Journal articles: 'Tracking experts'

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Wang, Ning, Wengang Zhou, Guojun Qi, and Houqiang Li. "POST: POlicy-Based Switch Tracking." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12184–91. http://dx.doi.org/10.1609/aaai.v34i07.6899.

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In visual object tracking, by reasonably fusing multiple experts, ensemble framework typically achieves superior performance compared to the individual experts. However, the necessity of parallelly running all the experts in most existing ensemble frameworks heavily limits their efficiency. In this paper, we propose POST, a POlicy-based Switch Tracker for robust and efficient visual tracking. The proposed POST tracker consists of multiple weak but complementary experts (trackers) and adaptively assigns one suitable expert for tracking in each frame. By formulating this expert switch in consecutive frames as a decision-making problem, we learn an agent via reinforcement learning to directly decide which expert to handle the current frame without running others. In this way, the proposed POST tracker maintains the performance merit of multiple diverse models while favorably ensuring the tracking efficiency. Extensive ablation studies and experimental comparisons against state-of-the-art trackers on 5 prevalent benchmarks verify the effectiveness of the proposed method.

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Gyorgy, András, Tamás Linder, and Gábor Lugosi. "Efficient Tracking of Large Classes of Experts." IEEE Transactions on Information Theory 58, no. 11 (November 2012): 6709–25. http://dx.doi.org/10.1109/tit.2012.2209627.

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Prytz, Erik G., Caroline Norén, and Carl-Oscar Jonson. "Fixation Differences in Visual Search of Accident Scenes by Novices and Expert Emergency Responders." Human Factors: The Journal of the Human Factors and Ergonomics Society 60, no. 8 (August 13, 2018): 1219–27. http://dx.doi.org/10.1177/0018720818788142.

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Objective: We sought to investigate whether expert–novice differences in visual search behavior found in other domains also apply to accident scenes and the emergency response domain. Background: Emergency service professionals typically arrive at accidents only after being dispatched when a civilian witness has called an emergency dispatch number. Differences in visual search behavior between the civilian witness (usually a novice in terms of emergency response) and the professional first responders (experts at emergency response) could thus result in the experts being given insufficient or erroneous information, which would lead them to arrive unprepared for the actual situation. Method: A between-subjects, controlled eye-tracking experiment with 20 novices and 17 experts (rescue and ambulance service personnel) was conducted to explore expert–novice differences in visual search of accident and control images. Results: The results showed that the experts spent more time looking at task-relevant areas of the accident images than novices did, as predicted by the information reduction hypothesis. The longer time was due to longer fixation durations rather than a larger fixation count. Conclusion: Expert–novice differences in visual search are present in the emergency domain. Given that this domain is essential to saving lives and also relies heavily on novices as the first link in the chain of response, such differences deserve further exploration. Application: Visual search behavior from experts can be used for training purposes. Eye-tracking studies of novices can be used to inform the design of emergency dispatch interviews.

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Hirt, Julian, Thomas Nordhausen, Christian Appenzeller-Herzog, and Hannah Ewald. "Using citation tracking for systematic literature searching - study protocol for a scoping review of methodological studies and an expert survey." F1000Research 9 (December 1, 2020): 1386. http://dx.doi.org/10.12688/f1000research.27337.1.

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Background: Up-to-date guidance on comprehensive study identification for systematic reviews is crucial. According to current recommendations, systematic searching should combine electronic database searching with supplementary search methods. One such supplementary search method is citation tracking. It aims at collecting directly and/or indirectly cited and citing references from "seed references”. Tailored and evidence-guided recommendations concerning the use of citation tracking are strongly needed. Objective: We intend to develop recommendations for the use of citation tracking in health-related systematic literature searching. Our study will be guided by the following research questions: What are the benefits of citation tracking for health-related systematic literature searching? Which perspectives and experiences do experts in the field of literature retrieval methods have with regard to citation tracking in health-related systematic literature searching? Methods: Our study will have two parts: a scoping review and an expert survey. The scoping review aims at identifying methodological studies on benefits or problems of citation tracking in health-related systematic literature searching with no restrictions on study design, language, and publication date. We will perform database searching in MEDLINE, The Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science Core Collection, two information science databases, and free web searching. Two reviewers will independently assess full texts of selected abstracts. We will conduct direct backward and forward citation tracking on included articles. The results of the scoping review will inform our expert survey through which we aim to learn about experts΄ perspectives and experiences. We will narratively synthesize the results and derive recommendations for performing health-related systematic reviews.

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V’yugin, V. V., I. A. Stel’makh, and V. G. Trunov. "Adaptive Algorithm of Tracking the Best Experts Trajectory." Journal of Communications Technology and Electronics 62, no. 12 (December 2017): 1434–47. http://dx.doi.org/10.1134/s1064226917120117.

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INCERA, SARA, and CONOR T. McLENNAN. "Mouse tracking reveals that bilinguals behave like experts." Bilingualism: Language and Cognition 19, no. 3 (May 12, 2015): 610–20. http://dx.doi.org/10.1017/s1366728915000218.

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We used mouse tracking to compare the performance of bilinguals and monolinguals in a Stroop task. Participants were instructed to respond to the color of the words (e.g., blue in yellow font) by clicking on response options on the screen. We recorded participants’ movements of a computer mouse: when participants started moving (initiation times), and how fast they moved towards the correct response (x-coordinates over time). Interestingly, initiation times were longer for bilinguals than monolinguals. Nevertheless, when comparing mouse trajectories, bilinguals moved faster towards the correct response. Taken together, these results indicate that bilinguals behave qualitatively differently from monolinguals; bilinguals are “experts” at managing conflicting information. Experts across many different domains take longer to initiate a response, but then they outperform novices. These qualitative differences in performance could be at the root of apparently contradictory findings in the bilingual literature.

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White, M., D. Howes, R. Egan, H. Braund, and A. Szulewski. "P128: The novel application of eye-tracking for the cognitive task analysis of expert physician decision-making while leading real-world traumatic resuscitations." CJEM 19, S1 (May 2017): S121. http://dx.doi.org/10.1017/cem.2017.330.

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Introduction: Resuscitation is a dynamic, complex and time-sensitive field which encompasses management of both critically-ill patients as well as large multidisciplinary teams. Expertise in this area has not been adequately defined, and to date, no research has directly examined the decision-making and cognitive processes involved. The evolving paradigm of competency-based medical education (CBME) makes better defining expertise in this field of critical importance to aid in the development of both educational and assessment methods. The technique of cognitive task analysis (CTA) has been used in a variety of fields to explicate the cognitive underpinnings of experts. Experts, however, often have limited insight and incomplete recall of their decision-making processes. We hypothesized that the use of eye-tracking, which provides the combination of first-person video as well as an overlying gaze indicator, could be used to enhance CTA to better understand the defining characteristics of experts in resuscitation. Methods: Over an 18-month period a sample of 11 traumatic resuscitations were obtained, each led by one of four pre-selected expert physicians outfitted with the Tobii Pro Eye-Tracking Glasses. After each resuscitation, the participant was debriefed using a cued-recall, think-aloud protocol while watching his or her corresponding eye-tracking video. A subsequent qualitative analysis of the resulting video and debrief transcript was performed using an ethnographic approach to establish emerging themes and behaviours of the expert physicians. Results: The expert participants demonstrated specific, common patterns in their cognitive processes. In particular, participants exhibited similar anticipatory and visual behaviours, dynamic communication strategies and the ability to distinguish between task-relevant and task-redundant information. All participants reported that this technique uncovered otherwise subconscious aspects of their cognition. Conclusion: The novel combination of eye-tracking technology to supplement the CTA of expert resuscitationists enriched our understanding of expertise in this field and yielded specific findings that can be applied to better develop and assess resuscitation skills.

