prevent eye muscle stiffness during VR reading. This method achieves gaze redirection by slowly shifting the relative position of the text window within the user’s field of view through a head-bound coordinate system. We implemented our method with two different redirection speeds (i.e., unnoticeable speed at 0.03 rad/s; noticeable speed at 0.12 rad/s) and conducted a user study (N = 24) comparing with a baseline using a fixed text window. Results show that both our methods successfully minimized the decline in accommodative ability caused by prolonged reading, without negatively impacting reading comprehension. Results also show that the unnoticeable redirection speed produced less subjective discomfort, eye fatigue, and reading distraction than the noticeable speed.

Regulatory limits on explicit targeting have not eliminated algorithmic profiling on the Web, as optimisation systems still adapt ad delivery to users' private attributes. The widespread availability of powerful zero-shot multimodal Large Language Models (LLMs) has dramatically lowered the barrier for exploiting these latent signals for adversarial inference. We investigate this emerging societal risk, specifically how adversaries can now exploit these signals to reverse-engineer private attributes from ad exposure alone. We introduce a novel pipeline that leverages LLMs as adversarial inference engines to perform natural language profiling. Applying this method to a longitudinal dataset comprising over 435,000 Facebook ad impressions collected from 891 users, we conducted a large-scale study to assess the feasibility and precision of inferring private attributes from passive online ad observations. Our results demonstrate that off-the-shelf LLMs can accurately reconstruct complex user private attributes, including party preference, employment status, and education level, consistently outperforming strong census-based priors and matching or exceeding human social perception at only a fraction of the cost (223× lower) and time (52× faster) required by humans. Critically, actionable profiling is feasible even within short observation windows, indicating that prolonged tracking is not a prerequisite for a successful attack. These findings provide the first empirical evidence that ad streams serve as a high-fidelity digital footprint, enabling off-platform profiling that inherently bypasses current platform safeguards, highlighting a systemic vulnerability in the ad ecosystem and the urgent need for responsible web AI governance in the generative AI era. The code is available at https://github.com/Breezelled/when-ads-become-profiles.

Individuals tend to apply preferences and beliefs as heuristics to effectively sift through the sheer amount of information available online. Such tendencies, however, often result in cognitive biases, which can skew judgment and open doors for manipulation. In this work, we investigate how individual and contextual factors lead to instances of confirmation bias when seeking, evaluating, and recalling polarising information. We conducted a lab study, in which we exposed participants to opinions on controversial issues through a Twitter-like news feed. We found that low-effortful thinking, strong political beliefs, and content conveying a strong issue amplify the occurrences of confirmation bias, leading to skewed information processing and recall. We discuss how the adverse effects of confirmation bias can be mitigated by taking bias-susceptibility into account. Specifically, social media platforms could aim to reduce strong expressions and integrate media literacy-building mechanisms, as low-effortful thinking styles and strong political beliefs render individuals especially susceptible to cognitive biases.

Computing systems are increasingly designed to adapt to users’ cognitive states and mental models. Yet, cognitive biases affect how humans form such models and, therefore, they can impact their interactions with computers. To better understand this interplay, we conducted a scoping review to chart how Human-Computer Interaction (HCI) researchers study cognitive biases. Our findings show that computing systems not only have the potential to induce and amplify cognitive biases but also can be designed to steer users’ behaviour and decision-making by capitalising on biases. We describe how HCI researchers develop algorithms and sensing methods to detect and quantify the effects of cognitive biases and discuss how we can use their understanding to inform system design. In this paper, we outline a research agenda for more theory-grounded research and highlight ethical issues when researching and designing computing systems with cognitive biases in mind as they affect real-world behaviour.

