Forward to the past

Our daily experience shows that the CNS is a highly efficient machine to predict the effect of actions into the future; are we so efficient also in reconstructing the past of an action? Previous studies demonstrated we are more effective in extrapolating the final position of a stimulus moving according to biological kinematic laws. Here we address the complementary question: are we more effective in extrapolating the starting position (SP) of a motion following a biological velocity profile? We presented a dot moving upward and corresponding to vertical arm movements that were masked in the first part of the trajectory. The stimulus could either move according to biological or non-biological kinematic laws of motion. Results show a better efficacy in reconstructing the SP of a natural motion: participants demonstrate to reconstruct coherently only the SP of the biological condition. When the motion violates the biological kinematic law, responses are scattered and show a tendency toward larger errors. Instead, in a control experiment where the full motions were displayed, no-difference between biological and non-biological motions is found. Results are discussed in light of potential mechanisms involved in visual inference. We propose that as soon as the target appears the cortical motor area would generate an internal representation of reaching movement. When the visual input and the stored kinematic template match, the SP is traced back on the basis of this memory template, making more effective the SP reconstruction

On Computer Mouse Pointing Model Online Identification and Endpoint Prediction

International audienceThis paper proposes a new simplified pointing model as a feedback-based dynamical system, including both human and computer sides of the process. It takes into account the commutation between the correction and ballistic phases in pointing tasks. We use the mouse position increment signal from noisy experimental data to achieve our main objectives: to estimate the model parameters online and predict the task endpoint. Some estimation tools and validation results, applying linear regression techniques on the experimental data are presented. We also compare with a similar prediction algorithm to show the potential of our algorithm's implementation

Exploiting behavioral biometrics for user security enhancements

As online business has been very popular in the past decade, the tasks of providing user authentication and verification have become more important than before to protect user sensitive information from malicious hands. The most common approach to user authentication and verification is the use of password. However, the dilemma users facing in traditional passwords becomes more and more evident: users tend to choose easy-to-remember passwords, which are often weak passwords that are easy to crack. Meanwhile, behavioral biometrics have promising potentials in meeting both security and usability demands, since they authenticate users by who you are , instead of what you have . In this dissertation, we first develop two such user verification applications based on behavioral biometrics: the first one is via mouse movements, and the second via tapping behaviors on smartphones; then we focus on modeling user web browsing behaviors by Fitts\u27 Law.;Specifically, we develop a user verification system by exploiting the uniqueness of people\u27s mouse movements. The key feature of our system lies in using much more fine-grained (point-by-point) angle-based metrics of mouse movements for user verification. These new metrics are relatively unique from person to person and independent of the computing platform. We conduct a series of experiments to show that the proposed system can verify a user in an accurate and timely manner, and induced system overhead is minor. Similar to mouse movements, the tapping behaviors of smartphone users on touchscreen also vary from person to person. We propose a non-intrusive user verification mechanism to substantiate whether an authenticating user is the true owner of the smartphone or an impostor who happens to know the passcode. The effectiveness of the proposed approach is validated through real experiments. to further understand user pointing behaviors, we attempt to stress-test Fitts\u27 law in the wild , namely, under natural web browsing environments, instead of restricted laboratory settings in previous studies. Our analysis shows that, while the averaged pointing times follow Fitts\u27 law very well, there is considerable deviations from Fitts\u27 law. We observe that, in natural browsing, a fast movement has a different error model from the other two movements. Therefore, a complete profiling on user pointing performance should be done in more details, for example, constructing different error models for slow and fast movements. as future works, we plan to exploit multiple-finger tappings for smartphone user verification, and evaluate user privacy issues in Amazon wish list

A forecasting algorithm for latency compensation in indirect human-computer interactions

International audienceHuman-computer interactions are greatly affected by the latency between the human input and the system visual response. The compensation of this latency is an important problem for the HCI (human-computer interaction) community. In this work, a simple forecasting algorithm is developed for latency compensation in indirect interaction using a mouse, based on numerical differentiation. Several differentiators are compared, including a novel algebraic version. An optimized procedure is developed for tuning the parameters of the algorithm. The efficiency is demonstrated on real data, measured with a 1 ms sampling time

Mental Blocks: The behavioural effects and neural encoding of obstacles when reaching and grasping