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Guzik, Aldona, and Anna Stolińska. "Niesłyszalni eksperci? Zarządzanie percepcją wypowiedzi eksperckich w dobie mediów multimodalnych na przykładzie wybranego materiału z serwisu informacyjnego – badania pilotażowe." Zarządzanie Mediami 9, no. 3 (2021): 475–91. http://dx.doi.org/10.4467/23540214zm.21.025.13756.

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In-Audible Experts? Managing the Perception of Expert’s Statements in the Era of Multimodal Media, on the Example of Selected Material From an Information Service ‒ Pilot Study The article discusses the perception studies of experts commenting political events on television. The aim of the study was to determine to what extent the expert hypothesis works in the era of multimodal communication and information noise. In addition, an attempt was made to identify elements of viewers’ attention management, such as: attractors and visual distractors, and to determine the importance of emblems of expertise in qualifying people who speak to this group. Two research techniques were used in the study: eye tracking and surveys, which allowed for comprehensive analysis of multimodal message. Research shows that in the case of such messages, viewers usually use the face-caption-face scheme, with the face being the attractor for both groups – a single person and a crowd. The elements of the background turned out to be distractors, which can be of great importance in the perception of expert statements, since in the media we recently tend to place the expert outside, in a dynamic surrounding. At the same time, the analysis showed that expert emblems, e.g. title or clothing, are an indicator of the speaker’s belonging to this group. Despite of this, expert statements were considered by the respondents to be aggressive, non-substantive and not adding anything to their knowledge of politics, which supports the recognition of Tom Nichols’s thesis about the death of experts and their expertise, and at the same time about the inadequacy of the hypothesis referring to experts in the age of multimodal media.

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Gold, Anne U., Rachel Atkins, and Karen S. McNeal. "Undergraduates Graph Interpretation and Scientific Paper Reading Shift from Novice- to Expert-like as a Result of Participation in a Summer Research Experience: A Case Study." Scholarship and Practice of Undergraduate Research 5, no. 2 (2021): 7–19. http://dx.doi.org/10.18833/spur/5/2/2.

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Research Experiences for Undergraduate (REU) programs often introduce students to scientific research and STEM career possibilities. However, the program impact on students and their research skill development is not well understood. In a case study with 10 REU students, the authors used eye-tracking and self-report data to determine student strategies for reading scientific papers and interpreting graphs at the beginning and end of the program. The strategies of REU students and science experts were then compared. The REU students changed their strategies and performed more like experts at posttest. These findings indicate that, during the REU, students acquired expert-like strategies necessary to engage with scientific articles and extract key information from graphs. The study demonstrates that eye-tracking can document skill growth in REU students.

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Kastens, Kim A., Thomas F. Shipley, Alexander P. Boone, and Frances Straccia. "What Geoscience Experts And Novices Look At, And What They See, When Viewing Data Visualizations." Journal of Astronomy & Earth Sciences Education (JAESE) 3, no. 1 (May 31, 2016): 27. http://dx.doi.org/10.19030/jaese.v3i1.9689.

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This study examines how geoscience experts and novices make meaning from an iconic type of data visualization: shaded relief images of bathymetry and topography. Participants examined, described, and interpreted a global image, two high-resolution seafloor images, and 2 high-resolution continental images, while having their gaze direction eye-tracked and their utterances and gestures videoed. In addition, experts were asked about how they would coach an undergraduate intern on how to interpret this data. Not unexpectedly, all experts were more skillful than any of the novices at describing and explaining what they were seeing. However, the novices showed a wide range of performance. Along the continuum from weakest novice to strongest expert, proficiency developed in the following order: making qualitative observations of salient features, making simple interpretations, making quantitative observations. The eye-tracking analysis examined how the experts and novices invested 20 seconds of unguided exploration, after the image came into view but before the researcher began to ask questions. On the cartographic elements of the images, experts and novices allocated their exploration time differently: experts invested proportionately more fixations on the latitude and longitude axes, while students paid more attention to the color bar. In contrast, within the parts of the image showing the actual geomorphological data, experts and novices on average allocated their attention similarly, attending preferentially to the geologically significant landforms. Combining their spoken responses with their eye-tracking behavior, we conclude that the experts and novices are looking in the same places but “seeing” different things.

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Zhu, Mengyao, Defu Bao, Yuxiang Yu, Danni Shen, and Minzhe Yi. "Differences in thinking flexibility between novices and experts based on eye tracking." PLOS ONE 17, no. 6 (June 30, 2022): e0269363. http://dx.doi.org/10.1371/journal.pone.0269363.

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The influence of thinking flexibility on design is often underestimated by researchers in the field of design education. In this study, morphological analysis was used as a tool to develop design proposals and eye tracking technology was applied to track the attention. The feature of thinking activities in problem-solving between two groups (novice and expert) was analyzed by Heat map and Gaze plot in qualitative, and measured by indicators such as fixation and saccade in quantitative. Findings suggested that, i) Experts showed more fixation duration and fixation numbers in thinking activities, and the attention containing more AOIs was positively related to the rationality of the scheme. ii) Saccades with greater amplitude were more beneficial to the novelty of scheme. iii) Experts considered the information of each block in a balanced way, while novices tended to ignore unimportant blocks. These results will have a far-reaching impact on the development of designers’ thinking and help novices to exercise creative thinking and produce high-quality designs.

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McQuade, Scott, and Claire Monteleoni. "Global Climate Model Tracking Using Geospatial Neighborhoods." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (September 20, 2021): 335–41. http://dx.doi.org/10.1609/aaai.v26i1.8178.

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A key problem in climate science is how to combine the predictions of the multi-model ensemble of global climate models. Recent work in machine learning (Monteleoni et al. 2011) showed the promise of an algorithm for online learning with experts for this task.We extend the Tracking Climate Models (TCM) approach to (1) take into account climate model predictions at higher spatial resolutions and (2) to model geospatial neighborhood influence between regions. Our algorithm enables neighborhood influence by modifying the transition dynamics of the Hidden Markov Model used by TCM, allowing the performance of spatial neighbors to influence the temporal switching probabilities for the best expert (climate model) at a given location. In experiments on historical data at a variety of spatial resolutions, our algorithm demonstrates improvements over TCM, when tracking global temperature anomalies.

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Li, Jiatong, Chenwei Deng, Richard Yi Da Xu, Dacheng Tao, and Baojun Zhao. "Robust Object Tracking With Discrete Graph-Based Multiple Experts." IEEE Transactions on Image Processing 26, no. 6 (June 2017): 2736–50. http://dx.doi.org/10.1109/tip.2017.2686601.

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Gu, S., Z. Ma, M. Xie, and Z. Chen. "Online learning of mixture experts for real‐time tracking." IET Computer Vision 10, no. 6 (April 13, 2016): 585–92. http://dx.doi.org/10.1049/iet-cvi.2015.0210.

