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Human-computer interaction (HCI) with screens through gestures is a pivotal method amidst the digitalization trend. In this work, a gesture recognition method is proposed that combines multi-band spectral features with spatial characteristics of screen-reflected light. Based on the method, a red-green-blue (RGB) three-channel spectral gesture recognition system has been developed, composed of a display screen integrated with narrowband spectral receivers as the hardware setup. During system operation, emitted light from the screen is reflected by gestures and received by the narrowband spectral receivers. These receivers at various locations are tasked with capturing multiple narrowband spectra and converting them into light-intensity series. The availability of multi-narrowband spectral data integrates multidimensional features from frequency and spatial domains, enhancing classification capabilities. Based on the RGB three-channel spectral features, this work formulates an RGB multi-channel convolutional neural network long short-term memory (CNN-LSTM) gesture recognition model. It achieves accuracies of 99.93% in darkness and 99.89% in illuminated conditions. This indicates the system's capability for stable operation across different lighting conditions and accurate interaction. The intelligent gesture recognition method can be widely applied for interactive purposes on various screens such as computers and mobile phones, facilitating more convenient and precise HCI.

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Objective: Data sharing promotes the scientific progress. However, not all data can be shared freely due to privacy issues. This work is intended to foster FAIR sharing of sensitive data exemplary in the biomedical domain, via an integrated computational approach for utilizing and enriching individual datasets by scientists without coding experience. Methods: We present an in silico pipeline for openly sharing controlled materials by generating synthetic data. Additionally, it addresses the issue of inexperience to computational methods in a non-IT-affine domain by making use of a cyberinfrastructure that runs and enables sharing of computational notebooks without the need of local software installation. The use of a digital twin based on cancer datasets serves as exemplary use case for making biomedical data openly available. Quantitative and qualitative validation of model output as well as a study on user experience are conducted. Results: The metadata approach describes generalizable descriptors for computational models, and outlines how to profit from existing data resources for validating computational models. The use of a virtual lab book cooperatively developed using a cloud-based data management and analysis system functions as showcase enabling easy interaction between users. Qualitative testing revealed a necessity for comprehensive guidelines furthering acceptance by various users. Conclusion: The introduced framework presents an integrated approach for data generation and interpolating incomplete data, promoting Open Science through reproducibility of results and methods. The system can be expanded from the biomedical to any other domain while future studies integrating an enhanced graphical user interface could increase interdisciplinary applicability.

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Researchers are studying makerspaces as one way to support older adults in learning about and using new technologies and tools. In this paper, through a long-term (34 months), ethnographic approach, we study the ways that older adults arranged sociotechnical resources to sustain the community use of a makerspace. Our analysis identifies three interconnected resources that were developed: an adaptive staffing approach that could withstand constant personnel shifts and shortages; structured activities to draw interest and overcome challenges associated with learning to use the machines; and reference materials to support individuals in independent usage of the space. We describe the issues that arose as time went on with each of these resource types, and how individuals affiliated with the makerspace adapted the resources to address these issues. In the discussion, we extend best practices by reflecting on strategies that worked well in the makerspace, such as drawing interest through introductory classes, as well as different purposes for reference materials to support technology use.

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INTRODUCTION: Parkinson's disease represents a burdensome condition with complex manifestations. A licensed, standardized paper-based questionnaire is completed by both patients and physicians to monitor the progression and state of the disease. However, integrating the obtained scores into digital systems still poses a challenge. METHODS: Paper-based handwriting is intuitive and an efficient mode of human-computer interaction. Accordingly, we transformed a consumer-grade tablet into a device where an exact digital copy of the disease-specific questionnaire can be filled with the supplied pen. Utilizing a small convolutional neural network directly on the device and trained on MNIST data, we translated the handwritten digits to appropriate LOINC codes and made them accessible through a FHIR-compatible HTTP interface. RESULTS: When evaluating the usability from a patient-centric point of view, the System Usability Score revealed an excellent rating (SUS = 83.01) from the participants. However, we identified some challenges associated with the magnetic pen and the flat design of the device. CONCLUSION: In setups where certified medical devices are not required, consumer hardware can be used to map handwritten digits of patients to appropriate medical standards without manual intervention through healthcare professionals.

