Abstract
We propose a novel approach to context recognition for use in extracting user interests in e-learning systems. Under our approach, a series of screen images are captured by an imaging device worn by the user engaged in elearning. From these images, vendor logo information is detected and used to deduce the e-learning context. The compiled history of recognized context and elearning access can then be compared to extract low-interest topics. Experimental results show that the proposed approach is robust and able to identify the proper context 96% of the time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Clark, R.C., Mayer, R.E.: e-Learning and the Science of Instruction. Pfeiffer (2007)
Shute, V., Towle, B.: Adaptive e-learning. Educational Psychologist 38(2), 105–112 (2003)
Blum, M.L.: Real-time context recognition. MIT Media Lab (2005)
Ugurlu, Y.: Human interactive e-learning systems using head posture images. Proc. World Academy of Science, Engineering and Technology 65, 1107–1110 (2012)
Ugurlu, Y.: Head posture detection using skin and hair information. In: Proc. Int. Conference on Pattern Recognition, pp. 1–4 (2012)
Demirel, H., Anbarjafari, G.: Image resolution enhancement by using discrete and stationary wavelet decomposition. IEEE Trans. on Image Processing 20(4), 1458–1460 (2011)
Vetterli, M., Kovacevic, J.: Wavelets and Subband Coding. Prentice Hall (1995)
Kouibia, A., Pasadas, M.: Approximation of surfaces by fairness bicubic splines. Advances in Computational Mathematics 20, 87–103 (2004)
Duda, R., Hart, P., Stork, D.: Pattern Classification. Wiley, New York (2001)
Sharma, G., Sood, S., Gaba, G.S.: Image recognition systems using geometric matching and contour detection. International Journal of Computer Applications 51(17), 49–53 (2012)
Klinger, T.: Image Processing with LabVIEW and IMAQ Vision. Prentice Hall PTC (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Uğurlu, Y. (2013). Wearable Context Recognition for Distance Learning Systems: A Case Study in Extracting User Interests. In: Lee, R. (eds) Computer and Information Science. Studies in Computational Intelligence, vol 493. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00804-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-00804-2_5
Publisher Name: Springer, Heidelberg
Print ISBN: 978-3-319-00803-5
Online ISBN: 978-3-319-00804-2
eBook Packages: EngineeringEngineering (R0)