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Mukherjee, Maheswari, Amber Donnelly, Blake Rose, David E. Warren, Elizabeth Lyden, Nikolaos Chantziantoniou, Brian Dimmitt, Karyn Varley, and Liron Pantanowitz. "Eye tracking in cytotechnology education: “visualizing” students becoming experts." Journal of the American Society of Cytopathology 9, no. 2 (March 2020): 76–83. http://dx.doi.org/10.1016/j.jasc.2019.07.002.

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Mukherjee, Maheswari, Amber Donnelly, Blake Rose, David Warren, Elizabeth Lyden, Nikolaos Chantziantoniou, Brian Dimmitt, Karyn Varley, and Liron Pantanowitz. "Eye Tracking in Cytotechnology Education: “Visualizing” Students Becoming Experts." Journal of the American Society of Cytopathology 8, no. 5 (September 2019): S66—S67. http://dx.doi.org/10.1016/j.jasc.2019.08.149.

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Zhang, Lei, Hong Zhang, Yawei Li, Qiaochu Kang, and Naixue Xiong. "Robust Visual Tracking via Multiple Experts With Correlation Filters." IEEE Access 7 (2019): 129504–13. http://dx.doi.org/10.1109/access.2019.2930460.

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Moeinirad, Samira, Behrouz Abdoli, Alireza Farsi, and Nasour Ahmadi. "The Role of Quiet Eye Duration and Its Components in a Complex Far-Aiming Task." Journal of Motor Learning and Development 8, no. 3 (December 1, 2020): 516–27. http://dx.doi.org/10.1123/jmld.2019-0048.

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The quiet eye is a characteristic of highly skilled perceptual and motor performance that is considered as the final fixation toward a target before movement initiation. The aim of this study was to extend quiet eye–related knowledge by investigating expertise effects on overall quiet eye duration among expert and near-expert basketball players, as well as to determine the relative contribution of early and late visual information in a basketball jump shot by comparing the timing components of quiet eye duration (early and late quite eye). Twenty-seven expert and near-expert male basketball players performed the jump shots. Gaze was recorded with the SensoMotoric Instruments eye tracking glasses and shooting performance accuracy was evaluated by scoring each shot on a scale of 1–8. Six infrared cameras circularly arranged around the participants were used to collect the kinematic information of the players. The performance accuracy, gaze behavior, and kinematic characteristics of the participants during the test were calculated. The experts with longer quiet eye duration had better performance in a basketball jump shot compared to the near-experts. Also the experts had longer early and late quiet eye duration than the near-experts. The results revealed a relationship between quiet eye duration and performance. The combined visual strategy is a more efficient strategy in complex far-aiming tasks such as a basketball jump shot.

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Xu Zhao, Yun Fu, and Yuncai Liu. "Human Motion Tracking by Temporal-Spatial Local Gaussian Process Experts." IEEE Transactions on Image Processing 20, no. 4 (April 2011): 1141–51. http://dx.doi.org/10.1109/tip.2010.2076820.

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Addakula Lavanya and T.Murali Krishna. "An AI and Cloud Based Collaborative Platform for PlantDisease Identification, Tracking and Forecasting for Farmers." international journal of engineering technology and management sciences 6, no. 6 (November 28, 2022): 527–37. http://dx.doi.org/10.46647/ijetms.2022.v06i06.091.

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Plant diseases are a major threat to farmers, consumers, environment and the global economy. In India alone, 35% of field crops are lost to pathogens and pests causing lossesto farmers. Indiscriminate use of pesticides is also a serious health concern as many are toxic and biomagnified. These adverse effects can be avoided by early disease detection, crop surveillance and targeted treatments. Most diseases are diagnosed by agricultural experts by examining external symptoms. However, farmers have limited access to experts. Our project is the first integrated and collaborative platform for automated disease diagnosis, tracking and forecasting. Farmers can instantly and accurately identify diseases and get solutions with a mobile app by photographing affected plant parts. Real- time diagnosis is enabled using the latest Artificial Intelligence (AI) algorithms for Cloud-based image processing. The AI model continuously learns from user uploaded images and expert suggestions to enhance its accuracy. Farmers can also interact with local expertsthrough the platform. For preventive measures, disease density maps with spread forecasting are rendered from a Cloud based repository of geo-tagged images and micro-climactic factors. A web interface allows experts to perform disease analytics with geographical visualizations. In our experiments, the AI model (CNN) was trained with large disease datasets, created with plant images self-collected from many farms over 7 months. Test images were diagnosed using the automated CNN model and the results were validated by plant pathologists. Over 95% disease identification accuracy was achieved. Our solution is a novel, scalable and accessible tool for disease management of diverse agricultural crop plants and can be deployed as a Cloud based service for farmers and experts for ecologically sustainable crop production.

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Jo, Eunkyung, Seora Park, Hyeonseok Bang, Youngeun Hong, Yeni Kim, Jungwon Choi, Bung Nyun Kim, Daniel A. Epstein, and Hwajung Hong. "GeniAuti: Toward Data-Driven Interventions to Challenging Behaviors of Autistic Children through Caregivers' Tracking." Proceedings of the ACM on Human-Computer Interaction 6, CSCW1 (March 30, 2022): 1–27. http://dx.doi.org/10.1145/3512939.

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Challenging behaviors significantly impact learning and socialization of autistic children and can stress and burden their caregivers. Documentation of challenging behaviors is fundamental for identifying what environmental factors influence them, such as how others respond to a child's such behaviors. Caregiver-tracked data on their child's challenging behaviors can help clinical experts make informed recommendations about how to manage such behaviors. To support caregivers in recording their children's challenging behaviors, we developed GeniAuti, a mobile-based data-collection tool built upon a clinical data collection form to document challenging behaviors and other clinically relevant contextual information such as place, duration, intensity, and what triggers such behaviors. Through an open-ended deployment with 19 parent-child pairs and three expert collaborators, caregivers found GeniAuti valuable for (1) becoming more attentive and reflective to behavioral contexts, including their own response strategies, (2) discovering positive aspects of their children's behaviors, and (3) promoting collaboration with clinical experts around the caregiver-tracked data to develop tailored intervention strategies for their children. However, participant experiences surface challenges of logging behaviors in social circumstances, conflicting views between caregivers and clinical experts around the structured recording process, and emotional struggles resulting from recording and reflecting on intensely negative experiences. Considering the complex nature of caregiver-based health tracking and caregiver--clinician collaboration, we suggest design opportunities for facilitating negotiations between caregivers and clinicians and accounting for caregivers' emotional needs.

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Witkowski, Mateusz, Ewa Tomczak, Łukasz Bojkowski, Zbigniew Borysiuk, and Maciej Tomczak. "Do Expert Fencers Engage the Same Visual Perception Strategies as Beginners?" Journal of Human Kinetics 78, no. 1 (March 1, 2021): 49–58. http://dx.doi.org/10.2478/hukin-2021-0045.

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Abstract An effective visual perception strategy helps a fencer quickly react to an opponent’s actions. This study aimed to examine and compare visual perception strategies used by high-performance foil fencers (experts) and beginners. In an eye tracking experiment, we analysed to which areas beginning and expert fencers paid attention during duels. Novices paid attention to all examined areas of interest comprising the guard, foil (blade and tip), armed hand, lower torso, and upper torso of their opponents. Experts, however, paid significantly less attention to the foil, picking up information from other areas, mainly the upper torso and the armed hand. These results indicate that expert fencers indeed engage different visual perception strategies than beginners. The present findings highlight the fact that beginner fencers should be taught already in the early stages of their careers how to pick up information from various body areas of their opponents.