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This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants' subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agent's intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

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In today's digital age, ensuring high-quality healthcare services is paramount for both physical and mental well-being. This is particularly challenging in developing countries like Bangladesh, where marginalized individuals, especially women, face barriers to accessing essential healthcare. Financial constraints and limited awareness often hinder their ability to avail themselves of private or public healthcare services. Besides, women have distinct healthcare difficulties compared to men, and they are also more likely to catch certain illnesses and diseases. However, there is a noticeable gap in the existing literature, with insufficient research specifically dedicated to addressing the healthcare challenges faced by this underprivileged group of women in developing countries. In this study, we delve deeper into the healthcare challenges faced by underprivileged women in Bangladesh, including issues related to menstruation and gynecological disorders through a field study in a slum area. Drawing valuable insights from this field study, we propose leveraging voice bots in the telecommunications sector as an innovative solution to provide accessible and targeted healthcare support for marginalized women. We present the prototype of such a voice bot to be available 24/7 with detailed workflow and performance evaluation. We expect that our study will contribute to the ICTD scholarship by informing future interventions for economically disadvantaged communities as well as informing policymakers grappling with the healthcare challenges faced by underprivileged women.

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This study evaluated the effects of target sizes on biomechanical and cognitive load and the performance of virtual reality (VR) interactions. In a repeated-measures laboratory study, each of the twenty participants performed standardised VR tasks with three different target sizes: small, medium, and large. During the VR tasks, biomechanical load in the neck and shoulders (joint angles, joint moments, and muscle activity), cognitive load (perceived workload and cognitive stress), and task performance (completion time) were collected. The neck and shoulder joint angles, joint moments, and muscle activities were greater with the large targets compared to the medium and small targets. Moreover, the larger VR targets caused greater temporal demand and longer task completion time compared to the other target sizes. These findings indicate that target sizes in VR interfaces play important roles in biomechanical and cognitive load as well as task performance.

This study investigated the effects of target sizes on biomechanical and cognitive load and the performance of virtual reality (VR) interactions. The results showed that the VR target size is an important design factor affecting neck and shoulder joint angles, moments, muscle activity, temporal demand, and performance measures.

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Reducing inactivity in patients with chronic disease is vital since it can decrease the risk of disease progression and mortality. Exergames are an innovative approach to becoming more physically active and positively affecting physical health outcomes. Serious games are designed for purposes beyond entertainment and exergames are serious games for physical activity. However, current commercial exergames might not optimally meet the needs of patients with special needs. Developing tailored exergames is challenging and requires an appropriate process. The primary goal of this viewpoint is to describe significant lessons learned from designing and developing an exergame for patients with chronic heart failure using the player-centered, iterative, interdisciplinary, and integrated (P-III) framework for serious games. Four of the framework's pillars were used in the design and development of a mobile exergame: player-centered design, iterative development of the game, interdisciplinary teamwork, and integration of play and serious content. The mobile exergame was developed iteratively in 7 iterations by an interdisciplinary team involving users and stakeholders in all iterations. Stakeholders played various roles during the development process, making the team stay focused on the needs of the patients and creating an exergame that catered to these needs. Evaluations were conducted during each iteration by both the team and users or patients according to the player-centered design pillar. Since the exergame was created for a smartphone, the assessments were conducted both on the development computer and on the intended platforms. This required continuous deployment of the exergame to the platforms and smartphones that support augmented reality. Our findings show that the serious game P-III framework needs to be modified in order to be used for the design and development of exergames. In this viewpoint, we propose an updated version of the P-III framework for exergame development including (1) a separate and thorough design of the physical activity and physical interaction, and (2) early and continuous deployment of the exergame on the intended platform to enable evaluations and everyday life testing.

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In recent years, artificial intelligence (AI) has gained momentum in many fields of daily live. In healthcare, AI can be used for diagnosing or predicting illnesses. However, explainable AI (XAI) is needed to ensure that users understand how the algorithm arrives at a decision. In our research project, machine learning methods are used for individual risk prediction of hospital-onset bacteremia (HOB). This paper presents a vision on a step-wise process for implementation and evaluation of user-centered XAI for risk prediction of HOB. An initial requirement analysis revealed first insights on the users' needs of explainability to use and trust such risk prediction applications. The findings were then used to propose step-wise process towards a user-centered evaluation.

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BACKGROUND: Nursing students often face anxiety and cognitive overload, leading to high attrition rates; meanwhile, traditional pastoral interventions have proved insufficient. A novel, digitally enhanced learning environment named The Wellbeing in Student Education (WISE) Room, was introduced as an alternative at a UK university. AIMS: This study investigated the impact of The WISE Room, a 4m × 4m digital platform containing touch-interactive walls, co-created with students and academic staff, designed to promote insight through immersive experiences beyond traditional learning settings. METHODS: A mixed-methods approach was adopted, collecting data from nursing students, healthcare staff and academic faculty. Data were collected at three stages, each corresponding with the iterative development of The WISE Room, including an evaluation of the intervention. RESULTS: Feedback from 240 participants was positive. In Phase 2 of the study, 93% of the 79 students and 21 staff who visited the room and completed a survey were satisfied with the room's suitability in three key areas: emotional wellbeing, academic readiness, and placement preparedness. CONCLUSION: The WISE Room has proved effective in supporting nursing students' wellbeing, and educational and placement preparation requirements. Its multisensory, digital and experiential nature is beneficial for students throughout their learner journey. Additional trials and content development will be required to enhance the learning and teaching experience further.

