PATTERNS IDENTIFICATION FOR HITTING ADJACENT KEY ERRORS CORRECTION USING NEURAL NETWORK MODELS

摘要：

People with Parkinson diseases or motor disability miss-stroke keys. It appears that keyboard layout, key distance, time gap are affecting this group of peoples 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 users ‘Hitting Adjacent Key Errors behaviour with this specific datasets.

关键词： QWERTY Keyboard Probabilistic Neural Network Backpropagation Key Distance Time Gap Error

作者: Jun Li Karim Ouazzane Sajid Afzal Hassan Kazemian

作者单位: Department of Land Economy, University of Cambridge, 19 Silver Street, Cambridge, U.K. Faculty of Computing, London Metropolitan University, London, U.K.

会议类型: 国际会议

会议名称: 13th International Conference on Enterprise Information System(第13届企业信息系统国际会议 ICEIS 2011)

会议地点: 北京

会议语种:英文

页码: 2474-2481

在线出版日期: 2011-06-08（万方平台首次上网日期，不代表论文的发表时间）