Board #118 - Research Abstract Real Time Quantification of Stress during High-fidelity Human Simulation for a Standardized Learning Experience (Submission #9956)

2014 ◽  
Vol 9 (6) ◽  
pp. 406
Author(s):  
Harshavardhan Deoghare ◽  
Marie Gilbert
Keyword(s):  
Real Time ◽  
Learning Experience ◽  
Research Abstract ◽  
High Fidelity ◽  
Human Simulation

Robust and high fidelity real-time hybrid substructuring

2021 ◽  
Vol 157 ◽  
pp. 107720
Author(s):  
Christina Insam ◽  
Arian Kist ◽  
Henri Schwalm ◽  
Daniel J. Rixen
Keyword(s):  
Real Time ◽  
High Fidelity

scholarly journals The use of multiple-criteria decision-making theory to measure students’ perceptions of high-fidelity simulation

2017 ◽  
Vol 3 (3) ◽  
pp. 88-93 ◽  
Author(s):  
Maureen Anne Jersby ◽  
Paul Van-Schaik ◽  
Stephen Green ◽  
Lili Nacheva-Skopalik
Keyword(s):  
Decision Making ◽  
Clinical Practice ◽  
Repeated Measures ◽  
Clinical Decision Making ◽  
Learning Experience ◽  
Clinical Decision ◽  
High Fidelity ◽  
High Fidelity Simulation ◽  
Initial Exposure ◽  
Repeated Measures Design

BackgroundHigh-Fidelity Simulation (HFS) has great potential to improve decision-making in clinical practice. Previous studies have found HFS promotes self-confidence, but its effectiveness in clinical practice has not been established. The aim of this research is to establish if HFS facilitates learning that informs decision-making skills in clinical practice using MultipleCriteria DecisionMaking Theory (MCDMT).MethodsThe sample was 2nd year undergraduate pre-registration adult nursing students.MCDMT was used to measure the students’ experience of HFS and how it developed their clinical decision-making skills. MCDMT requires characteristic measurements which for the learning experience were based on five factors that underpin successful learning, and for clinical decision-making, an analytical framework was used. The study used a repeated-measures design to take two measurements: the first one after the first simulation experience and the second one after clinical placement. Baseline measurements were obtained from academics. Data were analysed using the MCDMT tool.ResultsAfter their initial exposure to simulation learning, students reported that HFS provides a high-quality learning experience (87%) and supports all aspects of clinical decision-making (85%). Following clinical practice, the level of support for clinical decision-making remained at 85%, suggesting that students believe HFS promotes transferability of knowledge to the practice setting.ConclusionOverall, students report a high level of support for learning and developing clinical decision-making skills from HFS. However, there are no comparative data available from classroom teaching of similar content so it cannot be established if these results are due to HFS alone.

Introducing MantisBot: Hexapod robot controlled by a high-fidelity, real-time neural simulation

2015 ◽  
Author(s):  
Nicholas S. Szczecinski ◽  
David M. Chrzanowski ◽  
David W. Cofer ◽  
Andrea S. Terrasi ◽  
David R. Moore ◽  
...  
Keyword(s):  
Real Time ◽  
High Fidelity ◽  
Hexapod Robot ◽  
Neural Simulation

Dynamic Data-Driven Spatiotemporal System Behavior Prediction With Simulations and Sensor Measurement Data

2018 ◽  
Author(s):  
Xiangxue Zhao ◽  
Shapour Azarm ◽  
Balakumar Balachandran
Keyword(s):  
Dynamical System ◽  
Real Time ◽  
Measurement Data ◽  
Data Driven ◽  
Sensor Data ◽  
Model Parameters ◽  
High Fidelity ◽  
System Behavior ◽  
Sensor Measurement

