Designing smarter touch-based interfaces for educational contexts

Child or Adult? Inferring Smartphone Users’ Age Group from Touch Measurements Alone

“…However, we must leave such detailed investigations for future work, as they will most likely need examination of additional features to reach similar levels of accuracy. For example, Figure 5 shows two such measures, PRESSURE and TOUCH-AREA for 5 age groups and 185 participants [3,9]. Because such measurements may not be available for all touch-screen hardware, fine age group discrimination may be restricted to some devices only.…”

“…By placing our discoveries in the larger context set out by previous work on touch-screen interaction design for children [2,3,6,9], we can already anticipate multiple application opportunities made possible by our classification technique: (1) Adaptive widget layouts. Since we know from the literature that children miss onscreen touch targets more often than adults [3], are less precise [9], and have more difficulty with smaller targets [3], children would benefit from an interface layout with larger widgets and more space in-between. Our technique makes it possible to detect that the user is a child and, therefore, the application can reconfigure the interface layout on the fly.…”

“…processing chain of the application. For instance, we know that gesture recognizers perform worse on children's gestures [3], so the application could benefit from the age group knowledge to know when to activate one recognizer or another [4,10]. Also, we know that visual feedback during gesture input is more important for children than for adults [2], and the app could use the age group data to increase or dim down visual feedback as necessary, which will provide more cues for children and more screen space for adults to display other app widgets and data.…”

“…This trend is especially true for children of pre-school and elementary school age (ages 3 to 10) [5]. Previous work has determined that touch and stroke gesture interaction behaviors differ between adults and children in ways that significantly impact gesture recognition and processing of users' touch input [2,3,6], e.g., children's touches are more likely to be just outside the boundaries of their intended target [3] and their gestures are less likely to be recognized correctly than adults' gestures [2,3].…”

“…In this work, a child is a person having 6 years at most; at this age, children's touch input behaviors are the most different from those of adults knowing that children's input performance and touch accuracy improve with age [3]. Our technique, using a Bayes' rule classifier, delivered 86.5% user-independent accuracy on a dataset of 119 participants (89 children ages 3 to 6) when classifying each touch event one at a time and 99% accuracy with a window of 7+ consecutive touch events.…”