  This article proposes a multistage framework for time series analysis of user
activity on touch sensitive surfaces in noisy environments. Here multiple
methods are put together in multi stage framework; including moving average,
moving median, linear regression, kernel density estimation, partial
differential equations and Kalman filter. The proposed three stage filter
consisting of partial differential equation based denoising, Kalman filter and
moving average method provides ~25% better noise reduction than other methods
according to Mean Squared Error (MSE) criterion in highly noise susceptible
environments. Apart from synthetic data, we also obtained real world data like
hand writing, finger/stylus drags etc. on touch screens in the presence of high
noise such as unauthorized charger noise or display noise and validated our
algorithms. Furthermore, the proposed algorithm performs qualitatively better
than the existing solutions for touch panels of the high end hand held devices
available in the consumer electronics market qualitatively.

Human-Computer Interaction;Applications