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Background: The exponential growth in computing power and the increasing digitization of information have substantially advanced the machine learning (ML) research field. However, ML algorithms are often considered "black boxes," and this fosters distrust. In medical domains, in which mistakes can result in fatal outcomes, practitioners may be especially reluctant to trust ML algorithms. Objective: The aim of this study is to explore the effect of user-interface design features on intensivists' trust in an ML-based clinical decision support system. Methods: A total of 47 physicians from critical care specialties were presented with 3 patient cases of bacteremia in the setting of an ML-based simulation system. Three conditions of the simulation were tested according to combinations of information relevancy and interactivity. Participants' trust in the system was assessed by their agreement with the system's prediction and a postexperiment questionnaire. Linear regression models were applied to measure the effects. Results: Participants' agreement with the system's prediction did not differ according to the experimental conditions. However, in the postexperiment questionnaire, higher information relevancy ratings and interactivity ratings were associated with higher perceived trust in the system (P<.001 for both). The explicit visual presentation of the features of the ML algorithm on the user interface resulted in lower trust among the participants (P=.05). Conclusions: Information relevancy and interactivity features should be considered in the design of the user interface of ML-based clinical decision support systems to enhance intensivists' trust. This study sheds light on the connection between information relevancy, interactivity, and trust in human-ML interaction, specifically in the intensive care unit environment.

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PURPOSE: The operating microscope plays a central role in middle and inner ear procedures that involve working within tightly confined spaces under limited exposure. Augmented reality (AR) may improve surgical guidance by combining preoperative computed tomography (CT) imaging that can provide precise anatomical information, with intraoperative microscope video feed. With current technology, the operator must manually interact with the AR interface using a computer. The latter poses a disruption in the surgical flow and is suboptimal for maintaining the sterility of the operating environment. The purpose of this study was to implement and evaluate free-hand interaction concepts leveraging hand tracking and gesture recognition as an attempt to reduce the disruption during surgery and improve human-computer interaction. METHODS: An electromagnetically tracked surgical microscope was calibrated using a custom 3D printed calibration board. This allowed the augmentation of the microscope feed with segmented preoperative CT-derived virtual models. Ultraleap's Leap Motion Controller 2 was coupled to the microscope and used to implement hand-tracking capabilities. End-user feedback was gathered from a surgeon during development. Finally, users were asked to complete tasks that involved interacting with the virtual models, aligning them to physical targets, and adjusting the AR visualization. RESULTS: Following observations and user feedback, we upgraded the functionalities of the hand interaction system. User feedback showed the users' preference for the new interaction concepts that provided minimal disruption of the surgical workflow and more intuitive interaction with the virtual content. CONCLUSION: We integrated hand interaction concepts, typically used with head-mounted displays (HMDs), into a surgical stereo microscope system intended for AR in otologic microsurgery. The concepts presented in this study demonstrated a more favorable approach to human-computer interaction in a surgical context. They hold potential for a more efficient execution of surgical tasks under microscopic AR guidance.

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Improving the quality of life for people with vision impairments has been an important goal in the research and design of assistive devices for several decades. This paper seeks to further that goal by introducing a novel assistive technology platform that leverages real-time 3D spatial audio to promote safe and efficient navigation for people who are blind or visually impaired (PVI). The presented platform, EchoSee, uses modern 3D scanning technology on a mobile device to construct a live, digital 3D map of a user's environment as they move about their surroundings. Spatialized, virtual audio sources (i.e., virtual speakers) are dynamically placed within the digital 3D scan of the world, providing the navigator with a real-time 3D stereo audio "soundscape." The digital 3D map, and its resultant soundscape, are continuously updated as the user moves about their environment. The generated soundscape is played back through headphones connected to the navigator's device. This paper details (1) the underlying technical components and how they were integrated to produce the mobile application that generates a dynamic soundscape on a consumer mobile device, (2) a methodology for analyzing navigation performance with the application, (3) the design and execution of a user study investigating the effectiveness of the presented system, and (4) a discussion of the results of that study along with a proposed future study and possible improvements. Altogether, this paper presents a novel software platform aimed at assisting individuals with vision impairments to navigate and understand spaces safely, efficiently, and independently and the results of a feasibility study analyzing the viability of the approach.

