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

Predicting and Reducing the Impact of Errors in Character-Based Text Entry

This dissertation focuses on the effect of errors in character-based text entry techniques. The effect of errors is targeted from theoretical, behavioral, and practical standpoints. This document starts with a review of the existing literature. It then presents results of a user study that investigated the effect of different error correction conditions on popular text entry performance metrics. Results showed that the way errors are handled has a significant effect on all frequently used error metrics. The outcomes also provided an understanding of how users notice and correct errors. Building on this, the dissertation then presents a new high-level and method-agnostic model for predicting the cost of error correction with a given text entry technique. Unlike the existing models, it accounts for both human and system factors and is general enough to be used with most character-based techniques. A user study verified the model through measuring the effects of a faulty keyboard on text entry performance. Subsequently, the work then explores the potential user adaptation to a gesture recognizer’s misrecognitions in two user studies. Results revealed that users gradually adapt to misrecognition errors by replacing the erroneous gestures with alternative ones, if available. Also, users adapt to a frequently misrecognized gesture faster if it occurs more frequently than the other error-prone gestures. Finally, this work presents a new hybrid approach to simulate pressure detection on standard touchscreens. The new approach combines the existing touch-point- and time-based methods. Results of two user studies showed that it can simulate pressure detection more reliably for at least two pressure levels: regular (~1 N) and extra (~3 N). Then, a new pressure-based text entry technique is presented that does not require tapping outside the virtual keyboard to reject an incorrect or unwanted prediction. Instead, the technique requires users to apply extra pressure for the tap on the next target key. The performance of the new technique was compared with the conventional technique in a user study. Results showed that for inputting short English phrases with 10% non-dictionary words, the new technique increases entry speed by 9% and decreases error rates by 25%. Also, most users (83%) favor the new technique over the conventional one. Together, the research presented in this dissertation gives more insight into on how errors affect text entry and also presents improved text entry methods

Understanding Mode and Modality Transfer in Unistroke Gesture Input

Unistroke gestures are an attractive input method with an extensive research history, but one challenge with their usage is that the gestures are not always self-revealing. To obtain expertise with these gestures, interaction designers often deploy a guided novice mode -- where users can rely on recognizing visual UI elements to perform a gestural command. Once a user knows the gesture and associated command, they can perform it without guidance; thus, relying on recall. The primary aim of my thesis is to obtain a comprehensive understanding of why, when, and how users transfer from guided modes or modalities to potentially more efficient, or novel, methods of interaction -- through symbolic-abstract unistroke gestures. The goal of my work is to not only study user behaviour from novice to more efficient interaction mechanisms, but also to expand upon the concept of intermodal transfer to different contexts. We garner this understanding by empirically evaluating three different use cases of mode and/or modality transitions. Leveraging marking menus, the first piece investigates whether or not designers should force expertise transfer by penalizing use of the guided mode, in an effort to encourage use of the recall mode. Second, we investigate how well users can transfer skills between modalities, particularly when it is impractical to present guidance in the target or recall modality. Lastly, we assess how well users' pre-existing spatial knowledge of an input method (the QWERTY keyboard layout), transfers to performance in a new modality. Applying lessons from these three assessments, we segment intermodal transfer into three possible characterizations -- beyond the traditional novice to expert contextualization. This is followed by a series of implications and potential areas of future exploration spawning from our work

Optimizing Human Performance in Mobile Text Entry

Although text entry on mobile phones is abundant, research strives to achieve desktop typing performance "on the go". But how can researchers evaluate new and existing mobile text entry techniques? How can they ensure that evaluations are conducted in a consistent manner that facilitates comparison? What forms of input are possible on a mobile device? Do the audio and haptic feedback options with most touchscreen keyboards affect performance? What influences users' preference for one feedback or another? Can rearranging the characters and keys of a keyboard improve performance? This dissertation answers these questions and more. The developed TEMA software allows researchers to evaluate mobile text entry methods in an easy, detailed, and consistent manner. Many in academia and industry have adopted it. TEMA was used to evaluate a typical QWERTY keyboard with multiple options for audio and haptic feedback. Though feedback did not have a significant effect on performance, a survey revealed that users' choice of feedback is influenced by social and technical factors. Another study using TEMA showed that novice users entered text faster using a tapping technique than with a gesture or handwriting technique. This motivated rearranging the keys and characters to create a new keyboard, MIME, that would provide better performance for expert users. Data on character frequency and key selection times were gathered and used to design MIME. A longitudinal user study using TEMA revealed an entry speed of 17 wpm and a total error rate of 1.7% for MIME, compared to 23 wpm and 5.2% for QWERTY. Although MIME's entry speed did not surpass QWERTY's during the study, it is projected to do so after twelve hours of practice. MIME's error rate was consistently low and significantly lower than QWERTY's. In addition, participants found MIME more comfortable to use, with some reporting hand soreness after using QWERTY for extended periods