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Stern, David, Horst Samulowitz, Ralf Herbrich, Thore Graepel, Luca Pulina, and Armando Tacchella. "Collaborative Expert Portfolio Management." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 3, 2010): 179–84. http://dx.doi.org/10.1609/aaai.v24i1.7561.

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We consider the task of assigning experts from a portfolio of specialists in order to solve a set of tasks. We apply a Bayesian model which combines collaborative filtering with a feature-based description of tasks and experts to yield a general framework for managing a portfolio of experts. The model learns an embedding of tasks and problems into a latent space in which affinity is measured by the inner product. The model can be trained incrementally and can track non-stationary data, tracking potentially changing expert and task characteristics. The approach allows us to use a principled decision theoretic framework for expert selection, allowing the user to choose a utility function that best suits their objectives. The model component for taking into account the performance feedback data is pluggable, allowing flexibility. We apply the model to manage a portfolio of algorithms to solve hard combinatorial problems. This is a well studied area and we demonstrate a large improvement on the state of the art in one domain (constraint solving) and in a second domain (combinatorial auctions) created a portfolio that performed significantly better than any single algorithm.

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Hüttermann, S., B. Noël, and D. Memmert. "Eye tracking in high-performance sports: Evaluation of its application in expert athletes." International Journal of Computer Science in Sport 17, no. 2 (December 1, 2018): 182–203. http://dx.doi.org/10.2478/ijcss-2018-0011.

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Abstract In the last thirty years, an increasing interest in sport sciences regarding the analysis of expert athletes’ gaze behavior has become apparent. This narrative review provides an overview of the use of eye tracking systems in high-performance sports from 1987 to 2016. A systematic search of the PubMed, Scopus, SPORTDiscus, and WebofScience databases was conducted. The search was performed using the keywords eye tracking, eye movement, gaze behavior/patterns, and visual search strategies in combination with high-performance sports, elite athletes, high-class athletes, sport experts, and top-athletes. It yielded a total of 86 studies of which almost half were conducted computer-based or in front of a screen. Most studies dealt with the analysis of gaze behavior during dead ball situations while also focusing on differences between expert athletes and novices. More high-quality intervention studies are essential to determine if there are ideal gaze strategies and, if yes, how it is possible to learn/implement these.

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Candello, Heloisa, Claudio Pinhanez, Michael Muller, and Mairieli Wessel. "Unveiling Practices of Customer Service Content Curators of Conversational Agents." Proceedings of the ACM on Human-Computer Interaction 6, CSCW2 (November 7, 2022): 1–33. http://dx.doi.org/10.1145/3555768.

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Conversational interfaces require two types of curation: data curation by data science workers and content curation by domain experts. Recent years have seen the possibilities for content curators to instruct conversational machines in the customer service domain (i.e., Machine Teaching). The activities of curating specialized data are time-consuming. These activities have a learning curve for the domain expert, and they rely on collaborators beyond the domain experts, including product owners, technology expert curators, management, marketing, and communication employees. However, recent research has looked at making this task easier for domain experts with a lack of knowledge in the Machine Learning system, and few papers have investigated the work practices and collaborations involved in this role. This paper aims to fill this gap, presenting and unveiling practices extracted from eleven semi-structured interviews and four design workshops with experts in Banking, Technical support, Humans Resources, Telecommunications, and Automotive sectors. First, we investigate the articulation work of the content curators and tech curators in training conversational machines. Second, we inspect the curatorial and collaboration strategies they use, which are not afforded by current conversational platforms. Third, we draw the design implications and possibilities to support individual and collaboration curating practices. We reflect on how those practices rely on self and collaboration with others for curation, trust, and data tracking and ownership.

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Hwang, Cheng-Neng. "The Integrated Design of Fuzzy Collision-Avoidance and H∞-Autopilots on Ships." Journal of Navigation 55, no. 1 (January 2002): 117–36. http://dx.doi.org/10.1017/s0373463301001631.

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Collision avoidance remains the most important concern for ships at sea. Despite the electronic equipment now fitted on ships to support the mariner, expert experience is still essential when a ship is in danger of colliding with the others. To include these experts' experiences to resolve the problems of collision, we have designed a fuzzy collision-avoidance expert system that includes a knowledge base to store facts and rules, an inference engine to simulate experts' decisions and a fuzzy interface device. Either a quartermaster or an autopilot system can then implement the avoidance action proposed in the research. To perform the task of collision-avoidance effectively, a robust autopilot system using the state space H∞ control methodology has been designed to steer a ship safely for various conditions at sea in performing course keeping, course-changing and route-tracking more robustly. The integration of fuzzy collision-avoidance and H∞autopilot systems is then proposed in this paper.

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Keskin, Merve, Kristien Ooms, Ahmet Ozgur Dogru, and Philippe De Maeyer. "Exploring the Cognitive Load of Expert and Novice Map Users Using EEG and Eye Tracking." ISPRS International Journal of Geo-Information 9, no. 7 (July 8, 2020): 429. http://dx.doi.org/10.3390/ijgi9070429.

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The main objective of this research is to explore the cognitive processes of expert and novice map users during the retrieval of map-related information, within varying difficulty levels (i.e., easy, moderate, hard), by using eye tracking and electroencephalogram (EEG). In this context, we present a spatial memory experiment consisting of a large number of stimuli to study the effect of task difficulty on map users’ behavior through cognitive load measurements. Next to the reaction time and success rate, we used fixation and saccade related eye tracking metrics (i.e., average fixation duration, the number of fixations per second, saccade amplitude and saccade velocity), and EEG power spectrum (i.e., event-related changes in alpha and theta frequency bands) to identify the cognitive load. While fixation metrics indicated no statistically significant difference between experts and novices, saccade metrics proved the otherwise. EEG power spectral density analysis, on the other side, suggested an increase in theta (i.e., event-related synchronization) and a decrease in alpha (except moderate tasks) (i.e., event-related desynchronization) at all difficulty levels of the task for both experts and novices, which is an indicator of cognitive load. Although no significant difference emerged between two groups, we found a significant difference in their overall performances when the participants were classified as good and relatively bad learners. Triangulating EEG results with the recorded eye tracking data and the qualitative analysis of focus maps indeed provided a detailed insight on the differences of the individuals’ cognitive processes during this spatial memory task.

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Zhang, Wan, Ran Yin, Yu Xiao Deng, Jun Yi Zeng, Lu Ding, and Yi Peng Li. "Numerical Movement Tracking of Mitral Valve Annulus." Advanced Materials Research 936 (June 2014): 2267–70. http://dx.doi.org/10.4028/www.scientific.net/amr.936.2267.

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Clinical diagnosis and therapy planning are increasingly often supported by 3D imaging modalities. Visual and quantitative evaluation of the valve is an important step in the clinical workflow according to experts as knowledge about mitral morphology and dynamics is crucial for interventional planning. We consider optimal matching of the annulus curves as sub-manifolds by a variational approach based on diffeomorphic transformations.The performance of the algorithm is illustrated by numerical results for examples from medical image analysis.

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Meyer, Johannes, Frowin Fasold, Karsten Schul, Matthias Sonnenschein, and Stefanie Klatt. "The Defender’s Vision—Gaze Behavior of One-on-One Defenders in Basketball." Journal of Sport & Exercise Psychology 44, no. 2 (April 1, 2022): 127–37. http://dx.doi.org/10.1123/jsep.2021-0149.

