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A User-specific machine learning approach for improving touch accuracy on mobile devices

A User-specific machine learning approach for improving touch accuracy on mobile devices. TOUCH INTERFACE ISSUES. Electrostatic interference (noise) Jittery touch registration Unintentional selection Screen Misalignments Users constantly miss targets “Fat Finger Problem”

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A User-specific machine learning approach for improving touch accuracy on mobile devices

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  1. A User-specific machine learning approach for improving touch accuracy on mobile devices

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

  3. TOUCH INTERFACE ISSUES (2) • Results in: • Trivial actions requiring mental involvement • “Why won’t 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

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

  5. 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?

  6. 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

  7. 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

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

  9. 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

  10. THE EXPERIMENT (2) • Test Population • 8 Different Participants • Age between 23 and 34 • Most experiment subjects owned and regularly used smartphones though this wasn’t a requirement

  11. 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

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

  13. THE RESULTS (2) • 2nd 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

  14. 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

  15. 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

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