PATTERNS IDENTIFICATION FOR HITTING ADJACENT KEY ERRORS CORRECTION USING NEURAL NETWORK MODELS
Jun Li, Karim Ouazzane, Sajid Afzal, Hassan Kazemian
2011
Abstract
People with Parkinson diseases or motor disability miss-stroke keys. It appears that keyboard layout, key distance, time gap are affecting this group of people’s typing performance. This paper studies these features based on neural network learning algorithms to identify the typing patterns, further to correct the typing mistakes. A specific user typing performance, i.e. Hitting Adjacent Key Errors, is simulated to pilot this research. In this paper, a Time Gap and a Prediction using Time Gap model based on BackPropagation Neural Network, and a Distance, Angle and Time Gap model based on the use of Probabilistic Neural Network are developed respectively for this particular behaviour. Results demonstrate a high performance of the designed model, about 70% of all tests score above Basic Correction Rate, and simulation also shows a very unstable trend of user’s ‘Hitting Adjacent Key Errors’ behaviour with this specific datasets.
References
- Artificial neural network, [online], 31 Dec. 2010, available: http://en.wikipedia.org/wiki/Artificial_ neural_network [12 January 2010]
- David J. Ward, Alan F. Blackwell at el. (2000). 'Dasher-a Data Entry Interface Using Continuous Gestures and Language Models', UIST 7800 Proceedings of the 13th annual ACM symposium on User interface software and technology
- Disability Essex, http://www.disabilityessex.org [accessed 18 January 2009]
- Karim Ouazzane, Jun Li and Marielle Brouwer (2008). 'A hybrid framework towards the solution for people with disability effectively using computer keyboard', IADIS International Conference Intelligent Systems and Agents 2008, pp. 209-212
- Knowledge Transfer Partnership, http://www. ktponline.org.uk/ [accessed 18 January 2009]
- Paul M. Fitts (1954). 'The information capacity of the human motor system in controlling the amplitude of movement', Journal of Experimental Psychology, volume 47, number 6, June 1954, pp. 381-391
- Prototype, [online], n.d., available: http://www.sensory software.com/prototype.html [accessed 03 March 2008]
- R. W. Soukoreff, & I. S. MacKenzie, n.d. KeyCapture [online], available: http://dynamicnetservices.com/ will/academic/textinput/keycapture/ [accessed 18 January 2009]
- R. W. Soukoreff, & I. S. MacKenzie (2003). 'Input-based language modelling in the design of high performance text input techniques', Proceedings of Graphics Interface 2003, 89-96.
- The Dasher Project, [online], 14 Nov. 2007, Inference Group of Cambridge, available: http://www.inference. phy.cam.ac.uk/dasher/ [accessed 03 March 2008]
- Unary coding, [online], 23 December 2009, available: http://en.wikipedia.org/wiki/Unary_coding [12 January 2010]
- Virtual key codes [online], available: http://api.farmanager.com/en/winapi/virtualkeycodes.h tml [accessed 05 February 2009]
Paper Citation
in Harvard Style
Li J., Ouazzane K., Afzal S. and Kazemian H. (2011). PATTERNS IDENTIFICATION FOR HITTING ADJACENT KEY ERRORS CORRECTION USING NEURAL NETWORK MODELS . In Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-54-6, pages 5-12. DOI: 10.5220/0003413700050012
in Bibtex Style
@conference{iceis11,
author={Jun Li and Karim Ouazzane and Sajid Afzal and Hassan Kazemian},
title={ PATTERNS IDENTIFICATION FOR HITTING ADJACENT KEY ERRORS CORRECTION USING NEURAL NETWORK MODELS},
booktitle={Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2011},
pages={5-12},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003413700050012},
isbn={978-989-8425-54-6},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 13th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - PATTERNS IDENTIFICATION FOR HITTING ADJACENT KEY ERRORS CORRECTION USING NEURAL NETWORK MODELS
SN - 978-989-8425-54-6
AU - Li J.
AU - Ouazzane K.
AU - Afzal S.
AU - Kazemian H.
PY - 2011
SP - 5
EP - 12
DO - 10.5220/0003413700050012