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In fast-paced team sports, anticipation is one important element in defense strategies. The primary objective of this study was to examine the recommendation for action and use of defensive gaze strategies by defensive players in basketball. Four national-level expert-basketball coaches were interviewed and a field study with mobile eye-tracking devices was conducted on 16 expert and 16 novice players defending in a one-on-one situation. Differences in relative fixation times between experts and novices were elaborated for the predetermined gaze zones—head, ball, torso, and feet—as given by the expert coaches. This was done for three phases of the movement sequence: receiving, dribbling, and shooting. The results of the interviews with expert coaches indicated that the existing coaching doctrine instructs players to look at the torso of an opponent to avoid being vulnerable to fakes. Surprisingly, our findings with the players showed a discrepancy in the evaluated gaze behavior of the experts and novices. For the receiving and dribbling phase, experts mainly fixated their gaze on the head while novices focused on the ball. For the final shooting phase, both the groups mainly fixated their gaze on the ball. Fixating the gaze on the ball or head makes the player potentially vulnerable to deceptive movements, as video-based research has shown. Expert coaches also indicated that peripheral vision is of importance to defenders, contradicting the existing assumption in the literature that focusing on the task-relevant areas is key for anticipation performance.

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Imron, Imron, Miftah Nur Afidah, M. Sinta Nurhayati, Sulistiyah Sulistiyah, and Fatmawati Fatmawati. "Sistem Pakar Diagnosa Kerusakan Mesin Sepeda Motor Transmission Automatic dengan Metode Forward Chaining Studi Kasus: AHASS 00955 Mitra Perdana." Jurnal Ilmiah Universitas Batanghari Jambi 19, no. 3 (October 15, 2019): 544. http://dx.doi.org/10.33087/jiubj.v19i3.742.

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Implementation of Expert System Diagnosis of motorcycle engine damage is a solution of motorcycle service at AHASS 00955 Mitra Perdana, Expert System is a system that is related to the knowledge possessed by experts and technology users to solve a problem. This article, discusses the design of decision-making applications that have an important role in the process of diagnosing motor engine damage. Because the system is currently running the user only hands over the motorcycle to the technician. The use of computer-based artificial intelligence can be a solution to problems in structured decision making. The design of this expert system is designed using the forward tracking inference method and is designed in the form of a website to facilitate users in using it.

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Suman, Abdulla Al, Carlo Russo, Ann Carrigan, Patrick Nalepka, Benoit Liquet-Weiland, Robert Ahadizad Newport, Poonam Kumari, and Antonio Di Ieva. "Spatial and time domain analysis of eye-tracking data during screening of brain magnetic resonance images." PLOS ONE 16, no. 12 (December 2, 2021): e0260717. http://dx.doi.org/10.1371/journal.pone.0260717.

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Introduction Eye-tracking research has been widely used in radiology applications. Prior studies exclusively analysed either temporal or spatial eye-tracking features, both of which alone do not completely characterise the spatiotemporal dynamics of radiologists’ gaze features. Purpose Our research aims to quantify human visual search dynamics in both domains during brain stimuli screening to explore the relationship between reader characteristics and stimuli complexity. The methodology can be used to discover strategies to aid trainee radiologists in identifying pathology, and to select regions of interest for machine vision applications. Method The study was performed using eye-tracking data 5 seconds in duration from 57 readers (15 Brain-experts, 11 Other-experts, 5 Registrars and 26 Naïves) for 40 neuroradiological images as stimuli (i.e., 20 normal and 20 pathological brain MRIs). The visual scanning patterns were analysed by calculating the fractal dimension (FD) and Hurst exponent (HE) using re-scaled range (R/S) and detrended fluctuation analysis (DFA) methods. The FD was used to measure the spatial geometrical complexity of the gaze patterns, and the HE analysis was used to measure participants’ focusing skill. The focusing skill is referred to persistence/anti-persistence of the participants’ gaze on the stimulus over time. Pathological and normal stimuli were analysed separately both at the “First Second” and full “Five Seconds” viewing duration. Results All experts were more focused and a had higher visual search complexity compared to Registrars and Naïves. This was seen in both the pathological and normal stimuli in the first and five second analyses. The Brain-experts subgroup was shown to achieve better focusing skill than Other-experts due to their domain specific expertise. Indeed, the FDs found when viewing pathological stimuli were higher than those in normal ones. Viewing normal stimuli resulted in an increase of FD found in five second data, unlike pathological stimuli, which did not change. In contrast to the FDs, the scanpath HEs of pathological and normal stimuli were similar. However, participants’ gaze was more focused for “Five Seconds” than “First Second” data. Conclusions The HE analysis of the scanpaths belonging to all experts showed that they have greater focus than Registrars and Naïves. This may be related to their higher visual search complexity than non-experts due to their training and expertise.

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Ismail, Nurmaisarah, Sazilah Salam, Siti Nurul Mahfuzah Mohamad, Bambang Pudjoatmodjo, Norazlina Shafie, Rashidah Lip, Mohd Adili Norasikin, and Faaizah Shahbodin. "Mobile game model for monitoring Malaysian food calories intake using image recognition." Bulletin of Electrical Engineering and Informatics 12, no. 3 (June 1, 2023): 1839–48. http://dx.doi.org/10.11591/eei.v12i3.4916.

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Two important problems related to food consumption were reported in Malaysia: Malaysia was the sixth rank in Asia for the highest adult obesity rate; and the United Nation reported that Malaysian consumed an average of 2,910 calories per day. An imbalanced diet and high intake of calorie-dense food problems that need attention to reduce obesity. These problems affect national economies by lowering productivity, increasing disability, raising health care expenses, and shortening life spans. Although, there are food calorie tracking applications available, however, existing apps are less engaging and to recognize Malaysian food due to its not versatile databases. This can be solved using game technologies. Hence, this study will propose mobile game model as a solution to the underlying problems. There are 4 phases in the method: expert validation, initial model, expert verification, and final model. The proposed parameters were validated by dietitians, and nutritionists. The model was verified by game experts. A low fidelity prototype was developed based on the proposed model to assist the expert verification process. The model was finalized based on the expert’s feedback. The proposed game model resolves the limited recognition of Malaysian food and monitoring the food calories intake in an engaging way.

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Lee, W., and J. Chenkin. "LO82: Exploring eye-tracking technology to assess competency in point-of-care ultrasound." CJEM 22, S1 (May 2020): S37. http://dx.doi.org/10.1017/cem.2020.136.

