The Experts below are selected from a list of 24222 Experts worldwide ranked by ideXlab platform
Artetxe E Gonzalez - One of the best experts on this subject based on the ideXlab platform.
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user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities
arXiv: Human-Computer Interaction, 2017Co-Authors: Sergii Stirenko, T. Shemsedinov, Yu. Kochura, N. Gordienko, A. Rojbi, Oleg Alienin, Yuri Gordienko, J Lopez R Benito, Artetxe E GonzalezAbstract:The analysis of the current integration attempts of some modes and use cases of user-machine interaction is presented. The new concept of the user-driven intelligent interface is proposed on the basis of multimodal augmented reality and brain-computer interaction for various applications: in disabilities studies, education, home care, health care, etc. The several use cases of multimodal augmentation are presented. The perspectives of the better human comprehension by the Immediate Feedback through neurophysical channels by means of brain-computer interaction are outlined. It is shown that brain-computer interface (BCI) technology provides new strategies to overcome limits of the currently available user interfaces, especially for people with functional disabilities. The results of the previous studies of the low end consumer and open-source BCI-devices allow us to conclude that combination of machine learning (ML), multimodal interactions (visual, sound, tactile) with BCI will profit from the Immediate Feedback from the actual neurophysical reactions classified by ML methods. In general, BCI in combination with other modes of AR interaction can deliver much more information than these types of interaction themselves. Even in the current state the combined AR-BCI interfaces could provide the highly adaptable and personal services, especially for people with functional disabilities.
Sergii Stirenko - One of the best experts on this subject based on the ideXlab platform.
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User-Driven Intelligent Interface on the Basis of Multimodal Augmented Reality and Brain-Computer Interaction for People with Functional Disabilities
Advances in Information and Communication Networks, 2019Co-Authors: Peng Gang, Yu. Gordienko, T. Shemsedinov, Yu. Kochura, N. Gordienko, A. Rojbi, Oleg Alienin, Sergii Stirenko, Jiang Hui, J. R. López BenitoAbstract:The analysis of the current integration attempts of some modes and use cases of human-to-machine interaction is presented. The new concept of the user-driven intelligent interface for accessibility is proposed on the basis of multimodal augmented reality and brain-computer interaction for various applications: in disabilities studies, education, home care, health care, eHealth, etc. The several use cases of multimodal augmentation are presented. The perspectives of the better human comprehension by the Immediate Feedback through neurophysical channels by means of brain-computer interaction are outlined. It is shown that brain–computer interface (BCI) technology provides new strategies to overcome limits of the currently available user interfaces, especially for people with functional disabilities. The results of the previous studies of the low end consumer and open-source BCI-devices allow us to conclude that combination of machine learning (ML), multimodal interactions (visual, sound, tactile) with BCI will profit from the Immediate Feedback from the actual neurophysical reactions classified by ML methods. In general, BCI in combination with other modes of AR interaction can deliver much more information than these types of interaction themselves. Even in the current state the combined AR-BCI interfaces could provide the highly adaptable and personal services, especially for people with functional disabilities.
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user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities
arXiv: Human-Computer Interaction, 2017Co-Authors: Sergii Stirenko, T. Shemsedinov, Yu. Kochura, N. Gordienko, A. Rojbi, Oleg Alienin, Yuri Gordienko, J Lopez R Benito, Artetxe E GonzalezAbstract:The analysis of the current integration attempts of some modes and use cases of user-machine interaction is presented. The new concept of the user-driven intelligent interface is proposed on the basis of multimodal augmented reality and brain-computer interaction for various applications: in disabilities studies, education, home care, health care, etc. The several use cases of multimodal augmentation are presented. The perspectives of the better human comprehension by the Immediate Feedback through neurophysical channels by means of brain-computer interaction are outlined. It is shown that brain-computer interface (BCI) technology provides new strategies to overcome limits of the currently available user interfaces, especially for people with functional disabilities. The results of the previous studies of the low end consumer and open-source BCI-devices allow us to conclude that combination of machine learning (ML), multimodal interactions (visual, sound, tactile) with BCI will profit from the Immediate Feedback from the actual neurophysical reactions classified by ML methods. In general, BCI in combination with other modes of AR interaction can deliver much more information than these types of interaction themselves. Even in the current state the combined AR-BCI interfaces could provide the highly adaptable and personal services, especially for people with functional disabilities.
Rebecca S. Crowley - One of the best experts on this subject based on the ideXlab platform.