Mid-air gestures serve as a common interaction modality across Extended Reality (XR) applications, enhancing engagement and ownership through intuitive body movements. However, prolonged arm movements induce shoulder fatigue—known as "Gorilla Arm Syndrome"—degrading user experience and reducing interaction duration. Although existing ergonomic techniques derived from Fitts’ law (such as reducing target distance, increasing target width, and modifying control-display gain) provide some fatigue mitigation, their implementation in XR applications remains challenging due to the complex balance between user engagement and physical exertion. We present AlphaPIG, a meta-technique designed to Prolong Interactive Gestures by leveraging real-time fatigue predictions. AlphaPIG assists designers in extending and improving XR interactions by enabling automated fatigue-based interventions. Through adjustment of intervention timing and intensity decay rate, designers can explore and control the trade-off between fatigue reduction and potential effects such as decreased body ownership. We validated AlphaPIG’s effectiveness through a study (N=22) implementing the widely-used Go-Go technique. Results demonstrated that AlphaPIG significantly reduces shoulder fatigue compared to non-adaptive Go-Go, while maintaining comparable perceived body ownership and agency. Based on these findings, we discuss positive and negative perceptions of the intervention. By integrating real-time fatigue prediction with adaptive intervention mechanisms, AlphaPIG constitutes a critical first step towards creating fatigue-aware applications in XR.

Wearable eye-tracking in field studies presents challenges in synchronising gaze data with dynamic stimuli and integrating observational notes from multiple observers. Existing tools often struggle to visualise eye-tracking patterns in complex, real-world environments with frequently changing areas of interest (AOIs). To address this, we propose a streamlined workflow that simplifies analysis preparation by integrating real-time observer notes with eye-tracking data with enhanced timestamp-based synchronisation, improving data mapping, and automating AOI detection with an energy control room use case. This workflow makes eye-tracking tools like Gazealytics more practical for complex field studies. By streamlining data preparation and automation, our method enhances the scalability and usability of eye-tracking analysis in complex environments, enabling more efficient and accurate visual analysis of real-world decision-making.

Mental Workload (MWL) is a construct widely used in HCI to assess the cognitive demand users must exert to perform a task. Research in human factors, however, has suggested several issues regarding its definitions, scales, and applications. This paper, first, introduces debates surrounding the MWL concept and its most popular measure, the NASA-TLX. We present a systematic review of CHI papers involving MWL and highlight severe issues in its application. Finally, through a validation experiment, we assess the convergent validity and sensitivity of two MWL instruments—NASA-TLX and MRQ. Our findings reveal disagreements in the definitions of MWL and severe drawbacks in NASA-TLX and its applications. Our validation study also presents evidence for a lack of convergent validity and sensitivity of MWL subjective scales in HCI tasks. Our findings recommend caution when employing NASA-TLX in user studies and highlight the need for an MWL definition that is agreed upon within the HCI community.

Abstract Despite the widely acknowledged importance of well-being, our well-being can regularly be under pressure from external sources. Work is often attributed as a source of stress and dissatisfaction, so, unsurprisingly, extensive efforts are made to measure and improve our well-being in this context. This paper examines opportunities to better design supportive digital solutions through two complementary studies. In the first study, we present a longitudinal assessment of a well-being-focused self-report application deployed in two organizations. Through an analysis of one year of application usage across 219 users, we find both established and novel patterns of application usage and well-being evaluation. While prior work has highlighted substantial dropout rates and daily well-being fluctuations that peak in the morning and early evening, our results highlight that substantial breaks in usage are common, suggesting that users choose to engage with well-being applications mainly when they need them. In the second study, we expand on the topic of well-being reflection at work and the use of technology for this purpose. Through a survey involving 100 participants, we identify current practices in increasing well-being at work, obstacles to sharing and discussing mental well-being states, opportunities for digital well-being solutions and reflections on transparency and communication. Our combined results highlight opportunities for HCI research and practice to address the ongoing challenges of maintaining well-being in today’s work environments.

Cycling has gained popularity due to growing interest in healthy and sustainable lifestyles. Simultaneously, Augmented Reality (AR) Head-Mounted Displays (HMDs) can assist cyclists by presenting notifications within their field of view without diverting their attention to external devices. While previous studies have investigated these advantages, safety concerns have primarily limited them to lab settings, creating a notable gap in understanding their real-world feasibility. We conducted a user study with 20 participants on a shared-use outdoor path and explored the impact of text-based HMD notification placement (top, right, bottom), on attentional tunneling and cyclists' experiences. Our results suggested that while the bottom placement received higher scores for perceived safety and noticeability, HMD notifications induced attentional tunneling, regardless of placement. We discuss our findings and present design insights for future HMD systems aimed at enhancing cyclists' safety and experience.