The ability to adeptly interact with a cluttered and dynamic world requires that the brain simultaneously encode multiple objects. Theoretical frameworks of selective visuomotor attention provide evidence for parallel encoding (Baldauf & Deubel, 2010; Cisek & Kalaska, 2010; Duncan, 2006) where concurrent object processing results in neural competition. Since the end goal of object representation is usually action, these frameworks argue that the competitive activity is best characterized as the development of visuomotor biases. While some behavioural and neural evidence has been accumulated in favour of this explanation, one of the most striking, yet deceptively common, demonstrations of this capacity is often overlooked; the movement of the arm away from an obstacle while reaching for a target object is definitive proof that both objects are encoded and affect behaviour. In the current thesis, I discuss three experiments exploring obstacle avoidance. While some previous studies have shown how visuomotor biases develop prior to movement onset, the dynamics of the bias during movement remains largely unexplored. In the first experiment I use the availability and predictability of vision during movement as a means of exploring whether obstacle representations might change during a reach (Chapter 2, Chapman & Goodale, 2010b). While the visuomotor system seems optimized to use vision, I found no difference between reaching with and without vision, providing no evidence that obstacle representations were altered. To more directly test this question, in the second experiment participants made reaches to a target that sometimes changed position during the reach (Chapter 3, Chapman & Goodale, 2010a). The automatic online corrections to the new target location were sometimes interfered with by an obstacle. Using this more direct approach we found definitive evidence that obstacle representations were accessed or updated during movement. In the third experiment, I directly tested the neural encoding of obstacles using functional magnetic resonance imaging (Chapter 4, Chapman, Gallivan, Culham, & Goodale, 2010). When participants planned a grasp movement that was interfered with by an obstacle versus when the grasp was not interfered with, one area in the left posterior intraparietal sulcus was activated. This activity was concurrent with a suppression of early visual areas that were responsive to the position of the obstacle. This study confirmed that the PPC was involved with the encoding of obstacles, and demonstrated that one effect of interference was the suppression of the visual cortical signal associated with the obstacle. These findings extend our understanding of competitive visuomotor biases. Critically, in a world filled with potential action targets, the selection of one target necessarily means all other objects in the workspace are potential obstacles. My results indicate that the visuomotor biasing signal to inhibit obstacle activity is putatively provided by the PPC, which in turn causes the visual cortical representation of the obstacle to be suppressed. The behavioural result of biasing the visual input is the propagation of this suppression to the motor output - ultimately resulting in a reach which intelligently deviates away from potential obstacles

Typing performance of blind users:an analysis of touch behaviors, learning effect, and in-situ usage

Non-visual text-entry for people with visual impairments has focused mostly on the comparison of input techniques reporting on performance measures, such as accuracy and speed. While researchers have been able to establish that non-visual input is slow and error prone, there is little understanding on how to improve it. To develop a richer characterization of typing performance, we conducted a longitudinal study with five novice blind users. For eight weeks, we collected in-situ usage data and conducted weekly laboratory assessment sessions. This paper presents a thorough analysis of typing performance that goes beyond traditional aggregated measures of text-entry and reports on character-level errors and touch measures. Our findings show that users improve over time, even though it is at a slow rate (0.3 WPM per week). Substitutions are the most common type of error and have a significant impact on entry rates. In addition to text input data, we analyzed touch behaviors, looking at touch contact points, exploration movements, and lift positions. We provide insights on why and how performance improvements and errors occur. Finally, we derive some implications that should inform the design of future virtual keyboards for non-visual input. Categories and Subject Descriptors H.5.2 [Information Interfaces and Presentation]: User Interfaces- Input devices and strategies. K4.2 [Computers an

Next-Point Prediction Metrics for Perceived Spatial Errors

International audienceTouch screens have a delay between user input and corresponding visual interface feedback, called input “latency” (or “lag”). Visual latency is more noticeable during continuous input actions like dragging, so methods to display feedback based on the most likely path for the next few input points have been described in research papers and patents. Designing these “next-point prediction” methods is challenging, and there have been no standard metrics to compare different approaches. We introduce metrics to quantify the probability of 7 spatial error “side-effects” caused by next-point prediction methods. Types of side-effects are derived using a thematic analysis of comments gathered in a 12 participants study covering drawing, dragging, and panning tasks using 5 state-of- the-art next-point predictors. Using experiment logs of actual and predicted input points, we develop quantitative metrics that correlate positively with the frequency of perceived side-effects. These metrics enable practitioners to compare next- point predictors using only input logs

Operating Different Displays in Military Fast Jets Using Eye Gaze Tracker

This paper investigated the use of an eye-gaze-controlled interface in a military aviation environment. We set up a flight simulator and used the gaze-controlled interface in three different configurations of displays (head down, head up, and head mounted) for military fast jets. Our studies found that the gaze-controlled interface statistically significantly increased the speed of interaction for secondary mission control tasks compared to touchscreen- and joystick-based target designation system. Finally, we tested a gaze-controlled system inside an aircraft both on the ground and in different phases of flight with military pilots. Results showed that they could undertake representative pointing and selection tasks in less than two seconds, on average

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