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Metacognitive scaffolds improve self-judgments of accuracy in a medical intelligent tutoring system
Instructional Science, 2014Co-Authors: Reza Feyzi-behnagh, Roger Azevedo, Elizabeth Legowski, Eugene Tseytlin, Kayse Reitmeyer, Rebecca S. CrowleyAbstract:In this study, we examined the effect of two metacognitive scaffolds on the accuracy of confidence judgments made while diagnosing dermatopathology slides in SlideTutor. Thirty-one ( N = 31) first- to fourth-year pathology and dermatology residents were randomly assigned to one of the two scaffolding conditions. The cases used in this study were selected from the domain of nodular and diffuse dermatitides. Both groups worked with a version of SlideTutor that provided Immediate Feedback on their actions for 2 h before proceeding to solve cases in either the Considering Alternatives or Playback condition. No Immediate Feedback was provided on actions performed by participants in the scaffolding mode. Measurements included learning gains (pre-test and post-test), as well as metacognitive performance, including Goodman–Kruskal Gamma correlation, bias, and discrimination. Results showed that participants in both conditions improved significantly in terms of their diagnostic scores from pre-test to post-test. More importantly, participants in the Considering Alternatives condition outperformed those in the Playback condition in the accuracy of their confidence judgments and the discrimination of the correctness of their assertions while solving cases. The results suggested that presenting participants with their diagnostic decision paths and highlighting correct and incorrect paths helps them to become more metacognitively accurate in their confidence judgments.
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Factors affecting feeling-of-knowing in a medical intelligent tutoring system: the role of Immediate Feedback as a metacognitive scaffold
Advances in Health Sciences Education, 2010Co-Authors: Gilan M. El Saadawi, Roger Azevedo, Melissa Castine, Velma Payne, Drazen Jukic, Elizabeth Legowski, Olga Medvedeva, Eugene Tseytlin, Rebecca S. CrowleyAbstract:Previous studies in our laboratory have shown the benefits of Immediate Feedback on cognitive performance for pathology residents using an intelligent tutoring system (ITS) in pathology. In this study, we examined the effect of Immediate Feedback on metacognitive performance, and investigated whether other metacognitive scaffolds will support metacognitive gains when Immediate Feedback is faded. Twenty-three participants were randomized into intervention and control groups. For both groups, periods working with the ITS under varying conditions were alternated with independent computer-based assessments. On day 1, a within-subjects design was used to evaluate the effect of Immediate Feedback on cognitive and metacognitive performance. On day 2, a between-subjects design was used to compare the use of other metacognitive scaffolds (intervention group) against no metacognitive scaffolds (control group) on cognitive and metacognitive performance, as Immediate Feedback was faded. Measurements included learning gains (a measure of cognitive performance), as well as several measures of metacognitive performance, including Goodman–Kruskal gamma correlation ( G ), bias, and discrimination. For the intervention group, we also computed metacognitive measures during tutoring sessions. Results showed that Immediate Feedback in an intelligent tutoring system had a statistically significant positive effect on learning gains, G and discrimination. Removal of Immediate Feedback was associated with decreasing metacognitive performance, and this decline was not prevented when students used a version of the tutoring system that provided other metacognitive scaffolds. Results obtained directly from the ITS suggest that other metacognitive scaffolds do have a positive effect on G and discrimination, as Immediate Feedback is faded. We conclude that Immediate Feedback had a positive effect on both metacognitive and cognitive gains in a medical tutoring system. Other metacognitive scaffolds were not sufficient to replace Immediate Feedback in this study. However, results obtained directly from the tutoring system are not consistent with results obtained from assessments. In order to facilitate transfer to real-world tasks, further research will be needed to determine the optimum methods for supporting metacognition as Immediate Feedback is faded.
ł Wyrzykowski - One of the best experts on this subject based on the ideXlab platform.