In AI-assisted decision-making, explanations aim to enhance transparency and user trust but can also lead to negligence. In two separate studies, we explore the use of partial explanations to activate cognitive forcing and increase user engagement. In Study I (N = 264), we present participants with weighted graphs and ask them to identify the shortest paths. In Study II (N = 210), participants correct spelling and grammar mistakes in short text segments. In both studies, we provide a solution suggestion accompanied by either no explanation, a full explanation, or a partial explanation. Our results show that partial explanations reduce overreliance on incorrect AI suggestions, performing significantly better than the baseline but not as well as full explanations. Individuals with a high need for cognition benefit more from AI explanations and consequently perform better. Our work suggests that partial explanations can be valuable in domains where reducing overreliance on AI is critical, like medical diagnosis. It also underscores the need to consider explanation effectiveness across different task difficulties, a factor often overlooked in contemporary human-AI studies.

Implantable Medical Devices (IMDs) are widely deployed today and often use wireless communication. Establishing a secure communication channel to these devices is challenging in practice. To address this issue, researchers have proposed IMD key exchange protocols, particularly ones that leverage an Out-Of-Band (OOB) channel such as audio, vibration and physiological signals. While these solutions have advantages over traditional key exchange, they are often proposed in an ad-hoc manner and lack a systematic evaluation of their security, usability and deployability properties. In this paper, we provide an in-depth analysis of existing OOB-based solutions for IMDs and, based on our findings, propose a novel IMD key exchange protocol that includes a new class of OOB channel based on human bodily motions. We implement prototypes and validate our designs through a user study (N = 24). The results demonstrate the feasibility of our approach and its unique features, establishing a new direction in the context of IMD security.

This paper presents an analysis of different interaction techniques used in interactive data visualisations to support end-users in visual analytics tasks. Our selection of interaction techniques is based on prior work and consists of the interaction techniques SELECT, EXPLORE, RECONFIGURE, ENCODE, FILTER, ABSTRACT/ELABORATE, and CONNECT. Through a within-subject study, we assessed participants’ abilities to utilise these techniques when faced with three distinct types of data-driven tasks; lookup, comparison, and Relation-seeking. Our research investigates the impact of these interaction techniques on the correctness, confidence, perceived difficulty, and cognitive load of N = 80 self-identified data scientists and N = 80 non-experts. We find that interaction technique significantly impacts answer correctness and participant confidence. Participants performed best across those interaction techniques that allow for information that is deemed least relevant to be concealed, which is reflected in lower intrinsic and extraneous cognitive load. Interestingly, participants’ expertise affected their confidence but not their accuracy. Our results provide insights useful for a more targeted and informed design and usage of interactive data visualisations.

Natural gestures are crucial for mid-air interaction, but predicting and managing muscle fatigue is challenging. Existing torque-based models are limited in their ability to model above-shoulder interactions and to account for fatigue recovery. We introduce a new hybrid model, NICER , which combines a torque-based approach with a new term derived from the empirical measurement of muscle contraction and a recovery factor to account for decreasing fatigue during rest. We evaluated NICER in a mid-air selection task using two interaction methods with different degrees of perceived fatigue. Results show that NICER can accurately model above-shoulder interactions as well as reflect fatigue recovery during rest periods. Moreover, both interaction methods show a stronger correlation with subjective fatigue measurement ( ρ = 0.978/0.976) than a previous model, Cumulative Fatigue ( ρ = 0.966/0.923), confirming that NICER is a powerful analytical tool to predict fatigue across a variety of gesture-based interactive applications.

Images and videos are widely used to elicit emotions; however, their visual appeal differs from real-world experiences. With virtual reality becoming more realistic, immersive, and interactive, we envision virtual environments to elicit emotions effectively, rapidly, and with high ecological validity. This work presents the first interactive virtual reality dataset to elicit emotions. We created five interactive virtual environments based on corresponding validated 360° videos and validated their effectiveness with 160 participants. Our results show that our virtual environments successfully elicit targeted emotions. Compared with the existing methods using images or videos, our dataset allows virtual reality researchers and practitioners to integrate their designs effectively with emotion elicitation settings in an immersive and interactive way.

Cognitive biases have been shown to play a critical role in creating echo chambers and spreading misinformation. They undermine our ability to evaluate information and can influence our behaviour without our awareness. To allow the study of occurrences and effects of biases on information consumption behaviour, we explore indicators for cognitive biases in physiological and interaction data. Therefore, we conducted two experiments investigating how people experience statements that are congruent or divergent from their own ideological stance. We collected interaction data, eye tracking data, hemodynamic responses, and electrodermal activity while participants were exposed to ideologically tainted statements. Our results indicate that people spend more time processing statements that are incongruent with their own opinion. We detected differences in blood oxygenation levels between congruent and divergent opinions, a first step towards building systems to detect and quantify cognitive biases.

