User Study Research Articles

A Digital Mental Health Solution to Improve Social, Emotional, and Learning Skills for Youth: Protocol for an Efficacy and Usability Study

Background The COVID-19 pandemic has exposed a devastating youth mental health crisis in the United States, characterized by an all-time high prevalence of youth mental illness. This crisis is exacerbated by limited access to mental health services and the reduction of mental health support in schools. Mobile health platforms offer a promising avenue for delivering tailored and on-demand mental health care. Objective To address the lack of youth mental health services, we created the Science Technology Engineering Math and Social and Emotional Learning (STEMSEL) study. Our aim was to investigate the efficacy of a digital mental health intervention, Neolth, in enhancing social and emotional well-being, reducing academic stress, and increasing mental health literacy and life skills among adolescents. Methods The STEMSEL study will involve the implementation and evaluation of Neolth across 4 distinct phases. In phase 1, a comprehensive needs assessment will be conducted across 3 diverse schools, each using a range of teaching methods, including in-person, digital, and hybrid modalities. Following this, in phase 2, school administrators and teachers undergo intensive training sessions on Neolth’s functionalities and intervention processes as well as understand barriers and facilitators of implementing a digital mental health program at their respective schools. Phase 3 involves recruiting middle and high school students aged 11-18 years from the participating schools, with parental consent and student assent obtained, to access Neolth. Students will then be prompted to complete an intake questionnaire, enabling the customization of available modules to address their specific needs. Finally, phase 4 will include a year-long pre- and posttest pilot study to rigorously evaluate the usability and effectiveness of Neolth in addressing the mental health concerns of students across the selected schools. Results Phase 1 was successfully completed in August 2022, revealing significant deficits in mental health resources within the participating schools. The needs assessment identified critical gaps in available mental health support services. We are currently recruiting a diverse group of middle and high school students to participate in the study. The study’s completion is scheduled for 2024, with data expected to provide insights into the real-world use of Neolth among the adolescent population. It is designed to deliver findings regarding the intervention’s efficacy in addressing the mental health needs of students. Conclusions The STEMSEL study plays a crucial role in assessing the feasibility and adoption of digital mental health interventions within the school-aged youth population in the United States. The findings generated from this study have the potential to dismantle obstacles to accessing mental health assistance and broaden the availability of care through evidence-based strategies. International Registered Report Identifier (IRRID) DERR1-10.2196/59372

The Depth Estimation and Visualization of Dermatological Lesions: Development and Usability Study.

Thus far, considerable research has been focused on classifying a lesion as benign or malignant. However, there is a requirement for quick depth estimation of a lesion for the accurate clinical staging of the lesion. The lesion could be malignant and quickly grow beneath the skin. While biopsy slides provide clear information on lesion depth, it is an emerging domain to find quick and noninvasive methods to estimate depth, particularly based on 2D images. This study proposes a novel methodology for the depth estimation and visualization of skin lesions. Current diagnostic methods are approximate in determining how much a lesion may have proliferated within the skin. Using color gradients and depth maps, this method will give us a definite estimate and visualization procedure for lesions and other skin issues. We aim to generate 3D holograms of the lesion depth such that dermatologists can better diagnose melanoma. We started by performing classification using a convolutional neural network (CNN), followed by using explainable artificial intelligence to localize the image features responsible for the CNN output. We used the gradient class activation map approach to perform localization of the lesion from the rest of the image. We applied computer graphics for depth estimation and developing the 3D structure of the lesion. We used the depth from defocus method for depth estimation from single images and Gabor filters for volumetric representation of the depth map. Our novel method, called red spot analysis, measures the degree of infection based on how a conical hologram is constructed. We collaborated with a dermatologist to analyze the 3D hologram output and received feedback on how this method can be introduced to clinical implementation. The neural model plus the explainable artificial intelligence algorithm achieved an accuracy of 86% in classifying the lesions correctly as benign or malignant. For the entire pipeline, we mapped the benign and malignant cases to their conical representations. We received exceedingly positive feedback while pitching this idea at the King Edward Memorial Institute in India. Dermatologists considered this a potentially useful tool in the depth estimation of lesions. We received a number of ideas for evaluating the technique before it can be introduced to the clinical scene. When we map the CNN outputs (benign or malignant) to the corresponding hologram, we observe that a malignant lesion has a higher concentration of red spots (infection) in the upper and deeper portions of the skin, and that the malignant cases have deeper conical sections when compared with the benign cases. This proves that the qualitative results map with the initial classification performed by the neural model. The positive feedback provided by the dermatologist suggests that the qualitative conclusion of the method is sufficient.

