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AUTOMATIC RECOGNITION of CHILDREN's TOUCHSCREEN STROKE GESTURES by ALEX SHAW a DISSERTATION PRESENTED to the GRADUATE SCHOOL O

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AUTOMATIC RECOGNITION of CHILDREN's TOUCHSCREEN STROKE GESTURES by ALEX SHAW a DISSERTATION PRESENTED to the GRADUATE SCHOOL O AUTOMATIC RECOGNITION OF CHILDREN'S TOUCHSCREEN STROKE GESTURES By ALEX SHAW A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2020 c 2020 Alex Shaw ACKNOWLEDGMENTS I would like to thank my advisor, Dr. Lisa Anthony, for her continued support, encouragement, and advice throughout my PhD. I thank my co-advisor, Dr. Jaime Ruiz, as well as the rest of my committee, for their invaluable support through the process. Finally, I thank my labmates for being excellent coworkers and I thank my parents for their support through the long journey that obtaining a PhD has been. 3 TABLE OF CONTENTS page ACKNOWLEDGMENTS..................................3 LIST OF TABLES......................................7 LIST OF FIGURES.....................................8 ABSTRACT......................................... 13 CHAPTER 1 INTRODUCTION................................... 15 1.1 Contributions................................... 17 2 RELATED WORK................................... 19 2.1 Developmentally Appropriate Prompts and Feedback.............. 20 2.2 Selection of Gestures............................... 21 2.3 Recognition and Classification of Children's Gestures.............. 23 2.3.1 Challenges in Studying Children's Gestures............... 24 2.3.2 Summary................................. 26 3 RECOGNITION METHODS.............................. 27 3.1 Template Matchers............................... 27 3.2 Feature-based Statistical Classifiers....................... 30 3.3 Hidden Markov Models (HMMs)........................ 32 3.4 Support Vector Machines (SVMs)........................ 34 3.5 Neural Networks................................. 34 3.6 Mixed Methods.................................. 36 3.6.1 Summary................................. 37 4 ESTABLISHING RECOGNITION RATES....................... 38 4.1 Recognition with $P............................... 38 4.1.1 Gesture Set................................ 39 4.1.2 Participants................................ 39 4.1.3 Equipment................................ 40 4.1.4 Applications............................... 40 4.1.5 Setup................................... 40 4.1.6 Results.................................. 41 4.1.7 Effect of Grade Level and Gender on Recognition............ 41 4.2 Comparing Recognizers............................. 44 4.3 Results of Recognition Experiments....................... 46 4.3.1 Setup of Experiments........................... 46 4.3.2 Template Matchers............................ 46 4 4.3.3 Feature-Based Statistical Classifiers................... 52 4.3.4 Support Vector Machines (SVMs).................... 54 4.3.5 Hidden Markov Models (HMMs)..................... 55 4.3.6 Neural Networks............................. 57 4.3.7 Mixed Methods.............................. 59 4.3.8 Statistical Differences Between Recognizers............... 60 4.3.9 Discussion................................ 60 4.4 Comparison of Recognition Rates across Devices................ 63 4.4.1 Tablet Study............................... 63 4.4.1.1 Participants.......................... 65 4.4.1.2 Results............................. 65 4.4.2 Tabletop................................. 66 4.4.2.1 Participants.......................... 66 4.4.2.2 Results............................. 67 4.5 Human Recognition............................... 67 4.5.1 Procedure................................. 68 4.5.2 Recognition Accuracy by Recognizer Type................ 69 4.5.3 Recognition Accuracy by Gesture Category............... 71 4.5.4 Confusion Matrices............................ 73 4.5.5 Discussion................................ 78 4.5.5.1 Human vs. Machine Recognition............... 78 4.5.5.2 Commonly Confused Pairs................... 80 4.6 Summary..................................... 81 5 ARTICULATION FEATURES............................. 83 5.1 Existing Articulation Features.......................... 83 5.1.1 Results.................................. 84 5.1.1.1 Simple Features........................ 84 5.1.1.2 Relative Accuracy Features.................. 97 5.1.1.3 Discussion........................... 109 5.2 Child-Specific Articulation Features....................... 110 5.2.1 Description of the Features....................... 111 5.2.1.1 Joining Error.......................... 111 5.2.1.2 Number of Enclosed Areas................... 111 5.2.1.3 Rotation Error......................... 112 5.2.1.4 Proportion of Extra Gesture in "Tails"............ 113 5.2.1.5 Average Percentage of Stray Ink............... 113 5.2.1.6 Disconnectedness....................... 