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Introduction: Assessment of point-of-care ultrasound (POCUS) competency has been reliant on practical, visual and written examinations performed 1-on-1 with an examiner. These tools attempt to assess competency through subjective ratings, checklists and multiple-choice questions that are labour intensive using surrogate measures. Eye-tracking has been used on a limited basis in various fields of medicine for training and assessment. This technology explores visual processing and holds great promise as a tool to monitor training progress towards the development of expertise. We hypothesize that eye-tracking may differentiate novices and experts as they progress to become competent in interpretation of POCUS images and provide an objective measure in assessment of competency. Methods: Medical students, residents and attending physicians working in an academic emergency department were recruited. Participants viewed a series of 16 ultrasound video clips in a POCUS protocol for Focused Assessment using Sonography in Trauma (FAST). The gaze pattern of the participants was recorded using a commercially available eye-tracking device. The primary outcome was the gaze parameters including total gaze time in the area of interest (AOI), average time to fixation on the AOI, number of fixations in the AOI and average duration of first fixation on the AOI. Secondary outcome was the accuracy on the interpretation of the FAST scan. Results: Four novices and eight experts completed this study. The total gaze time in the AOI (mean +/- SD) was 76.72 +/- 18.84s among experts vs 53.64 +/- 10.33s among novices (p = 0.048), average time to fixation on the AOI was 0.561 +/- 0.319s vs 1.048 +/- 0.280s (p = 0.027), number of fixations in the AOI was 158.9 +/- 29.0 vs 121.8 +/- 17.5 (p = 0.042) and average duration of first fixation was 0.444 +/- 0.119s vs 0.390 +/- 0.024s (p = 0.402). The accuracy of the answers was 79.7 +/- 14.1% vs 45.3 +/- 21.9% (p = 0.007). Conclusion: In this pilot study, eye tracking shows potential to differentiate between POCUS experts and novices by their gaze patterns. Gaze patterns captured by eye tracking may not necessarily translate to cognitive processing. However, it allows educators to visualise the thought processes of the learner by their gaze patterns and provide insight on how to guide them towards competency. Future studies are needed to further validate the metrics for competency in POCUS applications.

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Siregar, Riki. "SISTEM PAKAR ANALISA KERUSAKAN PADA SEPEDA MOTOR HONDA BEAT INJECTION DENGAN METODE BACKWARD CHAINING." PETIR 11, no. 1 (November 2, 2018): 1–8. http://dx.doi.org/10.33322/petir.v11i1.1.

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Expert system is one part of artificial intelligence designed to mimic the expertise of an expert in answering questions and completing a problem. This application made to resolve damages acquired from the symptoms which mostly encountered by motorcyclist. This application created by backward chaining using backward tracking method that works based on conclusions from series of happenings. In this study, collecting the data gained from the experts involved in the existing system by direct interview, direct observation and also from the literatures as references to support the explanation of the elements studied. The objective of this application is to help the mechanics or technicians in analyzing damages occured in Honda Beat Injection motorcycle at the garage in Honda Motor Festival faster.

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Wu, Chia-Chien, and Jeremy M. Wolfe. "Eye Movements in Medical Image Perception: A Selective Review of Past, Present and Future." Vision 3, no. 2 (June 20, 2019): 32. http://dx.doi.org/10.3390/vision3020032.

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The eye movements of experts, reading medical images, have been studied for many years. Unlike topics such as face perception, medical image perception research needs to cope with substantial, qualitative changes in the stimuli under study due to dramatic advances in medical imaging technology. For example, little is known about how radiologists search through 3D volumes of image data because they simply did not exist when earlier eye tracking studies were performed. Moreover, improvements in the affordability and portability of modern eye trackers make other, new studies practical. Here, we review some uses of eye movements in the study of medical image perception with an emphasis on newer work. We ask how basic research on scene perception relates to studies of medical ‘scenes’ and we discuss how tracking experts’ eyes may provide useful insights for medical education and screening efficiency.

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Li, Liping. "Migration Path Tracking Algorithm of Egret Birds Based on Intelligent Remote Sensing Monitoring." Wireless Communications and Mobile Computing 2022 (August 30, 2022): 1–10. http://dx.doi.org/10.1155/2022/4320297.

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There are billions of migratory birds migrating between breeding grounds and wintering grounds in the world every year. Egret is no exception. During the migration period, they need to travel for a long time and long distances. At present, the protection strategies of egrets and the dynamic mechanism of migration routes are concerned by experts in related fields. Researchers generally explore the above problems by tracking the migration paths of egrets. Most of the existing path tracking algorithms use some classical tracking algorithms, but the tracking accuracy of the classical algorithms is low. Therefore, this paper has applied the intelligent remote sensing monitoring technology to the improvement of the path tracking algorithm, used artificial intelligence technology to remove the redundant information of the image, and combined the Kalman filter with the single-target long-term algorithm to improve the tracking algorithm. 100 samples were simulated, and the experimental results showed that the tracking accuracy of the egret bird migration path tracking algorithm based on intelligent remote sensing monitoring was improved by 8.37% compared with the algorithm before improvement, which has better utilization value.

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Jones, Barbara, I. Ching Tsai, Yu-Mei Chang, and Jeffrey Bewley. "Weighting the relative importance of behaviors affecting gait score." Journal of Dairy Research 89, no. 1 (February 2022): 57–59. http://dx.doi.org/10.1017/s0022029922000206.

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AbstractThis research communication evaluates experts’ opinions on the importance and weights of six gait aspects. In 2016, a Qualtrics (Qualtrics LLC., Provo, Utah) survey was distributed to lameness experts. Six gait aspects – general symmetry, tracking, spine curvature, head bobbing, speed and abduction as well as adduction were included. Respondents were asked to rank the gait aspects from 1 (most important) to 6 (least important), and to indicate which weight each gait aspect should receive when assessing lameness. For each gait aspect, frequency (percentage %) was used to describe the distribution of rank, and medians as well as 25th and 75th percentiles were used to summarize assigned weights. Thirty-nine percent of respondents ranked general symmetry first, followed by 32% for tracking, and 19% ranked spine curvature third. Head bobbing ranked fourth with 10% whereas, speed, abduction and adduction were not ranked. The median, 25th and 75th percentiles weight for each gait aspect were: general symmetry (25, 15, and 30), tracking (20, 10, and 30), spine curvature (20, 10, and 21), head bobbing (15, 10, and 20), speed (10, 5, and 20), and abduction and adduction (10, 5, and 10). General symmetry and tracking were deemed the most important gait aspects. A composite gait score can be calculated based on weighted importance of different gait aspects to indicate possible lameness.

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Yao, Yao, Ihor Smal, Ilya Grigoriev, Anna Akhmanova, and Erik Meijering. "Deep-learning method for data association in particle tracking." Bioinformatics 36, no. 19 (July 6, 2020): 4935–41. http://dx.doi.org/10.1093/bioinformatics/btaa597.

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Abstract Motivation Biological studies of dynamic processes in living cells often require accurate particle tracking as a first step toward quantitative analysis. Although many particle tracking methods have been developed for this purpose, they are typically based on prior assumptions about the particle dynamics, and/or they involve careful tuning of various algorithm parameters by the user for each application. This may make existing methods difficult to apply by non-expert users and to a broader range of tracking problems. Recent advances in deep-learning techniques hold great promise in eliminating these disadvantages, as they can learn how to optimally track particles from example data. Results Here, we present a deep-learning-based method for the data association stage of particle tracking. The proposed method uses convolutional neural networks and long short-term memory networks to extract relevant dynamics features and predict the motion of a particle and the cost of linking detected particles from one time point to the next. Comprehensive evaluations on datasets from the particle tracking challenge demonstrate the competitiveness of the proposed deep-learning method compared to the state of the art. Additional tests on real-time-lapse fluorescence microscopy images of various types of intracellular particles show the method performs comparably with human experts. Availability and implementation The software code implementing the proposed method as well as a description of how to obtain the test data used in the presented experiments will be available for non-commercial purposes from https://github.com/yoyohoho0221/pt_linking. Supplementary information Supplementary data are available at Bioinformatics online.