TurnAware: motion-aware Augmented Reality information delivery while walking

Augmented Reality (AR) systems provide users with timely access to everyday information. Designing how AR messages are presented to the user, however, is challenging. If a visual message is presented suddenly in users’ field of view, it will be noticed easily, but might be disruptive to users. Conversely, if messages are made visible by slowly fading their opacity, for example, they might require more effort for users to notice and react, as they need to wait for the content to appear. This is particularly true for head-anchored virtual content, and when users are engaged in other tasks or walking in a physical environment. To address this challenge, we introduce a motion-aware technique that delivers AR visual information unobtrusively during walking when users rotate their head. When users make a turn, TurnAware moves the visual content into their field of view from the side at a speed proportional to their rotational velocity. We compare our method to a Fade-in and Pop-up baseline in a user study. Our results show that our method enables users to react to virtual content in a timely manner, while minimizing disruption on their walking patterns. Our technique improves current AR information delivery techniques by striking a balance between noticeability and disruptiveness.

Haptiknit: Distributed stiffness knitting for wearable haptics.

Haptic devices typically rely on rigid actuators and bulky power supply systems, limiting wearability. Soft materials improve comfort, but careful distribution of stiffness is required to ground actuation forces and enable load transfer to the skin. We present Haptiknit, an approach in which soft, wearable, knit textiles with embedded pneumatic actuators enable programmable haptic display. By integrating pneumatic actuators within high- and low-stiffness machine-knit layers, each actuator can transmit 40 newtons in force with a bandwidth of 14.5 hertz. We demonstrate the concept with an adjustable sleeve for the forearm coupled to an untethered pneumatic control system that conveys a diverse array of social touch signals. We assessed the sleeve's performance for discriminative and affective touch in a three-part user study and compared our results with those of prior electromagnetically actuated approaches. Haptiknit improves touch localization compared with vibrotactile stimulation and communicates social touch cues with fewer actuators than pneumatic textiles that do not invoke distributed stiffness. The Haptiknit sleeve resulted in similar recognition of social touch gestures compared to a voice-coil array but represented a more portable and comfortable form factor.

A novel approach to sustainable behavior enhancement through AI-driven carbon footprint assessment and real-time analytics

This research introduces an Artificial Intelligence-driven mobile application designed to help users calculate and reduce their Carbon Footprint (CFP). The proposed system employs an Intelligent Sustainable Behavior Tracking and Recommendation System, analyzing users' carbon emissions from daily activities and suggesting eco-friendly alternatives. It facilitates sustainability discussions through its chat community and educates users on sustainable practices via an intelligent chatbot powered by a sustainability knowledge base. To promote social engagement around sustainability, the application incorporates a competition and reward system. Additionally, it aggregates behavioral data to inform government sustainability policies and address challenges. Emphasizing individual responsibility, the proposed system stands out from other systems by offering a comprehensive solution that integrates recommendation, education, monitoring, and community engagement, contributing to the cultivation of sustainable communities. The results of a user study (n = 10) employing paired sample t-tests across the three dimensions of the Theory of Reasoned Action (TRA) revealed varying effects of using the application on attitudes, subjective norms, and behavioral intentions related to promoting sustainable human behavior. While the application did not yield significant changes in attitudes (t (9) = 1.7, p = 0.123), or behavioral intentions (t (9) = 0.6, p = 0.541), it did produce a significant increase in subjective norms (t (9) = 4.2, p = 0.002). This suggests that while attitudes towards using this application for sustainability and behavioral intentions remained relatively stable, there was a notable impact on the perception of social influence to engage in sustainable behavior through the use of the application attributed to the sustainability reward system.

