Target Acquisition in Multiscale Electronic Worlds

Since the advent of graphical user interfaces, electronic information has grown exponentially, whereas the size of screen displays has stayed almost the same. Multiscale interfaces were designed to address this mismatch, allowing users to adjust the scale at which they interact with information objects. Although the technology has progressed quickly, the theory has lagged behind. Multiscale interfaces pose a stimulating theoretical challenge, reformulating the classic target-acquisition problem from the physical world into an infinitely rescalable electronic world. We address this challenge by extending Fitts’ original pointing paradigm: we introduce the scale variable, thus defining a multiscale pointing paradigm. This article reports on our theoretical and empirical results. We show that target-acquisition performance in a zooming interface must obey Fitts’ law, and more specifically, that target-acquisition time must be proportional to the index of difficulty. Moreover, we complement Fitts’ law by accounting for the effect of view size on pointing performance, showing that performance bandwidth is proportional to view size, up to a ceiling effect. The first empirical study shows that Fitts’ law does apply to a zoomable interface for indices of difficulty up to and beyond 30 bits, whereas classical Fitts’ law studies have been confined in the 2-10 bit range. The second study demonstrates a strong interaction between view size and task difficulty for multiscale pointing, and shows a surprisingly low ceiling. We conclude with implications of these findings for the design of multiscale user interfaces

Collaborative Gaze Channelling for Improved Cooperation During Robotic Assisted Surgery

The use of multiple robots for performing complex tasks is becoming a common practice for many robot applications. When different operators are involved, effective cooperation with anticipated manoeuvres is important for seamless, synergistic control of all the end-effectors. In this paper, the concept of Collaborative Gaze Channelling (CGC) is presented for improved control of surgical robots for a shared task. Through eye tracking, the fixations of each operator are monitored and presented in a shared surgical workspace. CGC permits remote or physically separated collaborators to share their intention by visualising the eye gaze of their counterparts, and thus recovers, to a certain extent, the information of mutual intent that we rely upon in a vis-à-vis working setting. In this study, the efficiency of surgical manipulation with and without CGC for controlling a pair of bimanual surgical robots is evaluated by analysing the level of coordination of two independent operators. Fitts' law is used to compare the quality of movement with or without CGC. A total of 40 subjects have been recruited for this study and the results show that the proposed CGC framework exhibits significant improvement (p<0.05) on all the motion indices used for quality assessment. This study demonstrates that visual guidance is an implicit yet effective way of communication during collaborative tasks for robotic surgery. Detailed experimental validation results demonstrate the potential clinical value of the proposed CGC framework. © 2012 Biomedical Engineering Society.link_to_subscribed_fulltex

Shakespeareʼs Complete Works as a Benchmark for Evaluating Multiscale Document-Navigation Techniques

International audienceIn this paper, we describe an experimental platform dedicated to the comparative evaluation of multiscale electronic-document navigation techniques. One noteworthy characteristics of our platform is that it allows the user not only to translate the document (for example, to pan and zoom) but also to tilt the virtual camera to obtain freely chosen perspective views of the document. Second, the platform makes it possible to explore, with semantic zooming, the 150,000 verses that comprise the complete works of William Shakespeare. We argue that reaching and selecting one specific verse in this very large text corpus amounts to a perfectly well defined Fitts task, leading to rigorous assessments of target acquisition performance. For lack of a standard, the various multiscale techniques that have been reported recently in the literature are difficult to compare. We recommend that Shakespeare's complete works, converted into a single document that can be zoomed both geometrically and semantically, be used as a benchmark to facilitate systematic experimental comparisons, using Fitts' target acquisition paradigm

Effects of feedback, mobility and index of difficulty on deictic spatial audio target acquisition in the horizontal plane

We present the results of an empirical study investigating the effect of feedback, mobility and index of difficulty on a deictic spatial audio target acquisition task in the horizontal plane in front of a user. With audio feedback, spatial audio display elements are found to enable usable deictic interac-tion that can be described using Fitts law. Feedback does not affect perceived workload or preferred walking speed compared to interaction without feedback. Mobility is found to degrade interaction speed and accuracy by 20%. Participants were able to perform deictic spatial audio target acquisition when mobile while walking at 73% of their pre-ferred walking speed. The proposed feedback design is ex-amined in detail and the effects of variable target widths are quantified. Deictic interaction with a spatial audio display is found to be a feasible solution for future interface designs

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