Abstract
A virtual reality driving simulator is designed as a tool for improving driving skills of individuals with autism spectrum disorders (ASD). Training at an appropriate driving difficulty level can maximize long term performance. Affective state information has been used for difficulty level adjustment in our previous work. This paper integrates performance with affective state information to predict the optimal difficulty level. The participant’s performance data, physiology signals, and eye gaze data are captured. The performance features and affective state features are extracted. Two classification methods, Support Vector Machine (SVM) and Artificial Neural Network (ANN), are implemented to predict difficulty level. The results demonstrate that performance together with affective state information outperform the separated features in difficulty level prediction. A highest accuracy of 83.09% is achieved with the integrated features.
Chapter PDF
Similar content being viewed by others
References
Lahiri, U., Bekele, E., Dohrmann, E., Warren, Z., Sarkar, N.: Design of a Virtual Reality based Adaptive Response Technology for Children with Autism. IEEE Transactions on Neural Systems and Rehabilitation Engineering 21(1), 55–64 (2013)
Lahiri, U., Warren, Z., Sarkar, N.: Design of a Gaze-sensitive Virtual Social Interactive System for Children with Autism. IEEE Transactions on Neural Systems and Rehabilitation Engineering 19(4), 443–452 (2011)
Novak, D., Mihelj, M., Munih, M.: “A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing. Interacting with Computers 24(3), 154–172 (2012)
Woolf, B., et al.: “Affect-aware tutors: recognizing and responding to student affect. International Journal of Learning Technology 4(3), 129–164 (2009)
Rowe, J.P., Lester, J.C.: Modeling User Knowledge with Dynamic Bayesian Networks in Interactive Narrative Environments. In: AIIDE (2010)
Wilson, G.F., Russell, C.A.: Performance enhancement in an uninhabited air vehicle task using psychophysiologically determined adaptive aiding. Human Factors: The Journal of the Human Factors and Ergonomics Society 49(6), 1005–1018 (2007)
Novak, D., et al.: Psychophysiological measurements in a biocooperative feedback loop for upper extremity rehabilitation. IEEE Transactions on Neural Systems and Rehabilitation Engineering 19(4), 400–410 (2011)
Bergasa, L.M., Nuevo, J., Sotelo, M.A., Barea, R., Lopez, M.E.: Real-time system for monitoring driver vigilance. IEEE Transactions on Intelligent Transportation Systems 7(1), 63–77 (2006)
Kapoor, A., Picard, R.W.: Multimodal affect recognition in learning environments. In: Proceedings of the 13th Annual ACM International Conference on Multimedia. ACM (2005)
Liu, C., Rani, P., Sarkar, N.: An Empirical Study of Machine Learning Techniques for Affect Recognition in Human-Robot Interaction. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005), August 02-06, pp. 2451–2456 (2005)
Biopac system, http://www.biopac.com/
Eye tracker X120, http://www.tobii.com/
Liu, C., Conn, K., Sarkar, N., Stone, W.: Physiology-based affect recognition for computer-assisted intervention of children with Autism Spectrum Disorder. International Journal of Human-Computer Studies 66(9), 662–677 (2008)
Rani, P., Sarkar, N., Smith, C., Adams, J.: Affective Communication For Implicit Human-Machine Interaction. In: IEEE International Conference on System, Man and Cybernetics, Washington D. C., pp. 4896–4903 (October 2003)
Liu, C., Rani, P., Sarkar, N.: Human-Robot Interaction Using Affective Cues. In: The 15th IEEE International Symposium on Robot and Human Interactive Communication - ROMAN 2006, United Kingdom, pp. 285–290 (September 2006)
Boswell, D.: Introduction to support vector machines (2002)
Beale, M., Hagan, M.T., Demuth, H.B.: Neural network toolbox. Neural Network Toolbox, The Math Works, 5-25 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, L., Wade, J.W., Bian, D., Swanson, A., Warren, Z., Sarkar, N. (2014). Data Fusion for Difficulty Adjustment in an Adaptive Virtual Reality Game System for Autism Intervention. In: Stephanidis, C. (eds) HCI International 2014 - Posters’ Extended Abstracts. HCI 2014. Communications in Computer and Information Science, vol 434. Springer, Cham. https://doi.org/10.1007/978-3-319-07857-1_114
Download citation
DOI: https://doi.org/10.1007/978-3-319-07857-1_114
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07856-4
Online ISBN: 978-3-319-07857-1
eBook Packages: Computer ScienceComputer Science (R0)