Mid-air haptic rendering of 2D geometric shapes with a dynamic tactile pointer

An important challenge that affects ultrasonic midair haptics, in contrast to physical touch, is that we lose certain exploratory procedures such as contour following. This makes the task of perceiving geometric properties and shape identification more difficult. Meanwhile, the growing interest in mid-air haptics and their application to various new areas requires an improved understanding of how we perceive specific haptic stimuli, such as icons and control dials in mid-air. We address this challenge by investigating static and dynamic methods of displaying 2D geometric shapes in mid-air. We display a circle, a square, and a triangle, in either a static or dynamic condition, using ultrasonic mid-air haptics. In the static condition, the shapes are presented as a full outline in mid-air, while in the dynamic condition, a tactile pointer is moved around the perimeter of the shapes. We measure participants’ accuracy and confidence of identifying shapes in two controlled experiments (n1 = 34, n2 = 25). Results reveal that in the dynamic condition people recognise shapes significantly more accurately, and with higher confidence. We also find that representing polygons as a set of individually drawn haptic strokes, with a short pause at the corners, drastically enhances shape recognition accuracy. Our research supports the design of mid-air haptic user interfaces in application scenarios such as in-car interactions or assistive technology in education

WatchTrace: Design and Evaluation of an At-Your-Side Gesture Paradigm

In this thesis, we present the exploration and evaluation of a gesture interaction paradigm performed with arms at rest at the side of one's body. This gesture stance is informed persisting challenges in mid-air arm gesture interactions in relation to fatigue and social acceptability. The proposed arms-down posture reduces physical effort by minimizing the shoulder torque placed on the user. While this interaction posture has been previously explored, the gesture vocabulary in previous research has been small and limited. The design of this gesture interaction is motivated by the ability to provide rich and expressive input; the user performs gestures by moving the whole arm at the side of the body to create two-dimensional visual traces, as in hand-drawing in a bounded plane parallel to the ground. Within this space, we present the results of two studies that investigate the use of side-gesture input for interaction. First, we explore the users' mental model for using this interaction by conducting an elicitation study on a set of everyday tasks one would perform on a large display in public to semi-public contexts. The takeaway from this study presents the need for a dynamic and expressive set of gesture vocabulary including ideographic and alphanumeric gesture constructs that can be combined or chained together. We then explore the feasibility of designing such a gesture recognition system using commodity hardware and recognition techniques, dubbed WatchTrace, which supports alphanumeric gestures of up to length three, providing a vibrant, dynamic, and feasible gestural vocabulary. Finally, we explore potential approaches to improve the recognition through the use of adaptive thresholds, n-best lists, and changing reject rates among other conventional techniques in the field of gesture classification

Wearable Smart Rings for Multi-Finger Gesture Recognition Using Supervised Learning

This thesis presents a wearable, smart ring with an integrated Bluetooth low-energy (BLE) module. The system uses an accelerometer and a gyroscope to collect fingers motion data. A prototype was manufactured, and its performance was tested. To detect complex finger movements, two rings are worn on the point and thumb fingers while performing the gestures. Nine pre-defined finger movements were introduced to verify the feasibility of the proposed method. Data pre-processing techniques, including normalization, statistical feature extraction, random forest recursive feature elimination (RF-RFE), and k-nearest neighbors sequential forward floating selection (KNN-SFFS), were applied to select well-distinguished feature vectors to enhance gesture recognition accuracy. Three supervised machine learning algorithms were used for gesture classification purposes, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes (NB). We demonstrated that when utilizing the KNN-SFFS recommended features as the machine learning input, our proposed finger gesture recognition approach not only significantly decreases the dimension of the feature vector, results in faster response time and prevents overfitted model, but also provides approximately similar machine learning prediction accuracy compared to when all elements of feature vectors were used. By using the KNN as the primary classifier, the system can accurately recognize six one-finger and three two-finger gestures with 97.1% and 97.0% accuracy, respectively