Operator Augmentation for Improving Human Performance of Tasks With Uncertainties

Frazing is a manual task in turbine blade manufacturing companies, in which the operators must discover and repair the defects of the workpieces. Such detection is so attention-demanding and time-consuming that the operators sometimes miss the defects, resulting in poor human performance and quality. Hence, this study aims to guide the operators with a systematic visual search using augmented reality (AR). For this purpose, an AR-based assistance system was built. To evaluate the system’s usability and effectiveness, a user study was conducted on 24 skilled operators. The results showed that the performance of the augmented operators was enhanced as the task completion time and detection misses decreased using the AR tool by 11.6 and 100 percent, respectively. The mental workload declined significantly after using the AR system. The most prominent finding of this research is that the AR tool was quite effective in reducing mental fatigue and workload, leading to improved human performance in ever-changing tasks.

Evaluation of a New Mobile Virtual Reality Setup to Alter Pain Perception: Pilot Development and Usability Study in Healthy Participants.

Chronic pain presents a significant treatment challenge, often leading to frustration for both patients and therapists due to the limitations of traditional methods. Research has shown that synchronous visuo-tactile stimulation, as used in the rubber hand experiment, can induce a sense of ownership over a fake body part and reduces pain perception when ownership of the fake body part is reported. The effect of the rubber hand experiment can be extended to the full body, for example, during the full-body illusion, using both visuo-tactile and cardiovisual signals. This study first aimed to evaluate the usability and accuracy of a novel, mobile virtual reality (VR) setup that displays participants' heartbeats as a flashing silhouette on a virtual avatar, a technique known as the cardiovisual full-body illusion. The second part of the study investigated the effects of synchronous cardiovisual stimulation on pain perception and ownership in 20 healthy participants as compared with asynchronous stimulation (control condition). The setup comprised a head-mounted display (HMD) and a heart rate measurement device. A smartphone-based HMD (Samsung Galaxy S8+) was selected for its mobility, and heart rates were measured using smartwatches with photoplethysmography (PPG). The accuracy of 2 smartwatch positions was compared with a 5-point electrocardiogram (ECG) standard in terms of their accuracy (number and percent of missed beats). Each participant underwent two 5-minute sessions of synchronous cardiovisual stimulation and two 5-minute sessions of asynchronous cardiovisual stimulation (total of 4 sessions), followed by pain assessments. Usability, symptoms of cybersickness, and ownership of the virtual body were measured using established questionnaires (System Usability Scale, Simulator Sickness Questionnaire, Ownership Questionnaire). Pain perception was assessed using advanced algometric methods (Algopeg and Somedic algometer). Results demonstrated high usability scores (mean 4.42, SD 0.56; out of 5), indicating ease of use and acceptance, with minimal side effects (mean 1.18, SD 0.46; out of a possible 4 points on the Simulator Sickness Questionnaire). The PPG device showed high heart rate measurement precision, which improved with optimized filtering and peak detection algorithms. However, compared with previous work, no significant effects on body ownership and pain perception were observed between the synchronous and asynchronous conditions. These findings are discussed in the context of existing literature on VR interventions for chronic pain. In conclusion, while the new VR setup showed high usability and minimal side effects, it did not significantly affect ownership or pain perception. This highlights the need for further research to refine VR-based interventions for chronic pain management, considering factors like visual realism and perspective.

Rapid, Tailored Dietary and Health Education Through A Social Media Chatbot Microintervention: Development and Usability Study With Practical Recommendations.