Virtual Reality (VR) is increasingly being adopted in fitness, gaming, and workplace productivity applications for its natural interaction with body movement. A widely accepted method for quantifying the physical fatigue caused by VR interactions is through metrics such as Consumed Endurance (CE). Proposed in 2014, CE calculates the shoulder torque to infer endurance time (ET)—i.e. the maximum amount of time a pose can be maintained—during mid-air interactions. This model remains widely cited but has not been closely examined beyond its initial evaluation, leaving untested assumptions about exertion from low-intensity interactions and its basis on torque. In this paper, we present two VR studies where we (1) collect a baseline dataset that replicates the foundation of CE and (2) extend the initial evaluation in a pointing task from a two-dimensional (2D) screen to a three-dimensional (3D) immersive environment. Our baseline dataset collected from a high-precision tracking system found that the CE model overestimates ET for low-exertion interactions. Further, our studies reveal that a biomechanical model based on only torque cannot account for additional exertion measured when the shoulder angle exceeds 90° elevation. Based on these findings, we propose a revised formulation of CE to highlight the need for a hybrid approach in future fatigue modelling.

The rapid development and ubiquity of mobile and wearable devices promises to enable researchers to monitor users’ granular emotional data in a less intrusive manner. Researchers have used a wide variety of mobile and wearable devices for this purpose, and have proposed various approaches to sense users’ emotional states. In this survey, we utilise three established digital libraries (ACM Digital Library,IEEE Xplore Digital Library, andSpringer Nature). We analysed and critically assessed the different approaches used in the three stages (perception, learning, inference) of a typical mobile emotion sensing framework, following a structured paper selection process. The contribution of this survey is three-fold; first, we document all the latest relevant literature on mobile emotion sensing research; second, we describe how mobile and wearable devices use their sensing and computing capabilities to monitor human emotions; third, we discuss challenges and opportunities of mobile emotion sensing to demonstrate the potential of this thriving field of research.

With the rapid development of mobile and wearable devices, it is increasingly possible to access users’ affective data in a more unobtrusive manner. On this basis, researchers have proposed various systems to recognize user’s emotional states. However, most of these studies rely on traditional machine learning techniques and a limited number of signals, leading to systems that either do not generalize well or would frequently lack sufficient information for emotion detection in realistic scenarios. In this paper, we propose a novel attention-based LSTM system that uses a combination of sensors from a smartphone (front camera, microphone, touch panel) and a wristband (photoplethysmography, electrodermal activity, and infrared thermopile sensor) to accurately determine user’s emotional states. We evaluated the proposed system by conducting a user study with 45 participants. Using collected behavioral (facial expression, speech, keystroke) and physiological (blood volume, electrodermal activity, skin temperature) affective responses induced by visual stimuli, our system was able to achieve an average accuracy of 89.2 percent for binary positive and negative emotion classification under leave-one-participant-out cross-validation. Furthermore, we investigated the effectiveness of different combinations of data signals to cover different scenarios of signal availability.

Implicit tendencies and cognitive biases play an important role in how information is perceived and processed, a fact that can be both utilised and exploited by computing systems. The Implicit Association Test (IAT) has been widely used to assess people’s associations of target concepts with qualitative attributes, such as the likelihood of being hired or convicted depending on race, gender, or age. The condensed version–the Brief IAT–aims to implicit biases by measuring the reaction time to concept classifications. To use this measure in HCI research, however, we need a way to construct and validate target concepts, which tend to quickly evolve and depend on geographical and cultural interpretations. In this paper, we introduce and evaluate a new method to appropriate the BIAT using crowdsourcing to measure people’s leanings on polarising topics. We present a web-based tool to test participants’ bias on custom themes, where self-assessments often fail. We validated our approach with 14 domain experts and assessed the fit of crowdsourced test construction. Our method allows researchers of different domains to create and validate bias tests that can be geographically tailored and updated over time. We discuss how our method can be applied to surface implicit user biases and run studies where cognitive biases may impede reliable results.

