Investigating the characteristics of unistroke gestures using a mobile game

Touch gestures are today’s main input method for the interaction with smart phones. In particular, unistroke gestures, which are gestures consisting of one articulated line, are commonly used for text input (touch keyboards) and short cuts referring to functions on mobile devices. This work investigates the user’s accuracy of articulating unistroke touch gestures on mobile devices. Therefore, two studies were conducted which focused on the user’s ability to perceive different variations of unistroke gestures and reproduce them accurately. First, a control study aimed at analyzing the user’s touch accuracy during the articulation of single line and composed line gestures by varying gesture properties like the rotation or angles within the gestures. To analyze the infl uences of the user’s distraction in his natural environment, a large scale study was conducted which used a mobile game as apparatus. The game was designed in a way that the user was motivated to articulate the given gestures precisely. The game principle was based on the control study procedure of perceiving and reproducing gestures. To gather a great amount of touch samples the game was published on a mobile app store and the samples were collected trough the internet. The analysis of both studies showed that the gestures orientation and the angles within the gestures affected the articulation accuracy in terms of the deviations made by bending, rotating, and varying the shape of the gestures. Furthermore, the gestures articulated in the game study tended to be more error-prone compared to those being articulated in the control study.Touch-Gesten sind momentan die am weitesten verbreitete Eingabemethode für die Interaktion mit Smartphones. Insbesondere Unistroke-Gesten, welche aus einem einzelnen Strich bestehen, werden für die Texteingabe mittels Touch-Keyboards und als Shortcut für Funktion auf mobilen Geräten verwendet. Im Rahmen dieser Arbeit wird die Genauigkeit untersucht, mit der der Nutzer Unistroke-Gesten auf mobilen Geräten ausführt. Zu diesem Zweck wurden zwei Studien durchgeführt, die die Fähigkeit des Nutzers untersuchen, verschiedene vorgegebene Gesten wahrzunehmen und diese zu reproduzieren. Eine Kontroll-Studie zielte darauf ab zu untersuchen, wie genau der Nutzer Gesten wiedergeben kann, die aus einzelnen geraden Linien oder aus zusammengesetzten geraden Linien bestehen wobei die Winkel und die Längen dieser Linien in dem Experiment variiert wurden. Um die Ablenkung durch äußeren Einflüsse im gewohnten Nutzerumfeld zu untersuchen, wurde eine größer angelegte Studie mittels eines Mobile-Games durchgeführt. Das Spiel wurde so entworfen, dass der Nutzer motiviert war die vorgegebenen Gesten möglichst genau durchzuführen. Basis für das Spielprinzip war die Kontroll-Studie, bei der die Probanden die Gesten wahrnehmen und reproduzieren mussten. Um eine große Menge an Touch-Proben zu sammeln wurde das Spiel in einem App-Store für mobile Geräte veröffentlicht und die Ergebnisse über das Internet eingesammelt. Die Analyse beider Studien (Kontroll-Studie und Spiel-Studie) ergab, dass die Rotation und die Winkel innerhalb einer Geste eine Auswirkung auf die Genauigkeit der Ausführung haben. Dieser Effekt wurde an Variationen in der Biegung, der Winkel und der Form der Gesten beobachtet. Dabei wurden die Gesten von den Nutzern des Spiels (d.h. in der Spiel-Studie) ungenauer durchgeführt als in der Kontroll-Studie

A Fine Motor Skill Classifying Framework to Support Children's Self-Regulation Skills and School Readiness

Children’s self-regulation skills predict their school-readiness and social behaviors, and assessing these skills enables parents and teachers to target areas for improvement or prepare children to enter school ready to learn and achieve. Assessing these skills enables parents and teachers to target areas for improvement or prepare children to enter school ready to learn and achieve. To assess children’s fine motor skills, current educators are assessing those skills by either determining their shape drawing correctness or measuring their drawing time durations through paper-based assessments. However, the methods involve human experts manually assessing children’s fine motor skills, which are time consuming and prone to human error and bias. As there are many children that use sketch-based applications on mobile and tablet devices, computer-based fine motor skill assessment has high potential to solve the limitations of the paper-based assessments. Furthermore, sketch recognition technology is able to offer more detailed, accurate, and immediate drawing skill information than the paper-based assessments such as drawing time or curvature difference. While a number of educational sketch applications exist for teaching children how to sketch, they are lacking the ability to assess children’s fine motor skills and have not proved the validity of the traditional methods onto tablet-environments. We introduce our fine motor skill classifying framework based on children’s digital drawings on tablet-computers. The framework contains two fine motor skill classifiers and a sketch-based educational interface (EasySketch). The fine motor skill classifiers contain: (1) KimCHI: the classifier that determines children’s fine motor skills based on their overall drawing skills and (2) KimCHI2: the classifier that determines children’s fine motor skills based on their curvature- and corner-drawing skills. Our fine motor skill classifiers determine children’s fine motor skills by generating 131 sketch features, which can analyze their drawing ability (e.g. DCR sketch feature can determine their curvature-drawing skills). We first implemented the KimCHI classifier, which can determine children’s fine motor skills based on their overall drawing skills. From our evaluation with 10- fold cross-validation, we found that the classifier can determine children’s fine motor skills with an f-measure of 0.904. After that, we implemented the KimCHI2 classifier, which can determine children’s fine motor skills based on their curvature- and corner-drawing skills. From our evaluation with 10-fold cross-validation, we found that the classifier can determine children’s curvature-drawing skills with an f-measure of 0.82 and corner-drawing skills with an f-measure of 0.78. The KimCHI2 classifier outperformed the KimCHI classifier during the fine motor skill evaluation. EasySketch is a sketch-based educational interface that (1) determines children’s fine motor skills based on their drawing skills and (2) assists children how to draw basic shapes such as alphabet letters or numbers based on their learning progress. When we evaluated our interface with children, our interface determined children’s fine motor skills more accurately than the conventional methodology by f-measures of 0.907 and 0.744, accordingly. Furthermore, children improved their drawing skills from our pedagogical feedback. Finally, we introduce our findings that sketch features (DCR and Polyline Test) can explain children’s fine motor skill developmental stages. From the sketch feature distributions per each age group, we found that from age 5 years, they show notable fine motor skill development

Estimating the Perceived Difficulty of Pen Gestures

Part 1: Long and Short PapersInternational audienceOur empirical results show that users perceive the execution difficulty of single stroke gestures consistently, and execution difficulty is highly correlated with gesture production time. We use these results to design two simple rules for estimating execution difficulty: establishing the relative ranking of difficulty among multiple gestures; and classifying a single gesture into five levels of difficulty. We confirm that the CLC model does not provide an accurate prediction of production time magnitude, and instead show that a reasonably accurate estimate can be calculated using only a few gesture execution samples from a few people. Using this estimated production time, our rules, on average, rank gesture difficulty with 90% accuracy and rate gesture difficulty with 75% accuracy. Designers can use our results to choose application gestures, and researchers can build on our analysis in other gesture domains and for modeling gesture performance