There is an urgent need to innovate methods of health education, which can often be resource- and time-intensive. Microinterventions have shown promise as a platform for rapid, tailored resource dissemination yet have been underexplored as a method of standardized health or dietary education; social media chatbots display unique potential as a modality for accessible, efficient, and affordable educational microinterventions. This study aims to provide public health professionals with practical recommendations on the use of social media chatbots for health education by (1) documenting the development of a novel social media chatbot intervention aimed at improving dietary attitudes and self-efficacy among South Asian American young adults and (2) describing the applied experiences of implementing the chatbot, along with user experience and engagement data. In 2023, the "Roti" chatbot was developed on Facebook and Instagram to administer a 4-lesson tailored dietary health curriculum, informed by formative research and the Theory of Planned Behavior, to 18- to 29-year-old South Asian American participants (recruited through social media from across the United States). Each lesson (10-15 minutes) consisted of 40-50 prescripted interactive texts with the chatbot (including multiple-choice and open-response questions). A preintervention survey determined which lesson(s) were suggested to participants based on their unique needs, followed by a postintervention survey informed by the Theory of Planned Behavior to assess changes in attitudes, self-efficacy, and user experiences (User Experience Questionnaire). This study uses a cross-sectional design to examine postintervention user experiences, engagement, challenges encountered, and solutions developed during the chatbot implementation. Data from 168 participants of the intervention (n=92, 54.8% Facebook; n=76, 45.2% Instagram) were analyzed (mean age 24.5, SD 3.1 years; n=129, 76.8% female). Participants completed an average of 2.6 lessons (13.9 minutes per lesson) and answered an average of 75% of questions asked by the chatbot. Most reported a positive chatbot experience (User Experience Questionnaire: 1.34; 81/116, 69.8% positive), with pragmatic quality (ease of use) being higher than hedonic quality (how interesting it felt; 88/116, 75.9% vs 64/116, 55.2% positive evaluation); younger participants reported greater hedonic quality (P=.04). On a scale out of 10 (highest agreement), participants reported that the chatbot was relevant (8.53), that they learned something new (8.24), and that the chatbot was helpful (8.28). Qualitative data revealed an appreciation for the cheerful, interactive messaging of the chatbot and outlined areas of improvement for the length, timing, and scope of text content. Quick replies, checkpoints, online forums, and self-administered troubleshooting were some solutions developed to meet the challenges experienced. The implementation of a standardized, tailored health education curriculum through an interactive social media chatbot displayed strong feasibility. Lessons learned from challenges encountered and user input provide a tangible roadmap for future exploration of such chatbots for accessible, engaging health interventions.

Evaluation of RMES, an Automated Software Tool Utilizing AI, for Literature Screening with Reference to Published Systematic Reviews as Case-Studies: Development and Usability Study.

Systematic reviews and meta-analyses are important to evidence-based medicine, but the information retrieval and literature screening procedures are burdensome tasks. Rapid Medical Evidence Synthesis (RMES; Deloitte Tohmatsu Risk Advisory LLC) is a software designed to support information retrieval, literature screening, and data extraction for evidence-based medicine. This study aimed to evaluate the accuracy of RMES for literature screening with reference to published systematic reviews. We used RMES to automatically screen the titles and abstracts of PubMed-indexed articles included in 12 systematic reviews across 6 medical fields, by applying 4 filters: (1) study type; (2) study type + disease; (3) study type + intervention; and (4) study type + disease + intervention. We determined the numbers of articles correctly included by each filter relative to those included by the authors of each systematic review. Only PubMed-indexed articles were assessed. Across the 12 reviews, the number of articles analyzed by RMES ranged from 46 to 5612. The number of PubMed-cited articles included in the reviews ranged from 4 to 47. The median (range) percentage of articles correctly labeled by RMES using filters 1-4 were: 80.9% (57.1%-100%), 65.2% (34.1%-81.8%), 70.5% (0%-100%), and 58.6% (0%-81.8%), respectively. This study demonstrated good performance and accuracy of RMES for the initial screening of the titles and abstracts of articles for use in systematic reviews. RMES has the potential to reduce the workload involved in the initial screening of published studies.