Two decades of focus on User Experience has yielded an array of digital technologies that help people experience, understand and share emotions. Although the effects of specific technologies upon emotion have been well studied, less is known about how people actively appropriate and combine the full range of devices, apps and services at their disposal to deliberately manage emotions in everyday life. We conducted a one-week diary study in which 23 adults recorded interactions between their emotions and technology use. They reported using a diverse range of emotion-shaping tools and strategies as part of coping with daily challenges, managing routines, and pursuing work and social goals. We analyse these data in the light of psychological theories of emotion. Our findings point to the significance of digital emotion regulation as a powerful perspective to inform wider debates about the impacts of technology on social and emotional well-being.

Voice user interfaces (VUIs) have made their way into people’s daily lives, from voice assistants to smart speakers. Although VUIs typically just react to direct user commands, increasingly, they incorporate elements of proactive behaviors. In particular, proactive smart speakers have the potential for many applications, ranging from healthcare to entertainment; however, their usability in everyday life is subject to interaction errors. To systematically investigate the nature of errors, we designed a voice-based Experience Sampling Method (ESM) application to run on proactive speakers. We captured 1,213 user interactions in a 3-week field deployment in 13 participants’ homes. Through auxiliary audio recordings and logs, we identify substantial interaction errors and strategies that users apply to overcome those errors. We further analyze the interaction timings and provide insights into the time cost of errors. We find that, even for answering simple ESMs, interaction errors occur frequently and can hamper the usability of proactive speakers and user experience. Our work also identifies multiple facets of VUIs that can be improved in terms of the timing of speech.

Recognising and monitoring emotional states play a crucial role in mental health and well-being management. Importantly, with the widespread adoption of smart mobile and wearable devices, it has become easier to collect long-term and granular emotion-related physiological data passively, continuously, and remotely. This creates new opportunities to help individuals manage their emotions and well-being in a less intrusive manner using off-the-shelf low-cost devices. Pervasive emotion recognition based on physiological signals is, however, still challenging due to the difficulty to efficiently extract high-order correlations between physiological signals and users' emotional states. In this paper, we propose a novel end-to-end emotion recognition system based on a convolution-augmented transformer architecture. Specifically, it can recognise users' emotions on the dimensions of arousal and valence by learning both the global and local fine-grained associations and dependencies within and across multimodal physiological data (including blood volume pulse, electrodermal activity, heart rate, and skin temperature). We extensively evaluated the performance of our model using the K-EmoCon dataset, which is acquired in naturalistic conversations using off-the-shelf devices and contains spontaneous emotion data. Our results demonstrate that our approach outperforms the baselines and achieves state-of-the-art or competitive performance. We also demonstrate the effectiveness and generalizability of our system on another affective dataset which used affect inducement and commercial physiological sensors.

The emergence of big data combined with the technical developments in Artificial Intelligence has enabled novel opportunities for autonomous and continuous decision support. While initial work has begun to explore how human morality can inform the decision making of future Artificial Intelligence applications, these approaches typically consider human morals as static and immutable. In this work, we present an initial exploration of the effect of context on human morality from a Utilitarian perspective. Through an online narrative transportation study, in which participants are primed with either a positive story, a negative story or a control condition (N = 82), we collect participants' perceptions on technology that has to deal with moral judgment in changing contexts. Based on an in-depth qualitative analysis of participant responses, we contrast participant perceptions to related work on Fairness, Accountability and Transparency. Our work highlights the importance of contextual morality for Artificial Intelligence and identifies opportunities for future work through a FACT-based (Fairness, Accountability, Context and Transparency) perspective.

Electrodermal activity data is widely used in HCI to capture rich and unbiased signals. Results from related fields, however, have suggested several methodological issues that can arise when practices do not follow established standards. In this paper, we present a systematic methodological review of CHI papers involving the use of EDA data according to best practices from the field of psychophysiology, where standards are well-established and mature. We found severe issues in our sample at all stages of the research process. To ensure the validity of future research, we highlight pitfalls and offer directions for how to improve community standards.

Selection studies are prevalent and indispensable for VR research. However, due to the tedious and repetitive nature of many such experiments, participants can become disengaged during the study, which is likely to impact the results and conclusions. In this work, we investigate participant disengagement in VR selection experiments and how this issue affects the outcomes. Moreover, we evaluate the usefulness of four engagement strategies to keep participants engaged during VR selection studies and investigate how they impact user performance when compared to a baseline condition with no engagement strategy. Based on our findings, we distill several design recommendations that can be useful for future VR selection studies or user tests in other domains that employ similar repetitive features.

