TOUCH INTERFACE ISSUES Electrostatic interference (noise) Jittery touch registration Unintentional selection Screen Misalignments Users constantly miss targets Fat Finger Problem Imprecise selection

TOUCH INTERFACE ISSUES (2) Results in: Trivial actions requiring mental involvement Why wont this button activate when I press it? Users losing trust in the system Users cannot be confident in their selections Increasing error proneness Users must spend more time accommodating for mistakes

EXISTING SOLUTIONS Electrostatic sensor sensitivity hardware adjustments 3-point or 5-point calibration methodology UI Adjustments Error state recovery improvements Interface design alterations Were missing one major concept here…

USER PROFILING Every user performs differently User Profiling An association of specific data to specific users Why does this apply here? How do we obtain and apply the data?

INTELLIGENT UI Machine Learning approach Consists of training/test phases TRAINING PHASE Obtain data from some source (sensors) Process the data and generate a pattern (offset) TESTING PHASE Utilize the pattern to adjust the data collection process (recalibrate) Analyze how the adjustment affected the data (improvements) Lather, rinse, repeat until satisfied

HYPOTHESIS Users possess distinct touch offsets which hinder performance Machine learning can be implemented on raw data to calculate offsets Offsets can be used to calibrate the touch screen to provide a more consistent interface for the user Finally, the correction procedure will greatly improve user touch accuracy

CAPTURING THE DATA Nokia N9 MeeGo Capturing sensor data Uses Guassian Process Regression

THE EXPERIMENT Environment Uses the tester designated model phone Nokia Program on the phone prompts the user to touch crosshairs Records intended location (crosshair location) and physical touch location Phone held in landscape position

THE EXPERIMENT (2) Test Population 8 Different Participants Age between 23 and 34 Most experiment subjects owned and regularly used smartphones though this wasnt a requirement

THE EXPERIMENT (3) Procedure Prompt the user to touch 1,000 crosshairs Record intent/actual touch data for each attempt Split data into training/testing phase Repeat sets of 1,000 attempts for alternative experimental cases

THE RESULTS 1 st Experiment Used raw values from sensors Data analysis with 3 button radii and 2 training set sizes Resulted in statistically significant results

THE RESULTS (2) 2 nd Experiment Used interpreted touch location information specified by the phone Decreased button radius for each set Resulted in statistically significant improvements with even smaller training sets

FURTHER EXPERIMENTATION Testing in alternative cases to ensure genuine data Two users experimented in portrait mode as opposed to landscape Alternative phone models were used for some users Data still showed statistically significant improvements in touch accuracy when the offsets were applied to the test data

CONCLUSIONS Utilizing a ML approach proves a viable solution to user specifity This solution is versatile Research was thorough but for a small sample size Future work is necessary to further the study