Multisensor‐Driven Lightweight Networks for Intelligent Sports Training System: Design and Research

ABSTRACTEdge computing and multisensor integration have revolutionized sports training technologies, yet many existing systems remain computationally intensive and complex for everyday use. This study introduces an efficient and user‐friendly intelligent sports training system that leverages edge‐based multisensor data processing with lightweight neural networks. The system utilizes low‐latency activity and real‐time pose estimation data, powered by a modified MobileNetV2 model, to generate personalized training plans through reinforcement learning. Tests show that the system matches the accuracy of more complex models while significantly reducing computational needs. Compression techniques further enhance efficiency with minimal accuracy loss. User studies revealed notable improvements in fitness levels and adherence compared to traditional methods. The system adapts to real‐time user performance, offering feedback and dynamically adjusting plans, with low energy consumption across mobile devices. This research shows that our developed system, which integrates multisensors and artificial intelligence, can make personalized sports training more accessible.

FBG‐driven simulation for virtual augmentation of fluoroscopic images during endovascular interventions

AbstractEndovascular interventions are procedures designed to diagnose and treat vascular diseases, using catheters to navigate inside arteries and veins. Thanks to their minimal invasiveness, they offer many benefits, such as reduced pain and hospital stays, but also present many challenges for clinicians, as they require specialized training and heavy use of X‐rays. This is particularly relevant when accessing (i.e. cannulating) small arteries with steep angles, such as most aortic branches. To address this difficulty, a novel solution that enhances fluoroscopic 2D images in real‐time by displaying virtual configurations of the catheter and guidewire is proposed. In contrast to existing works, proposing either simulators or simple augmented reality frameworks, this approach involves a predictive simulation showing the resulting shape of the catheter after guidewire withdrawal without requiring the clinician to perform this task. This system demonstrated accurate prediction with a mean 3D error of 2.4 1.3 mm and a mean error of 1.1 0.7 mm on the fluoroscopic image plane between the real catheter shape after guidewire withdrawal and the predicted shape. A user study reported an average intervention time reduction of 56 when adopting this system, resulting in a lower X‐ray exposure.

All the Way There and Back: Inertial-Based, Phone-in-Pocket Indoor Wayfinding and Backtracking Apps for Blind Travelers

We describe two iOS apps designed to support blind travelers navigating in indoor building environments. The Wayfinding app provides guidance to a blind user while following a certain route. The Backtracking app records the route taken by the walker towards a certain destination and then provides guidance while re-tracing the same trajectory in the opposite direction. Our apps only use the inertial and magnetic sensors of the smartphone, and thus require no infrastructure modification (e.g., installation and support of BLE beacons). Unlike systems that use the phone’s camera, users of our apps can conveniently keep their phone tucked inside their pocket while interacting with the apps using a smartwatch. Routing directions are given via speech. Both apps were tested in a user study with seven blind participants who used them while navigating a campus building. Participants were able to successfully use the Wayfinding app to complete the prescribed paths (three paths each), although the app had to be restarted for the first three participants in one path due to incorrect step length measurements (the app was later modified to track the users’ step length). The Backtracking app worked well in most cases, although in 6 trials (out of 21), the app lost track of the participant’s location.

Examining the Effectiveness of Mnemonics Serious Games in Enhancing Memory and Learning: A Scoping Review

Mnemonics hold potential for promoting long-term memory. Hence, they are being leveraged in serious games aimed to support long-term retention and retrieval of information. However, there is limited work focused on synthesizing the published research and findings on mnemonics serious games with a view to uncovering the extent of their application and effectiveness. This scoping review aims to bridge this gap. Articles were retrieved from four databases (ACM Library, IEEE Xplore, Scopus, and Web of Science). The criteria for inclusion were that the papers must be user studies that focused on mnemonics and serious games at the same time, were written in English, and were published in peer-reviewed journals or conferences. Two researchers, with the guidance of a senior researcher, independently and collaboratively assessed the eligibility of the retrieved papers using the PRISMA flowchart, elicited the relevant data, and tabulated the results in tables and charts using the GPS (game play, purpose, and scope) model. There were 12 papers that were accepted in this scoping review. Overall, most of the mnemonics serious games had a positive effect on memory, suggesting that they hold potential for promoting long-term memory, especially in memorization-intensive instructions, where a good number of students still struggle to retain taught material due to pedagogical, personal, and social challenges. However, more research still needs to be conducted, especially in the area of player-created mnemonics and teaching users how mnemonics can be effectively created using visualization and elaboration techniques.

