WRD: an approach for evaluating the performance of training program in virtual reality

The paper presents a new approach for evaluating the performance of the training program in virtual reality. The training process is described through three main steps: watching, recalling and doing (WRD). Each step is related to the sensor-motoric layer. The watching is related to the sensory layer...

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Bibliographic Details
Main Authors: Steshina Liudmila, Petukhov Igor, Glazyrin Andrey
Format: Article
Language:English
Published: EDP Sciences 2020-01-01
Series:MATEC Web of Conferences
Subjects:
Online Access:https://www.matec-conferences.org/articles/matecconf/pdf/2020/05/matecconf_cscns2020_03023.pdf
Description
Summary:The paper presents a new approach for evaluating the performance of the training program in virtual reality. The training process is described through three main steps: watching, recalling and doing (WRD). Each step is related to the sensor-motoric layer. The watching is related to the sensory layer, which accumulates the visual information from virtual reality. After then, that visual information will source for recalling the previous experiences followed by the decision-making using the cognitive layer. Finally, the decision-making activates the motor layer in form doing. The WRD approach uses the tests for the measuring of motor and sensory layers. The tests describe typical model of reaction for operator and activate motor and sensory layers as in real professional case. The cognitive layer may be measured with EEG. At the same time, the continuous generation of training cases may help to saturate the knowledge base. For this reason, the fuzzy model based on the generation of training cases was developed. The analysis of experimental data showed the decreasing of time delay by 83%. Moreover, it was shown the decreasing of rejection from horizontal and vertical axes in pixels by 62.7% and decreasing of time expected by 57% for motor tracking. The WRD approach may help to evaluate training program with evaluating sensory, cognitive and motor layer without developing prototype training simulator.
ISSN:2261-236X