Contributions to Pen & Touch Human-Computer Interaction

[EN] Computers are now present everywhere, but their potential is not fully exploited due to some lack of acceptance. In this thesis, the pen computer paradigm is adopted, whose main idea is to replace all input devices by a pen and/or the fingers, given that the origin of the rejection comes from using unfriendly interaction devices that must be replaced by something easier for the user. This paradigm, that was was proposed several years ago, has been only recently fully implemented in products, such as the smartphones. But computers are actual illiterates that do not understand gestures or handwriting, thus a recognition step is required to "translate" the meaning of these interactions to computer-understandable language. And for this input modality to be actually usable, its recognition accuracy must be high enough. In order to realistically think about the broader deployment of pen computing, it is necessary to improve the accuracy of handwriting and gesture recognizers. This thesis is devoted to study different approaches to improve the recognition accuracy of those systems. First, we will investigate how to take advantage of interaction-derived information to improve the accuracy of the recognizer. In particular, we will focus on interactive transcription of text images. Here the system initially proposes an automatic transcript. If necessary, the user can make some corrections, implicitly validating a correct part of the transcript. Then the system must take into account this validated prefix to suggest a suitable new hypothesis. Given that in such application the user is constantly interacting with the system, it makes sense to adapt this interactive application to be used on a pen computer. User corrections will be provided by means of pen-strokes and therefore it is necessary to introduce a recognizer in charge of decoding this king of nondeterministic user feedback. However, this recognizer performance can be boosted by taking advantage of interaction-derived information, such as the user-validated prefix. Then, this thesis focuses on the study of human movements, in particular, hand movements, from a generation point of view by tapping into the kinematic theory of rapid human movements and the Sigma-Lognormal model. Understanding how the human body generates movements and, particularly understand the origin of the human movement variability, is important in the development of a recognition system. The contribution of this thesis to this topic is important, since a new technique (which improves the previous results) to extract the Sigma-lognormal model parameters is presented. Closely related to the previous work, this thesis study the benefits of using synthetic data as training. The easiest way to train a recognizer is to provide "infinite" data, representing all possible variations. In general, the more the training data, the smaller the error. But usually it is not possible to infinitely increase the size of a training set. Recruiting participants, data collection, labeling, etc., necessary for achieving this goal can be time-consuming and expensive. One way to overcome this problem is to create and use synthetically generated data that looks like the human. We study how to create these synthetic data and explore different approaches on how to use them, both for handwriting and gesture recognition. The different contributions of this thesis have obtained good results, producing several publications in international conferences and journals. Finally, three applications related to the work of this thesis are presented. First, we created Escritorie, a digital desk prototype based on the pen computer paradigm for transcribing handwritten text images. Second, we developed "Gestures à Go Go", a web application for bootstrapping gestures. Finally, we studied another interactive application under the pen computer paradigm. In this case, we study how translation reviewing can be done more ergonomically using a pen.[ES] Hoy en día, los ordenadores están presentes en todas partes pero su potencial no se aprovecha debido al "miedo" que se les tiene. En esta tesis se adopta el paradigma del pen computer, cuya idea fundamental es sustituir todos los dispositivos de entrada por un lápiz electrónico o, directamente, por los dedos. El origen del rechazo a los ordenadores proviene del uso de interfaces poco amigables para el humano. El origen de este paradigma data de hace más de 40 años, pero solo recientemente se ha comenzado a implementar en dispositivos móviles. La lenta y tardía implantación probablemente se deba a que es necesario incluir un reconocedor que "traduzca" los trazos del usuario (texto manuscrito o gestos) a algo entendible por el ordenador. Para pensar de forma realista en la implantación del pen computer, es necesario mejorar la precisión del reconocimiento de texto y gestos. El objetivo de esta tesis es el estudio de diferentes estrategias para mejorar esta precisión. En primer lugar, esta tesis investiga como aprovechar información derivada de la interacción para mejorar el reconocimiento, en concreto, en la transcripción interactiva de imágenes con texto manuscrito. En la transcripción interactiva, el sistema y el usuario trabajan "codo con codo" para generar la transcripción. El usuario valida la salida del sistema proporcionando ciertas correcciones, mediante texto manuscrito, que el sistema debe tener en cuenta para proporcionar una mejor transcripción. Este texto manuscrito debe ser reconocido para ser utilizado. En esta tesis se propone aprovechar información contextual, como por ejemplo, el prefijo validado por el usuario, para mejorar la calidad del reconocimiento de la interacción. Tras esto, la tesis se centra en el estudio del movimiento