Online prediction of dynamical system behavior based on a combination of simulation data and sensor measurement data has numerous applications. Examples include predicting safe flight configurations, forecasting storms and wildfire spread, estimating railway track and pipeline health conditions. In such applications, high-fidelity simulations may be used to accurately predict a system’s dynamical behavior offline (“non-real time”). However, due to the computational expense, these simulations have limited usage for online (“real-time”) prediction of a system’s behavior. To remedy this, one possible approach is to allocate a significant portion of the computational effort to obtain data through offline simulations. The obtained offline data can then be combined with online sensor measurements for online estimation of the system’s behavior with comparable accuracy as the off-line, high-fidelity simulation. The main contribution of this paper is in the construction of a fast data-driven spatiotemporal prediction framework that can be used to estimate general parametric dynamical system behavior. This is achieved through three steps. First, high-order singular value decomposition is applied to map high-dimensional offline simulation datasets into a subspace. Second, Gaussian processes are constructed to approximate model parameters in the subspace. Finally, reduced-order particle filtering is used to assimilate sparsely located sensor data to further improve the prediction. The effectiveness of the proposed approach is demonstrated through a case study. In this case study, aeroelastic response data obtained for an aircraft through simulations is integrated with measurement data obtained from a few sparsely located sensors. Through this case study, the authors show that along with dynamic enhancement of the state estimates, one can also realize a reduction in uncertainty of the estimates.

scholarly journals Effectiveness of High Fidelity Video-Assisted Real-Time Simulation: A Comparison of Three Training Methods for Acute Pediatric Emergencies

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Ester H. A. J. Coolen ◽  
Jos M. T. Draaisma ◽  
Marije Hogeveen ◽  
Tim A. J. Antonius ◽  
Charlotte M. L. Lommen ◽  
...  
Keyword(s):  
Real Time ◽  
High Fidelity ◽  
Training Methods ◽  
Real Time Simulation ◽  
Pediatric Emergencies ◽  
Video Assisted ◽  
Time Simulation

scholarly journals Show or Tell? Teaching with language outperforms demonstration but only when context is shared

2021 ◽  
Author(s):  
Theodore Sumers ◽  
Mark K Ho ◽  
Robert Hawkins ◽  
Tom Griffiths
Keyword(s):  
Real Time ◽  
Comparative Approach ◽  
High Fidelity ◽  
Linguistic Communication ◽  
Communicative Acts ◽  
Wide Range ◽  
The World ◽  
High Bandwidth ◽  
Abstract Language ◽  
Shared Context

People use a wide range of communicative acts, from concrete demonstrations to abstract language. What are the strengths and weaknesses of such different modalities? We present a series of real-time, multi-player experiments asking participants to teach (Boolean) concepts using either demonstrations or language. Our first experiment (N = 454) manipulated the complexity of the concept, finding that linguistic (but not demonstrative) teaching enables high-fidelity transmission of more complex concepts. Why, then, do humans use both demonstrations and language? As a form of conventionalized communication, language relies on shared context between speaker and listener, whereas demonstrations are inherently grounded in the world. We hypothesized linguistic communication would be more sensitive to perturbations of shared context than demonstrations. Our second experiment (N = 568) manipulated teachers’ ability to see the features that defined the concept. This restriction severely impaired linguistic (but not demonstrative) teaching. Our comparative approach confirms language relies on shared context to permit high bandwidth communication; in contrast, demonstrations are lower-bandwidth but more robust.

scholarly journals Using student feedback and professor-developed multimedia to improve instructor presence and student learning

2014 ◽  
pp. 12-30 ◽  
Author(s):  
William Albert Young II ◽  
Brett H. Hicks ◽  
Danielle Villa-Lobos ◽  
Teresa J. Franklin
Keyword(s):  
Real Time ◽  
Social Presence ◽  
Learning Experience ◽  
Student Feedback ◽  
Learning Needs ◽  
Time Data ◽  
Online Distance Education ◽  
Learning Content ◽  
Online Distance ◽  
Feedback Data

This paper explores the use of Professor-Developed Multimedia Content (PDMC) in online, distance education to build a community of inquiry (CoI) through enhanced social presence and real-time, student-driven, adaption of the learning content. The foundation of higher education has long been, developing curriculum to meet educational objectives. Most often faculty relies on assessment information gained at the end of each course. Then assessments, formative and summative, are re-designed based on student feedback/data from end of course surveys and educational materials such as textbooks, articles, and test banks are updated with newer editions. In the distance-learning environment, PDMC provides a creative, innovative, and interactive ways to engage the student for real-time learning. Still, the ability to target PDMC materials to the correct sub-sections of our classroom cohort can produce a richer, more immerse learning experience and perhaps become the closet recreation of in-seat, traditional classroom learning in a distance/online environment. By using PDMC with corresponding surveys, educators can obtain real-time data and metrics to alter content in the classroom immediately, and develop media content welcoming sub-sets of learners with desired content based on learning needs, desires, and feedback.

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