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an anomaly detector with Immediate Feedback to hunt for planets of earth mass and below by microlensing
Monthly Notices of the Royal Astronomical Society, 2007Co-Authors: M Dominik, N J Rattenbury, A Allan, Shude Mao, D M Bramich, M J Burgdorf, E Kerins, Y Tsapras, ł WyrzykowskiAbstract:The discovery of OGLE 2005-BLG-390Lb, the first cool rocky/i cy exoplanet, impressively demonstrated the sensitivity of the microlensing technique to extra-solar planets below 10 M�. A planet of 1 Minstead of the expected 5 Mfor OGLE 2005-BLG-390Lb (with an uncertainty factor of two) in the same spot would have provided a detectable deviation with an amplitude of � 3 per cent and a duration of � 12 h. While a standard sampling interval of 1.5 to 2.5 hours for microlensing follow-up observations appears to be insuffi- cient for characterizing such light curve anomalies and thereby claiming the discovery of the planets that caused these, an early detection of a deviation could trigger higher-cadence sam- pling which would have allowed the discovery of an Earth-mass planet in this case. Here, we describe the implementation of an automated anomaly detector, embedded into the eSTAR system, that profits from Immediate Feedback provided by the robotic telescopes that form the RoboNet-1.0 network. It went into operation for the 2007 microlensing observing season. As part of our discussion about an optimal strategy for planet detection, we shed some new light on whether concentrating on highly-magnified events i s promising and planets in the 'resonant' angular separation equal to the angular Einstei n radius are revealed most easily. Given that sub-Neptune mass planets can be considered being common around the host stars probed by microlensing (preferentially M- and K-dwarfs), the higher number of events that can be monitored with a network of 2m telescopes and the increased detection efficiency for planets below 5 Marising from an optimized strategy gives a common effort of current mi- crolensing campaigns a fair chance to detect an Earth-mass planet (from the ground) ahead of the COROT or Kepler missions. The detection limit of gravitational microlensing extends even below 0.1 M�, but such planets are not very likely to be detected from current cam- paigns. However, these will be within the reach of high-cadence monitoring with a network of wide-field telescopes or a space-based telescope.
T. Shemsedinov - One of the best experts on this subject based on the ideXlab platform.
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User-Driven Intelligent Interface on the Basis of Multimodal Augmented Reality and Brain-Computer Interaction for People with Functional Disabilities
Advances in Information and Communication Networks, 2019Co-Authors: Peng Gang, Yu. Gordienko, T. Shemsedinov, Yu. Kochura, N. Gordienko, A. Rojbi, Oleg Alienin, Sergii Stirenko, Jiang Hui, J. R. López BenitoAbstract:The analysis of the current integration attempts of some modes and use cases of human-to-machine interaction is presented. The new concept of the user-driven intelligent interface for accessibility is proposed on the basis of multimodal augmented reality and brain-computer interaction for various applications: in disabilities studies, education, home care, health care, eHealth, etc. The several use cases of multimodal augmentation are presented. The perspectives of the better human comprehension by the Immediate Feedback through neurophysical channels by means of brain-computer interaction are outlined. It is shown that brain–computer interface (BCI) technology provides new strategies to overcome limits of the currently available user interfaces, especially for people with functional disabilities. The results of the previous studies of the low end consumer and open-source BCI-devices allow us to conclude that combination of machine learning (ML), multimodal interactions (visual, sound, tactile) with BCI will profit from the Immediate Feedback from the actual neurophysical reactions classified by ML methods. In general, BCI in combination with other modes of AR interaction can deliver much more information than these types of interaction themselves. Even in the current state the combined AR-BCI interfaces could provide the highly adaptable and personal services, especially for people with functional disabilities.
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user driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities
arXiv: Human-Computer Interaction, 2017Co-Authors: Sergii Stirenko, T. Shemsedinov, Yu. Kochura, N. Gordienko, A. Rojbi, Oleg Alienin, Yuri Gordienko, J Lopez R Benito, Artetxe E GonzalezAbstract:The analysis of the current integration attempts of some modes and use cases of user-machine interaction is presented. The new concept of the user-driven intelligent interface is proposed on the basis of multimodal augmented reality and brain-computer interaction for various applications: in disabilities studies, education, home care, health care, etc. The several use cases of multimodal augmentation are presented. The perspectives of the better human comprehension by the Immediate Feedback through neurophysical channels by means of brain-computer interaction are outlined. It is shown that brain-computer interface (BCI) technology provides new strategies to overcome limits of the currently available user interfaces, especially for people with functional disabilities. The results of the previous studies of the low end consumer and open-source BCI-devices allow us to conclude that combination of machine learning (ML), multimodal interactions (visual, sound, tactile) with BCI will profit from the Immediate Feedback from the actual neurophysical reactions classified by ML methods. In general, BCI in combination with other modes of AR interaction can deliver much more information than these types of interaction themselves. Even in the current state the combined AR-BCI interfaces could provide the highly adaptable and personal services, especially for people with functional disabilities.