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Side channels are unintended pathways within target systems that leak internal target information. Side-channel sensing (SCS) is the process of exploiting side channels to extract embedded target information. SCS is well established within the cybersecurity (CYB) domain, and has recently been proposed for medical diagnostics and monitoring (MDM). Remaining unrecognised is its applicability to human-computer interaction (HCI), among other domains (Misc). This article analyses literature demonstrating SCS examples across the MDM, HCI, Misc, and CYB domains. Despite their diversity, established fields of advanced sensing and signal processing underlie each example, enabling the unification of these currently otherwise isolated domains. Identified themes are collating under a proposed domain-agnostic SCS framework. This SCS framework enables a formalised and systematic approach to studying, detecting, and exploiting of side channels both within and between domains. Opportunities exist for modelling SCS as data structures, allowing for computation irrespective of domain. Future methodologies can take such data structures to enable cross- and intra-domain transferability of extraction techniques, perform side-channel leakage detection, and discover new side channels within target systems.

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BACKGROUND: Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder characterized by persistent impairments in communication, social interaction and learning (Hodges et al., 2020). Because of its heterogeneous nature, ASD presents complex challenges, including social exclusion, school abandonment and limited access to health care. Technologies offer a pragmatic solution to overcome these limitations and to deliver therapeutic interventions in both physical and online environments. Studies have demonstrated that technology-mediated interventions (TMIs) have a positive impact on the development of capabilities of individuals with ASD. However, according to the European Parliament's Research Service technologies for ASDs are less mature (European Parliament. Directorate General for Parliamentary Research Services., 2018) and the causes and effects of TMIs in ASD should be better understood. AIMS: Present study aimed to investigate the psychological variables related to TMIs in ASD. METHODS AND PROCEDURES: The study employed a survey research method with a between-subjects design involving 61 adult participants comprising therapists, teachers and parents of children with ASD and it examined psychological variables related to children with ASD, factors of technology usage, and parental and educators' experiences. OUTCOMES AND RESULTS: The analysis of the data obtained from the questionnaire focused on correlations and differences between groups, and was carried out using nonparametric tests. Kendall's Tau B test was used to explore the relationships between variables. The Mann-Whitney nonparametric test was employed to identify differences between groups. CONCLUSIONS AND IMPLICATIONS: The findings revealed numerous relationships between variables and meaningful differences between the groups investigated in terms of how technologies are perceived by stakeholders involved in ASD interventions.

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Taste sensation recognition is a core for taste-related queries. Most prior research has been devoted to recognizing the basic taste sensations using the Brain-Computer Interface (BCI), which includes EEG, MEG, EMG, and fMRI. This research aims to recognize electronic taste (E-Taste) sensations based on surface electromyography (sEMG). Silver electrodes with platinum plating of the E-Taste device were placed on the tongue's tip to stimulate various tastes and flavors. In contrast, the electrodes of the sEMG were placed on facial muscles to collect the data. The dataset was organized and preprocessed, and a random forest classifier was applied, giving a five-fold accuracy of 70.43%. The random forest classifier was used on each participant dataset individually and in groups, providing the highest accuracy of 84.79% for a single participant. Moreover, various feature combinations were extracted and acquired 72.56% accuracy after extracting eight features. For a future perspective, this research offers guidance for electronic taste recognition based on sEMG.

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BACKGROUND: Precise dose position distribution is crucial for ocular proton therapy. METHODS: A non-invasive eye positioning and tracking system with novel structure is designed to reduce eye movement and facilitate precise dose by guiding the direction of patients' gaze. The system helps to achieve gaze guidance by controlling the light source fixed on two turntables above the patient's face. Tracking of the eye is achieved by cameras attached to the end of a 6DOFs robotic arm to capture the image reflected from a mirror above the patient's face. RESULTS: After all operation steps, the accuracy of the robotic arm is 0.18 mm (SD 0.25 mm) and the accuracy of the turntables is 0.01° (SD 0.02°). The EPTS is tested to be remotely controlled in real time with sufficient precision and repeatability. CONCLUSION: The system is expected to improve the safety and efficiency of ocular proton therapy.

