Abstract
Modeling cognitive load of user interaction based on ocular parameters have become a dominant method for exploring usability evaluation of interfaces for systems and applications. Growing importance of Artificial Intelligence in Human Computer Interaction (HCI) has proposed many approaches to understand users’ need and enhance human centric method for interface design. In particular, machine learning-based cognitive modeling, using eye tracking parameters have received more attention in the context of smart devices and applications. In this context, this paper aims to model the estimated cognitive load values for each user into different levels of cognition like very high, high, moderate, low, very low etc., while performing different tasks on a smart phone. The study focuses on the use behavioural measures, ocular parameters along with eight traditional machine learning classification algorithms like Decision Tree, Linear Discriminant Analysis, Random Forest, Support Vector Machine, Naïve Bayes, Neural Network, Fuzzy Rules with Weight Factor and K-Nearest Neighbor to model different levels of estimated cognitive load for each participant. The data set for modeling consisted of 250 records, 11 ocular parameters as prediction variables including age and type of task; and three types of classes (2-class, 3-class, 5-class) for classifying the estimated cognitive load for each participant. We noted that, Age, Fixation Count, Saccade Count, Saccade Rate, Average Pupil Dilation are the most important parameters contributing to modeling the estimated cognitive load levels. Further, we observed that, the Decision Tree algorithm achieved highest accuracy for classifying estimated cognitive load values into 2-class (86.8%), 3-class (74%) and 5-class (62.8%) respectively. Finally, from our study, it may be noted that, machine learning is an effective method for predicting 2-class-based (Low and High) cognitive load levels using ocular parameters. The outcome of the study also provides the fact that ageing affects users’ cognitive workload while performing tasks on smartphone.
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References
Moray, N.: Mental Workload: Its Theory and Measurement. Plenum, New York (1979)
Wickens, C.D., Hollands, J.G.: Engineering Psychology and Human Performance, 3rd edn. Prentice Hall, Upper Saddle River (2000)
Galy, E., Cariou, M., Mélan, C.: What is the relationship between mental workload factors and cognitive load types? Int. J. Psychophysiol. 83(3), 269–275 (2012)
Salvucci, D.D., Lee, F.J.: Simple cognitive modeling in a complex cognitive architecture. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 265–272 (2003)
Abdul, A., von der Weth, C., Kankanhalli, M., Lim, B.Y.: COGAM: measuring and moderating cognitive load in machine learning model explanations. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, pp. 1–14 (2020)
Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012)
Brunken, R., Plass, J.L., Leutner, D.: Direct measurement of cognitive load in multimedia learning. Educ. Psychol. 38(1), 53–61 (2003)
Gevins, A., et al.: Monitoring working memory load during computer-based tasks with EEG pattern recognition methods. Hum. Factors 40(1), 79–91 (1998)
Chen, S.: Cognitive load measurement from eye activity: acquisition, efficacy, and real-time system design. University of New South Wales (2014)
Klami, A., Saunders, C., de Campos, T.E., Kaski, S.: Can relevance of images be inferred from eye movements? In: Proceedings of the 1st ACM International Conference on Multimedia Information Retrieval, pp. 134–140 (2008)
Eivazi, S., Bednarik, R.: Predicting problem-solving behavior and performance levels from visual attention data. In: Proceedings of Workshop on Eye Gaze in Intelligent Human Machine Interaction at IUI, pp. 9–16 (2011)
Li, X., Younes, R., Bairaktarova, D., Guo, Q.: Predicting spatial visualization problems’ difficulty level from eye-tracking data. Sensors 20(7), 1949 (2020)
Behroozi, M., Parnin, C.: Can we predict stressful technical interview settings through eye-tracking? In: Proceedings of the Workshop on Eye Movements in Programming, pp. 1–5 (2018)
Appel, T., et al.: Predicting cognitive load in an emergency simulation based on behavioral and physiological measures. In: 2019 International Conference on Multimodal Interaction, pp. 154–163 (2019)
Joseph, A.W., DV, J.S., Saluja, K.P.S., Mukhopadhyay, A., Murugesh, R., Biswas, P.: Eye tracking to understand impact of aging on mobile phone applications (2021)
Mao, Y., He, Y., Liu, L., Chen, X.: Disease classification based on eye movement features with decision tree and random forest. Front. Neurosci. 14, 798 (2020)
Salojärvi, J., Puolamäki, K., Simola, J., Kovanen, L., Kojo, I., Kaski, S.: Inferring relevance from eye movements: feature extraction. In: Workshop at NIPS 2005, in Whistler, BC, Canada, 10 December 2005, p. 45 (2005)
Breiman, L.: Random forests Leobreiman and Adele Cutler. Random Forests-Classification Description (2015)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Zhang, Z.: Naïve Bayes classification in R. Ann. Transl. Med. 4(12) (2016)
Wang, S.C.: Artificial neural network. In: Wang, S.C. (ed.) Interdisciplinary Computing in java Programming, pp. 81–100. Springer, Boston (2003). https://doi.org/10.1007/978-1-4615-0377-4_5
Abadeh, M.S., Habibi, J., Soroush, E.: Induction of fuzzy classification systems via evolutionary ACO-based algorithms. Computer 35, 37 (2008)
Guan, F., Shi, J., Ma, X., Cui, W., Wu, J.: A method of false alarm recognition based on k-nearest neighbor. In: 2017 International Conference on Dependable Systems and Their Applications (DSA), pp. 8–12. IEEE (2017)
Behroozi, M., Parnin, C.: Can we predict stressful technical interview settings through eye-tracking? In: Proceedings of the Workshop on Eye Movements in Programming, pp. 1–5 (2018)
Krol, M., Krol, M.: A novel approach to studying strategic decisions with eye-tracking and machine learning. Judgm. Decis. Mak. 12(6), 596 (2017)
Richstone, L., Schwartz, M.J., Seideman, C., Cadeddu, J., Marshall, S., Kavoussi, L.R.: Eye metrics as an objective assessment of surgical skill. Ann. Surg. 252(1), 177–182 (2010)
Cai, Y., Huang, H., Cai, H., Qi, Y.: A k-nearest neighbor locally search regression algorithm for short-term traffic flow forecasting. In: 2017 9th International Conference on Modelling, Identification and Control (ICMIC), pp. 624–629. IEEE (2017)
Pouw, W.T., Eielts, C., Van Gog, T., Zwaan, R.A., Paas, F.: Does (non-) meaningful sensori-motor engagement promote learning with animated physical systems? Mind Brain Educ. 10(2), 91 (2016)
Dubé, A.K., McEwen, R.N.: Do gestures matter? The implications of using touchscreen devices in mathematics instruction. Learn. Instr. 40, 89–98 (2015)
Joseph, A.W., Murugesh, R.: Potential eye tracking metrics and indicators to measure cognitive load in human-computer interaction research. J. Sci. Res. 64(1) (2020)
Acknowledgment
The authors are thankful to the Department of Computer Application, Madurai Kamaraj University, Madurai, India and National Institute of Design, Bengaluru Campus, India for their encouragement, motivation, and relentless support in carrying out our study.
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Joseph, A.W., Vaiz, J.S., Murugesh, R. (2021). Modeling Cognitive Load in Mobile Human Computer Interaction Using Eye Tracking Metrics. In: Ahram, T.Z., Karwowski, W., Kalra, J. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-030-80624-8_13
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