115 5.2.2 Annotating the Features......................... 115 5.2.3 Results and Analysis........................... 117 5.2.3.1 Occurrence of Features.................... 117 5.2.3.2 Effect of Age.......................... 117 5.2.3.3 Correlation with Recognition................. 123 5.3 Discussion.................................... 124 5 5.3.1 Comparison between Studies....................... 128 5.3.2 Summary................................. 129 6 DESIGN IMPLICATIONS............................... 130 6.1 Gesture Collection................................ 130 6.1.1 Gesture Set Design............................ 131 6.2 Gesture Recognition............................... 131 6.3 Summary..................................... 132 7 FUTURE WORK................................... 133 7.1 Improving Recognition Rates.......................... 133 7.1.1 Beautification............................... 133 7.1.2 Child-Specific Algorithms......................... 135 7.2 Articulation Features............................... 135 7.3 Other Analyses.................................. 135 7.3.1 Effect of Context and Motivation.................... 135 7.3.2 Relationship with Cognitive Development................ 136 8 CONCLUSION..................................... 137 8.1 Research Goal 1................................. 137 8.1.1 Establishing Recognition Rates...................... 137 8.1.2 Human Recognition........................... 138 8.2 Research Goal 2................................. 138 8.2.1 Analyzing Existing Articulation Features................. 138 8.2.2 Developing New Articulation Features for Children........... 139 8.2.3 Analyzing the Correlation between Articulation Features and Recognition Rates................................... 139 8.3 Contributions................................... 140 8.4 Publications................................... 140 APPENDIX A ADULT RECOGNITION RATES........................... 142 REFERENCES........................................ 145 BIOGRAPHICAL SKETCH................................. 154 6 LIST OF TABLES Table page 2-1 Examples of gesture sets employed in prior work on recognition........... 22 4-1 The recognizers compared in our study......................... 45 4-2 Human Accuracy of gestures seen by a small number of participants versus a larger number. The accuracy is not significantly different when a large number of participants sees the gestures.................................... 69 5-1 Articulation features we examined in our study..................... 85 5-2 Formulae for calculating the features we examined in our study, where N is the number of points in the gesture, S is the number of strokes, xi is the x coordinate of the ith point, yi is the y coordinate of the ith point, and ti is the time (in milliseconds) of the ith point. See Figure 5-1 for reference..................... 86 5-3 The average time between strokes of gestures (in milliseconds) for each of the age groups in our corpus.................................. 96 5-4 The percentage of gestures with nonzero values for each feature by age group.... 117 5-5 Correlation coefficients for the articulation features in our studies. Features with a significant correlation are colored based on the magnitude of the r value: green for 0.25 ≤ jrj < 0.50, blue for 0.50 ≤ jrj < 0.75, and red for 0.75 ≤ jrj ≤ 1.00.... 124 A-1 Comparison of recognition rates of adults' gestures from previous work with rates from our work to verify correctness of implementation................ 144 7 LIST OF FIGURES Figure page 2-1 Diamond gestures produced by children ages 5 to 10 and adults from one of our studies [113]. Each column represents a different user................. 20 4-1 The gesture set we use in our studies.......................... 39 4-2 The applications used to collect gestures in our study: an abstract application (left) and a more complex, game-like application (right)................... 41 4-3 Effect of age on $P [103] recognition rates. Error bars represent the 95% confidence interval......................................... 42 4-4 Effect of grade level on $P [103] recognition rates. Error bars represent the 95% confidence interval................................... 43 4-5 Effect of age and gender on $P [103] recognition rates. Error bars represent the 95% confidence interval................................. 44 4-6 Effect of age on user-independent recognition rates for each of the recognizers in our study. Recognizers are listed roughly in order of performance, from highest accuracy to lowest................................... 47 4-7 Effect of age on user-dependent recognition rates using $N-Protractor [12]. Error bars represent the 95% confidence interval....................... 48 4-8 Effect of age on user-independent recognition rates using $N-Protractor [12]. Error bars represent the 95% confidence interval......................
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