TY - GEN
T1 - Using eye tracking to assess user behavior in virtual training
AU - Fahimipirehgalin, Mina
AU - Loch, Frieder
AU - Vogel-Heuser, Birgit
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Virtual training systems can provide flexible and effective training for interactions with increasingly complex industrial machines. However, existing approaches do not adapt to the adaptive attributes of the user. Being able to track the current state of the user enables a humanization of virtual training system, since it allows analyzing the strain and the cognitive processes of the user and reacting accordingly. In recent years, eye tracking technology has become a widespread research area in human machine interaction. This paper introduces an approach to adapt virtual training systems based on eye tracking analysis. The approach detects specific patterns from eye movements and evaluate the performance of the user based on detected patterns. If the pattern suggests that the user cannot follow the instructions or that the user is distracted, the complexity of the training system can be reduced.
AB - Virtual training systems can provide flexible and effective training for interactions with increasingly complex industrial machines. However, existing approaches do not adapt to the adaptive attributes of the user. Being able to track the current state of the user enables a humanization of virtual training system, since it allows analyzing the strain and the cognitive processes of the user and reacting accordingly. In recent years, eye tracking technology has become a widespread research area in human machine interaction. This paper introduces an approach to adapt virtual training systems based on eye tracking analysis. The approach detects specific patterns from eye movements and evaluate the performance of the user based on detected patterns. If the pattern suggests that the user cannot follow the instructions or that the user is distracted, the complexity of the training system can be reduced.
KW - Eye Tracking
KW - Pattern detection
KW - User behavior
KW - Virtual training
UR - http://www.scopus.com/inward/record.url?scp=85081909618&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39512-4_54
DO - 10.1007/978-3-030-39512-4_54
M3 - Conference contribution
AN - SCOPUS:85081909618
SN - 9783030395117
T3 - Advances in Intelligent Systems and Computing
SP - 341
EP - 347
BT - Intelligent Human Systems Integration - Proceedings of the 3rd International Conference on Intelligent Human Systems Integration IHSI 2020
A2 - Ahram, Tareq
A2 - Karwowski, Waldemar
A2 - Vergnano, Alberto
A2 - Leali, Francesco
A2 - Taiar, Redha
PB - Springer
T2 - 3rd International Conference on Intelligent Human Systems Integration, IHSI 2020
Y2 - 19 February 2020 through 21 February 2020
ER -