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Dogusoy-Taylan, Berrin, and Kursat Cagiltay. "Cognitive analysis of experts’ and novices’ concept mapping processes: An eye tracking study." Computers in Human Behavior 36 (July 2014): 82–93. http://dx.doi.org/10.1016/j.chb.2014.03.036.

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McNaughten, Ben, Caroline Hart, Stephen Gallagher, Carol Junk, Patricia Coulter, Andrew Thompson, and Thomas Bourke. "Clinicians’ gaze behaviour in simulated paediatric emergencies." Archives of Disease in Childhood 103, no. 12 (March 7, 2018): 1146–49. http://dx.doi.org/10.1136/archdischild-2017-314119.

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AimDifferences in the gaze behaviour of experts and novices are described in aviation and surgery. This study sought to describe the gaze behaviour of clinicians from different training backgrounds during a simulated paediatric emergency.MethodsClinicians from four clinical areas undertook a simulated emergency. Participants wore SMI (SensoMotoric Instruments) eye tracking glasses. We measured the fixation count and dwell time on predefined areas of interest and the time taken to key clinical interventions.ResultsPaediatric intensive care unit (PICU) consultants performed best and focused longer on the chest and airway. Paediatric consultants and trainees spent longer looking at the defibrillator and algorithm (51 180 ms and 50 551 ms, respectively) than the PICU and paediatric emergency medicine consultants.ConclusionsThis study is the first to describe differences in the gaze behaviour between experts and novices in a resuscitation. They mirror those described in aviation and surgery. Further research is needed to evaluate the potential use of eye tracking as an educational tool.

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Uemura, Munenori, Morimasa Tomikawa, Tiejun Miao, Ryota Souzaki, Satoshi Ieiri, Tomohiko Akahoshi, Alan K. Lefor, and Makoto Hashizume. "Feasibility of an AI-Based Measure of the Hand Motions of Expert and Novice Surgeons." Computational and Mathematical Methods in Medicine 2018 (2018): 1–6. http://dx.doi.org/10.1155/2018/9873273.

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This study investigated whether parameters derived from hand motions of expert and novice surgeons accurately and objectively reflect laparoscopic surgical skill levels using an artificial intelligence system consisting of a three-layer chaos neural network. Sixty-seven surgeons (23 experts and 44 novices) performed a laparoscopic skill assessment task while their hand motions were recorded using a magnetic tracking sensor. Eight parameters evaluated as measures of skill in a previous study were used as inputs to the neural network. Optimization of the neural network was achieved after seven trials with a training dataset of 38 surgeons, with a correct judgment ratio of 0.99. The neural network that prospectively worked with the remaining 29 surgeons had a correct judgment rate of 79% for distinguishing between expert and novice surgeons. In conclusion, our artificial intelligence system distinguished between expert and novice surgeons among surgeons with unknown skill levels.

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Keskin, Merve, Vassilios Krassanakis, and Arzu Çöltekin. "Visual Attention and Recognition Differences Based on Expertise in a Map Reading and Memorability Study." ISPRS International Journal of Geo-Information 12, no. 1 (January 12, 2023): 21. http://dx.doi.org/10.3390/ijgi12010021.

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This study investigates how expert and novice map users’ attention is influenced by the map design characteristics of 2D web maps by building and sharing a framework to analyze large volumes of eye tracking data. Our goal is to respond to the following research questions: (i) which map landmarks are easily remembered? (memorability), (ii) how are task difficulty and recognition performance associated? (task difficulty), and (iii) how do experts and novices differ in terms of recognition performance? (expertise). In this context, we developed an automated area-of-interest (AOI) analysis framework to evaluate participants’ fixation durations, and to assess the influence of linear and polygonal map features on spatial memory. Our results demonstrate task-relevant attention patterns by all participants, and better selective attention allocation by experts. However, overall, we observe that task type and map feature type mattered more than expertise when remembering the map content. Predominantly polygonal map features such as hydrographic areas and road junctions serve as attentive features in terms of map reading and memorability. We make our dataset entitled CartoGAZE publicly available.

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Bonnevie, Erika, Jennifer Sittig, and Joe Smyser. "The case for tracking misinformation the way we track disease." Big Data & Society 8, no. 1 (January 2021): 205395172110138. http://dx.doi.org/10.1177/20539517211013867.

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While public health organizations can detect disease spread, few can monitor and respond to real-time misinformation. Misinformation risks the public’s health, the credibility of institutions, and the safety of experts and front-line workers. Big Data, and specifically publicly available media data, can play a significant role in understanding and responding to misinformation. The Public Good Projects uses supervised machine learning to aggregate and code millions of conversations relating to vaccines and the COVID-19 pandemic broadly, in real-time. Public health researchers supervise this process daily, and provide insights to practitioners across a range of disciplines. Through this work, we have gleaned three lessons to address misinformation. (1) Sources of vaccine misinformation are known; there is a need to operationalize learnings and engage the pro-vaccination majority in debunking vaccine-related misinformation. (2) Existing systems can identify and track threats against health experts and institutions, which have been subject to unprecedented harassment. This supports their safety and helps prevent the further erosion of trust in public institutions. (3) Responses to misinformation should draw from cross-sector crisis management best practices and address coordination gaps. Real-time monitoring and addressing misinformation should be a core function of public health, and public health should be a core use case for data scientists developing monitoring tools. The tools to accomplish these tasks are available; it remains up to us to prioritize them.

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Grau, Lauretta E., Kaitlyn Larkin, Chitra Lalloo, Jennifer N. Stinson, William T. Zempsky, Samuel A. Ball, and Frank D. Buono. "Perspectives on adapting a mobile application for pain self-management in neurofibromatosis type 1: results of online focus group discussions with individuals living with neurofibromatosis type 1 and pain management experts." BMJ Open 12, no. 7 (July 2022): e056692. http://dx.doi.org/10.1136/bmjopen-2021-056692.

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ObjectiveNeurofibromatosis type 1 (NF1) is a genetic disorder in which chronic pain commonly occurs. The study sought to understand the needs of individuals with NF1 and pain management experts when adapting a pain self-management mobile health application (app) for individuals with NF1.DesignWe conducted a series of online, audio-recorded focus groups that were then thematically analysed.SettingOnline focus groups with adults currently residing in the USA.ParticipantsTwo types of participants were included: individuals with NF1 (n=32 across six focus groups) and pain management experts (n=10 across three focus groups).ResultsSix themes across two levels were identified. The individual level included lifestyle, reasons for using the mobile app and concerns regarding its use. The app level included desired content, desired features and format considerations. Findings included recommendations to grant free access to the app and include a community support feature for individuals to relate and validate one another’s experience with pain from NF1. In addition, participants noted the importance of providing clear instructions on navigating the app, the use of an upbeat, hopeful tone and appropriate visuals.ConclusionsBoth participant groups endorsed the use of iCanCope (iCC) as an NF1 pain self-management mobile app. Differences between groups were noted, however. The NF1 group appeared interested in detailed and nuanced pain tracking capabilities; the expert group prioritised tracking information such as mood, nutrition and activity to identify potential associations with pain. In tailoring the existing iCC app for individuals with NF1, attention should be paid to creating a community support group feature and to tailoring content, features and format to potential users’ specific needs.