humano, en particular del movimiento de las manos, utilizando la Teoría Cinemática y su modelo Sigma-Lognormal. Entender como se mueven las manos al escribir, y en particular, entender el origen de la variabilidad de la escritura, es importante para el desarrollo de un sistema de reconocimiento, La contribución de esta tesis a este tópico es importante, dado que se presenta una nueva técnica (que mejora los resultados previos) para extraer el modelo Sigma-Lognormal de trazos manuscritos. De forma muy relacionada con el trabajo anterior, se estudia el beneficio de utilizar datos sintéticos como entrenamiento. La forma más fácil de entrenar un reconocedor es proporcionar un conjunto de datos "infinito" que representen todas las posibles variaciones. En general, cuanto más datos de entrenamiento, menor será el error del reconocedor. No obstante, muchas veces no es posible proporcionar más datos, o hacerlo es muy caro. Por ello, se ha estudiado como crear y usar datos sintéticos que se parezcan a los reales. Las diferentes contribuciones de esta tesis han obtenido buenos resultados, produciendo varias publicaciones en conferencias internacionales y revistas. Finalmente, también se han explorado tres aplicaciones relaciones con el trabajo de esta tesis. En primer lugar, se ha creado Escritorie, un prototipo de mesa digital basada en el paradigma del pen computer para realizar transcripción interactiva de documentos manuscritos. En segundo lugar, se ha desarrollado "Gestures à Go Go", una aplicación web para generar datos sintéticos y empaquetarlos con un reconocedor de forma rápida y sencilla. Por último, se presenta un sistema interactivo real bajo el paradigma del pen computer. En este caso, se estudia como la revisión de traducciones automáticas se puede realizar de forma más ergonómica.[CA] Avui en dia, els ordinadors són presents a tot arreu i es comunament acceptat que la seva utilització proporciona beneficis. No obstant això, moltes vegades el seu potencial no s'aprofita totalment. En aquesta tesi s'adopta el paradigma del pen computer, on la idea fonamental és substituir tots els dispositius d'entrada per un llapis electrònic, o, directament, pels dits. Aquest paradigma postula que l'origen del rebuig als ordinadors prové de l'ús d'interfícies poc amigables per a l'humà, que han de ser substituïdes per alguna cosa més coneguda. Per tant, la interacció amb l'ordinador sota aquest paradigma es realitza per mitjà de text manuscrit i/o gestos. L'origen d'aquest paradigma data de fa més de 40 anys, però només recentment s'ha començat a implementar en dispositius mòbils. La lenta i tardana implantació probablement es degui al fet que és necessari incloure un reconeixedor que "tradueixi" els traços de l'usuari (text manuscrit o gestos) a alguna cosa comprensible per l'ordinador, i el resultat d'aquest reconeixement, actualment, és lluny de ser òptim. Per pensar de forma realista en la implantació del pen computer, cal millorar la precisió del reconeixement de text i gestos. L'objectiu d'aquesta tesi és l'estudi de diferents estratègies per millorar aquesta precisió. En primer lloc, aquesta tesi investiga com aprofitar informació derivada de la interacció per millorar el reconeixement, en concret, en la transcripció interactiva d'imatges amb text manuscrit. En la transcripció interactiva, el sistema i l'usuari treballen "braç a braç" per generar la transcripció. L'usuari valida la sortida del sistema donant certes correccions, que el sistema ha d'usar per millorar la transcripció. En aquesta tesi es proposa utilitzar correccions manuscrites, que el sistema ha de reconèixer primer. La qualitat del reconeixement d'aquesta interacció és millorada, tenint en compte informació contextual, com per exemple, el prefix validat per l'usuari. Després d'això, la tesi se centra en l'estudi del moviment humà en particular del moviment de les mans, des del punt de vista generatiu, utilitzant la Teoria Cinemàtica i el model Sigma-Lognormal. Entendre com es mouen les mans en escriure és important per al desenvolupament d'un sistema de reconeixement, en particular, per entendre l'origen de la variabilitat de l'escriptura. La contribució d'aquesta tesi a aquest tòpic és important, atès que es presenta una nova tècnica (que millora els resultats previs) per extreure el model Sigma- Lognormal de traços manuscrits. De forma molt relacionada amb el treball anterior, s'estudia el benefici d'utilitzar dades sintètiques per a l'entrenament. La forma més fàcil d'entrenar un reconeixedor és proporcionar un conjunt de dades "infinit" que representin totes les possibles variacions. En general, com més dades d'entrenament, menor serà l'error del reconeixedor. No obstant això, moltes vegades no és possible proporcionar més dades, o fer-ho és molt car. Per això, s'ha estudiat com crear i utilitzar dades sintètiques que s'assemblin a les reals. Les diferents contribucions d'aquesta tesi han obtingut bons resultats, produint diverses publicacions en conferències internacionals i revistes. Finalment, també s'han explorat tres aplicacions relacionades amb el treball d'aquesta tesi. En primer lloc, s'ha creat Escritorie, un prototip de taula digital basada en el paradigma del pen computer per realitzar transcripció interactiva de documents manuscrits. En segon lloc, s'ha desenvolupat "Gestures à Go Go", una aplicació web per a generar dades sintètiques i empaquetar-les amb un reconeixedor de forma ràpida i senzilla. Finalment, es presenta un altre sistema inter- actiu sota el paradigma del pen computer. En aquest cas, s'estudia com la revisió de traduccions automàtiques es pot realitzar de forma més ergonòmica.Martín-Albo Simón, D. (2016). Contributions to Pen & Touch Human-Computer Interaction [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/68482TESI