AI-Powered VR for Enhanced Learning Compared to Traditional Methods

This paper evaluates a VR (Virtual Reality) application aimed at enhancing the learning of Python collection data types and structures for electrical and electronic engineering students. By incorporating gamification and personalisation features, the application provides an immersive environment where students can interact with virtual representations of complex programming concepts. To further enhance interactivity and engagement, the application integrates a virtual assistant and example generator, developed using Meta Voice SDK (Software Development Kit) and wit.ai. These AI (Artificial Intelligence)-NLP (Natural Language Processing) tools create personalised learning paths and generate dynamic examples based on individual learning progress. A user study was conducted with a total of 48 participants. During the user study, participants were divided into two equal groups of 24, both wearing EEG (Electroencephalography) headsets: one group engaged with the VR application, while the other read the traditional booklet, allowing for the recording and analysis of attention and engagement levels. These measures of engagement and attention were then compared to those extracted from a benchmark cohort of students whose learning experience was through more traditional booklets. The results indicated a statistically significant improvement in understanding Python collections among VR users compared to their baseline scores, highlighting the benefits of interactive and tailored learning environments. Additionally, EEG data analysis showed that VR users exhibited higher average levels of attention and engagement compared to those using the paper-based method, demonstrating the effectiveness of immersive technologies in sustaining learner interest and focus, particularly in enhancing learning for software development.

A Novel End-to-End Provenance System for Predictive Maintenance: A Case Study for Industrial Machinery Predictive Maintenance

In this study, we address the critical gap in predictive maintenance systems regarding the absence of a robust provenance system and specification. To tackle this issue, we propose a provenance system based on the PROV-O schema, designed to enhance explainability, accountability, and transparency in predictive maintenance processes. Our framework facilitates the collection, processing, recording, and visualization of provenance data, integrating them seamlessly into these systems. We developed a prototype to evaluate the effectiveness of our approach and conducted comprehensive user studies to assess the system’s usability. Participants found the extended PROV-O structure valuable, with improved task completion times. Furthermore, performance tests demonstrated that our system manages high workloads efficiently, with minimal overhead. The contributions of this study include the design of a provenance system tailored for predictive maintenance and a specification that ensures scalability and efficiency.

Design and evaluation of a robot telemedicine system for initial medical examination with UK and Thai doctors

AbstractThis study presents the evaluation of a robot telemedicine system used for initial diagnosis of patient with UK and Thai doctors. In a prior study, a set of user requirements for a robot telemedicine system were outlined based on a focus group with medical professionals and an online survey with the public. Based on these recommendations we have developed a robot telemedicine system that consists of a telepresence interface with examination functionality for doctors to control a robot that is present with patients. The system is designed for initial diagnosis of medical conditions, and Bell’s palsy diagnosis is chosen as an example use case. This paper presents the design and usability evaluation of this robot telemedicine system. The usability study was conducted using a thinking-aloud protocol, and a semi-structured interview was conducted after using the system. Study results were analysed using framework analysis. Additionally, participants were asked to give subjective ratings of the system. The study was conducted cross-culturally with doctors from the UK and Thailand, allowing us to gain a deeper understanding of system usability with groups that differ in their medical practice and user needs. Study participants rated the system with 72.19 points on the System Usability Scale, which indicates acceptable usability when interpreted semantically. The system also received a presence score of 0.691, normalized between 0 (least) and 1 (highest). Based on the framework analysis the prior set of user requirements has been refined and developed into a set of design recommendations for use in the development of future robot telemedicine systems. Additionally, study results indicate differences in medical practice between UK and Thai doctors which could be relevant to adjust the robot telemedicine system to local users.