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Li, Siyu, Yongqing Jiang, Chao Sun, Kangkang Guo, and Xin Wang. "An Investigation on the Influence of Operation Experience on Virtual Hazard Perception Using Wearable Eye Tracking Technology." Sensors 22, no. 14 (July 7, 2022): 5115. http://dx.doi.org/10.3390/s22145115.

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Poor electrical hazard recognition is a widespread issue in the production industry. Hazard perception has impacts workers’ hazard recognition, causing them to experience unanticipated hazard exposure and suffer catastrophic injuries. To improve the factors of affecting hazard perception, the current study examined hazard recognition as an everyday visual search task. A comparative test was carried out combining the advantages and disadvantages of the two test methods. It was confirmed that the virtual image test data can replace the real image test data and demonstrate superior flexible settings performance, so the virtual image test method is used. A hazard perception test method based on wearable eye tracking technology was proposed to analyze the eye-tracking data (i.e., fixation, count, search duration, mean fixation duration, eye tracking, and hazard recognition performance feedback) were compared between experts in the field of electrical safety: skilled workers with at least five years of work experience and workers who had been on the job for less than a year. It was found that experts had a better hazard recognition accuracy and missed detection rate than other workers. Experts’ hazards research track was more concised and paid less attention time. This advantage is most obvious in complex risk environments. The findings also suggest that workers who have different working years was not obvious visual search patterns other than the search duration. As can be seen the work experience is not an absolute factor in improving hazard perception. The present research will be useful to understand the influence of working years on hazard perception and provide a theoretical basis for corresponding training.

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Faiella, Filomena, Emiliana Mannese, Giulia Savarese, Antonina Plutino, and Maria Grazia Lombardi. "Eye-tracking glasses for improving teacher education: the e-Teach project." Research on Education and Media 11, no. 1 (June 1, 2019): 85–92. http://dx.doi.org/10.2478/rem-2019-0012.

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Abstract This paper is about “Improvement of teaching techniques by eye tracking in technology enhanced classrooms” (e-Teach), an innovative project funded by the Erasmus Plus Programme (KA2 - Strategic Partnership in the field of School Education). The project aims to study teachers’ eye movements in real teaching situation using eye-tracking glasses and compares the teachers’ use of digital technologies between novices and experts teaching the same school subject. The purpose of this study was to provide indicators of skill gaps between novices and experts which can be addressed appropriately with highly targeted teacher education. The first part of the paper reviews recent developments in conceptual frameworks for digital competence and in digital competence descriptors. The second part describes the project status, the methods and its phases. In conclusion, the paper gives a brief overview of initial findings of ongoing research, focusing largely on the Italian experience, and development tasks for the next project phases. The initial findings suggest that teachers valued the benefits of using digital technologies in classrooms and recognized the necessity of professional development. They also provided specific insights for the purpose of developing an online course for teacher education in four languages: English, Turkish, Italian and Lithuanian.

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Tian, Wei, Long Chen, Ke Zou, and Martin Lauer. "Vehicle Tracking at Nighttime by Kernelized Experts With Channel-Wise and Temporal Reliability Estimation." IEEE Transactions on Intelligent Transportation Systems 19, no. 10 (October 2018): 3159–69. http://dx.doi.org/10.1109/tits.2017.2771410.

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Keskin, Merve, Kristien Ooms, Ahmet Dogru, and Philippe De Maeyer. "EEG & Eye Tracking User Experiments for Spatial Memory Task on Maps." ISPRS International Journal of Geo-Information 8, no. 12 (November 30, 2019): 546. http://dx.doi.org/10.3390/ijgi8120546.

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The aim of this research is to evaluate the use of ET and EEG for studying the cognitive processes of expert and novice map users and to explore these processes by comparing two types of spatial memory experiments through cognitive load measurements. The first experiment consisted of single trials and participants were instructed to study a map stimulus without any time constraints in order to draw a sketch map afterwards. According to the ET metrics (i.e., average fixation duration and the number of fixations per second), no statistically significant differences emerged between experts and novices. A similar result was also obtained with EEG Frontal Alpha Asymmetry calculations. On the contrary, in terms of alpha power across all electrodes, novices exhibited significantly lower alpha power, indicating a higher cognitive load. In the second experiment, a larger number of stimuli were used to study the effect of task difficulty. The same ET metrics used in the first experiment indicated that the difference between these user groups was not statistically significant. The cognitive load was also extracted using EEG event-related spectral power changes at alpha and theta frequency bands. Preliminary data exploration mostly suggested an increase in theta power and a decrease in alpha power.

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Nakamura, Jun, and Sanetake Nagayoshi. "Exploring Knowledge Engineering in Cognitive Skills Transfer for Small and Medium-Sized Companies Using Eye Tracking." International Journal of Systems and Service-Oriented Engineering 12, no. 1 (January 2022): 1–16. http://dx.doi.org/10.4018/ijssoe.297138.

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The authors are interested in knowledge engineering for small and medium-sized enterprises (SMEs) suffering from the difficult post-COVID19 business environment. We conducted fixed-point observation using eye tracking in a production site, aiming to improve the work efficiency of industrial production. Results showed that the experts not only paid attention to a large number of points but also spent considerable attention time, moving quickly among points of attention within the constant overall work time. The number of attention points and the attention time were measured over time. The results showed a learning effect, but experts paid more attention to the critical task. Based on these findings, knowledge engineering is discussed in terms of skill transfer.

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Jang, Hyeju, Emily Rempel, David Roth, Giuseppe Carenini, and Naveed Zafar Janjua. "Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis." Journal of Medical Internet Research 23, no. 2 (February 10, 2021): e25431. http://dx.doi.org/10.2196/25431.

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Background Social media is a rich source where we can learn about people’s reactions to social issues. As COVID-19 has impacted people’s lives, it is essential to capture how people react to public health interventions and understand their concerns. Objective We aim to investigate people’s reactions and concerns about COVID-19 in North America, especially in Canada. Methods We analyzed COVID-19–related tweets using topic modeling and aspect-based sentiment analysis (ABSA), and interpreted the results with public health experts. To generate insights on the effectiveness of specific public health interventions for COVID-19, we compared timelines of topics discussed with the timing of implementation of interventions, synergistically including information on people’s sentiment about COVID-19–related aspects in our analysis. In addition, to further investigate anti-Asian racism, we compared timelines of sentiments for Asians and Canadians. Results Topic modeling identified 20 topics, and public health experts provided interpretations of the topics based on top-ranked words and representative tweets for each topic. The interpretation and timeline analysis showed that the discovered topics and their trend are highly related to public health promotions and interventions such as physical distancing, border restrictions, handwashing, staying home, and face coverings. After training the data using ABSA with human-in-the-loop, we obtained 545 aspect terms (eg, “vaccines,” “economy,” and “masks”) and 60 opinion terms such as “infectious” (negative) and “professional” (positive), which were used for inference of sentiments of 20 key aspects selected by public health experts. The results showed negative sentiments related to the overall outbreak, misinformation and Asians, and positive sentiments related to physical distancing. Conclusions Analyses using natural language processing techniques with domain expert involvement can produce useful information for public health. This study is the first to analyze COVID-19–related tweets in Canada in comparison with tweets in the United States by using topic modeling and human-in-the-loop domain-specific ABSA. This kind of information could help public health agencies to understand public concerns as well as what public health messages are resonating in our populations who use Twitter, which can be helpful for public health agencies when designing a policy for new interventions.

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Journal articles on the topic 'Tracking experts'

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