As the day progresses, cognitive functions are subject to fluctuations. While the circadian process results in diurnal peaks and drops, the homeostatic process manifests itself in a steady decline of alertness across the day. Awareness of these changes allows the design of proactive recommender and warning systems, which encourage demanding tasks during periods of high alertness and flag accident-prone activities in low alertness states. In contrast to conventional alertness assessments, which are often limited to lab conditions, bulky hardware, or interruptive self-assessments, we base our approach on eye blink frequency data known to directly relate to fatigue levels. Using electrooculography sensors integrated into regular glasses' frames, we recorded the eye movements of 16 participants over the course of two weeks in-the-wild and built a robust model of diurnal alertness changes. Our proposed method allows for unobtrusive and continuous monitoring of alertness levels throughout the day.

We tend to synchronize our movements to the person we are talking to during face-to-face conversation. Higher interpersonal synchrony is linked to greater empathy and more effortless interactions. This paper presents a first method and a corresponding dataset to explore synchrony in natural conversation by capturing eye and head movement using commodity smart eyewear. We present a 17 hour dataset, using Electrooculography and inertial sensing, of 42 people in conversation (21 dyads: 10 in Japanese, 10 in English, 1 in Chinese). Initial results on 18 dyads show significant interpersonal synchrony of blink and head nod behaviour during conversation (at frequencies of 0.2 to 0.5 Hz). We also find that people are more likely to synchronise blinks at around 1 Hz when conversing back-to-back than when face-to-face.

In this work we present the smart eyewear demo setup consisting of the software platform for cognitive and social interaction assessments in the wild, with several application cases and a demonstration of activity recognition in real-time. The platform is designed to work with Jins MEME, smart EOG enabled glasses, The user software is capable data logging, posture tracking and recognition of several activities, such as talking, reading and blinking. In this work we present several applications and studies that the platform has been used for.

This paper presents a new approach to implement wearable haptic devices using Shape Memory Alloy (SMA) wires. The proposed concept allows building silent, soft, flexible and lightweight wearable devices, capable of producing the sense of pressure on the skin without any bulky mechanical actuators. We explore possible design considerations and applications for such devices, present user studies proving the feasibility of delivering meaningful information and use nonlinear autoregressive neural networks to compensate for SMA inherent drawbacks, such as delayed onset, enabling us to characterize and predict the physical behavior of the device.

To help deal with daily reading volumes, we present Reading Scheduler, a smartphone application linked to people's reading list, which triggers reading reminders throughout the day. The app suggests articles according to their length, complexity, and the time available for reading as indicated by the user. In a field study, we collected usage data from ten participants over the course of two weeks. During this time, we recorded mobile sensor data and trained a classifier to detect opportune moments for reading. Participants read 182 articles while we collected 787,752 sensor data points. Together with an assessment of the feasibility of proactive reading suggestions, we present a prediction model with close to 73% accuracy, that can be used to build mobile recommender systems for utilizing idle moments for reading throughout the user's day.

We are presenting a Virtual Reality training system for Karate kata based on motion capture and Virtual Reality technologies. The system is built as a game, in which the player needs to learn and repeat different kata to progress and reach the next level. Different levels are represented by obi (belts) of different color, corresponding real Karate obi. We capture players' motion with a Kinect camera and enable interaction with game objects. A database is integrated in the game so that different players can use, save and track their training progress.

In this paper we are proposing a new system for the production of VR stories that allow users seamless interaction with the content. Our system plays real life footage rather than animation allowing for interactive live-action experiences. The unaware empowered users take subliminal decisions by focusing their attention at predefined ROIs. The moment a decision is taken is always placed seconds before the actual video branching happens to allow the system to preload only the chosen storyline in order to keep the computational workload as low as possible. We believe that this unique system will not only change the way we experience VR contents, but will rather lead to a paradigm shift in film production.

Pervasive computing allows immediate image and sound capturing, interaction with content, participation in content productions, and thus virtually anyone can become a multimedia content producer on the spot. With our research focus on collaborative content production, we designed a platform called "Poligatari" which aims at bringing professional content producers and amateur filmmakers together.