How different levels of semantic segmentation affect human perception of driving scenes

As automated vehicles continue to advance, teleoperation has emerged as a critical support system for navigating complex and unpredictable environments that exceed the vehicles' current autonomous capabilities. A main issue in the implementation of teleoperation is latency caused by the high bandwidth required to transmit the video feed from the vehicle to the remote teleoperation station. A possible approach for addressing the latency problem is the transfer of lower-resolution or compressed videos between the vehicle and the teleoperation station. When applying semantic segmentation on the video feed, many pixels are mapped to a limited set of possible colors according to the types of objects that they represent. This concept has been commonly used in autonomous driving algorithms and has the potential to enable the transferring of smaller-sized videos thus reducing bandwidth. In this study, we examine how presenting semantically segmented driving scenes to humans affects their perception of the scene, and specifically, how it affects their hazard perception and situation awareness. We conducted two user studies comparing the effects of using different levels and types of semantic segmentation. Our results indicate that viewing partly segmented scenes, such that only a selected set of object types are colored, commonly achieves the same effect, and sometimes even outperforms a realistic view. Our study and its insights may pave the way for future research, development, and design of teleoperation systems of automated vehicles.

Intent-based Task-Oriented Shared Control for Intuitive Telemanipulation

The unique challenges in shared control for telemanipulation-beyond teleoperation-include physical discrepancy between human and robot hands and the fine manipulation constraints needed for task success. We present an intuitive shared-control strategy that generates robotic grasp poses better suited for human perception of success and feeling of control while ensuring a stable grasp for task success. The robot’s motion follows an arbitration between following the user’s motion constraints and accomplishing the inferred task. The arbitration adapts based on the physical discrepancy between the human and robot hands. We have conducted a user study with a telemanipulation scenario to analyze the effects of task predictability, following, and user preference. The results demonstrated that intent-based approaches provide advantages over direct motion mapping strategies.

Feature Weighted Cycle Generative Adversarial Network with Facial Landmark Recognition and Perceptual Color Distance for Enhanced Face Animation Generation

We propose an anime style transfer model to generate anime faces from human face images. We improve the model by modifying the normalization function to obtain more feature information. To make the face feature position of the anime face similar to the human face, we propose facial landmark loss to calculate the error between the generated image and the real human face image. To avoid obvious color deviation in the generated images, we introduced perceptual color loss into the loss function. In addition, due to the lack of reasonable metrics to evaluate the quality of the animated images, we propose the use of Fréchet anime inception distance to calculate the distance between the distribution of the generated animated images and the real animated images in high-dimensional space, so as to understand the quality of the generated animated images. In the user survey, up to 74.46% of users think that the image produced by the proposed method is the best compared with other models. Also, the proposed method reaches a score of 126.05 for Fréchet anime inception distance. Our model performs the best in both user studies and FAID, showing that we have achieved better performance in human visual perception and model distribution. According to the experimental results and user feedback, our proposed method can generate results with better quality compared to existing methods.

Assessing the Value of Information in an Augmented Reality City Experience

Information is an experience good, meaning that its value emerges upon use and varies based on individual perceptions. Augmented reality (AR) is a technology designed to deliver immersive informational experiences. This study investigates the impact of AR on the perceived value of information by people experiencing information as consumers or producers. Using Google Maps, we developed an AR mobile map called Tour-It-Yourself (TIY) for city navigation. The TIY app facilitates content consumption and production around local points of interest engagingly and interactively. We discuss the development of the research tool and results from two user studies (N = 37 teenagers and N = 51 students, respectively). Further, we highlight the economic valuation aspect by examining the effects of AR on Willingness-to-Pay and Willingness-to-Accept payment for information. The results indicate a significant main effect of participant experience (consumer/producer) and AR technology on the perceived evaluation of information. Moreover, the addition of AR technology to information improves knowledge regarding points of interest for producers. These findings contribute to our understanding of how AR shapes information value perception and have implications for designing interactive location-based information systems, benefiting tourism, education, and business, as well as fostering a sense of belonging in cities.