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The rapid development of information technology and the continuous improvement of human spiritual and cultural level have made people's acquisition of knowledge and information increasingly urgent. The information transmission method of traditional navigation systems is single and passive. This study aims to improve user experience and enhance the interactivity of museum visits. This article combines interactive technology in the Internet of Things to conduct in-depth research on the tour guide system in museums. By utilizing IoT technology, optimize the interaction design of the intelligent navigation system, and enhance the convenience and experience of visitors. In the experimental analysis, this article compared the main ticket purchasing methods of tourists, the level of understanding of museum staff about the collection, the amount of information obtained by tourists, and the satisfaction of tourists. The comparison results show that the proportion of electronic ticket purchases has increased by 35 % in terms of ticket purchasing methods. The staff's understanding of the collection has increased by 19 %, the amount of information received by tourists has increased by 12.7 %, and tourist satisfaction has also increased by 24.3 %. The conclusion indicates that the interactive design navigation system based on intelligent Internet of Things and interactive design can effectively meet the needs of different users and provide the public with a richer and deeper cultural experience.

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BACKGROUND: For many young people, the transition from child to adult mental health services is a vulnerable time associated with treatment disengagement and illness progression. Providing service information and options to youth, appealing to them, and tailoring to their needs during this period could help overcome systematic barriers to a successful transition. We know little about how SMS text message-based interventions might be leveraged to support the motivational, informational, and behavioral needs of youth during this time. Ascertaining youth preferences for the content and functionality of an SMS text message service could inform prototype development. OBJECTIVE: This study investigated consensus preferences among youth on important content, technology features, and engagement supports to inform a transition-focused SMS text message service. METHODS: A modified e-Delphi survey design was used to collect demographics, current levels of technology use, importance ratings on message content, preferred technical features, and barriers and enablers to engagement for youth in Canada aged 16-26 years who have accessed mental health services within the past 5 years. Survey items on content were categorized according to the information-motivation-behavioral skills (IMB) model. Survey items on technical features were categorized according to the persuasive system design (PSD) model. A predefined consensus rating matrix and descriptive statistics were used to characterize the sample. The high consensus threshold was 70%. RESULTS: A total of 100 participants, predominantly non-White (n=47, 47%), aged 20-26 years (n=59, 59%), and who had first accessed mental health services between the ages of 13 and 19 years (n=60, 60%), were selected. The majority (n=90, 90%) identified as daily SMS text message users. A high level of consensus on importance ratings was reported in 45% (9/20) of content items based on the IMB model. There were higher levels of consensus on importance ratings related to behavior domain items (3/3, 100%) than information domain items (4/9, 44%) or motivation domain items (2/8, 25%). A high level of consensus on importance ratings was reported in only 19% (4/21) of feature and functionality items based on the PSD model. Among PSD model categories, there was a high level of consensus on importance ratings in 8% (1/12) of the primary task support domain items and 100% (3/3) of the system credibility support domain items. None of the dialogue-support and social-support domain items met the high level of consensus thresholds. In total, 27% (27/100) of youth indicated that the most significant enabler for engaging with a transition-focused SMS text message intervention was the personalization of text messages. CONCLUSIONS: Scientists developing next-generation SMS text messaging interventions for this population need to consider how levels of consensus on different features may impact feasibility and personalization efforts. Youth can (and should) play an integral role in the development of these interventions.

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Some of the barriers preventing virtual reality (VR) from being widely adopted are the cost and unfamiliarity of VR systems. Here, we propose that in many cases, the specialized controllers shipped with most VR head-mounted displays can be replaced by a regular smartphone, cutting the cost of the system, and allowing users to interact in VR using a device they are already familiar with. To achieve this, we developed SmartVR Pointer, an approach that uses smartphones to replace the specialized controllers for two essential operations in VR: selection and navigation by teleporting. In SmartVR Pointer, a camera mounted on the head-mounted display (HMD) is tilted downwards so that it points to where the user will naturally be holding their phone in front of them. SmartVR Pointer supports three selection modalities: tracker based, gaze based, and combined/hybrid. In the tracker-based SmartVR Pointer selection, we use image-based tracking to track a QR code displayed on the phone screen and then map the phone's position to a pointer shown within the field of view of the camera in the virtual environment. In the gaze-based selection modality, the user controls the pointer using their gaze and taps on the phone for selection. The combined technique is a hybrid between gaze-based interaction in VR and tracker-based Augmented Reality. It allows the user to control a VR pointer that looks and behaves like a mouse pointer by moving their smartphone to select objects within the virtual environment, and to interact with the selected objects using the smartphone's touch screen. The touchscreen is used for selection and dragging. The SmartVR Pointer is simple and requires no calibration and no complex hardware assembly or disassembly. We demonstrate successful interactive applications of SmartVR Pointer in a VR environment with a demo where the user navigates in the virtual environment using teleportation points on the floor and then solves a Tetris-style key-and-lock challenge.