Ahmed Sabbir Arif

Analysis of text entry performance metrics

Researchers have proposed many text entry systems to enable users to perform this frequent task as quickly and precise as possible. Unfortunately the reported data varies widely and it is difficult to extract meaningful average entry speeds and error rates from this body of work. In this article we collect data from well-designed and well-reported experiments for the most important text entry methods, including those for handheld devices. Our survey results show that thumb keyboard is the fastest text entry method after the standard QWERTY keyboard, and that Twiddler is fastest amongst non-QWERTY methods. Moreover, we survey how text entry errors were handled in these studies. Finally, we conducted a user study to detect which effect different error-handling methodologies have on text entry performance metrics. Our study results show that the way human errors are handled has indeed a significant effect on all frequently used error metrics.

Predicting the cost of error correction in character-based text entry technologies

Researchers have developed many models to predict and understand human performance in text entry. Most of the models are specific to a technology or fail to account for human factors and variations in system parameters, and the relationship between them. Moreover, the process of fixing errors and its effects on text entry performance has not been studied. Here, we first analyze real-life text entry error correction behaviors. We then use our findings to develop a new model to predict the cost of error correction for character-based text entry technologies. We validate our model against quantities derived from the literature, as well as with a user study. Our study shows that the predicted and observed cost of error correction correspond well. At the end, we discuss potential applications of our new model.

A comparative evaluation of touch and pen gestures for adult and child users

In this paper, we present results of two user studies that compared the performance of touch-based and pen-based gesture input on capacitive touchscreens for both adult and 8-11 years old child users. Results showed that inputting gestures with pen was significantly faster and more accurate than touch for adult users. However, no significant effect of input method was observed on performance for child users. Similarly, user experience evaluation showed that a large number of adult users favoured one technique over the other and/or found a technique more comfortable to use than the other, while child users were mostly neutral. This trend, however, was not statistically significant.

Pseudo-pressure detection and its use in predictive text entry on touchscreens

In this article we first present a new hybrid technique that combines existing time- and touch-point-based approaches to simulate pressure detection on standard touchscreens. Results of two user studies show that the new hybrid technique can distinguish (at least) two pressure levels, where the first requires on average 1.04 N and the second 3.24 N force on the surface. Then, we present a novel pressure-based predictive text entry technique that utilizes our hybrid pressure detection to enable users to bypass incorrect predictions by applying extra pressure on the next key. For inputting short English phrases with 10% non-dictionary words a comparison with conventional text entry in a study showed that the new technique increases entry speed by 9% and decreases error rates by 25%. Also, most users (83%) favour the new technique.

A tap and gesture hybrid method for authenticating smartphone users

This paper presents a new tap and gesture hybrid method for authenticating mobile device users. The new technique augments four simple gestures - up, down, left, and right, to the dominant digit lock technique, allowing users to either tap or perform any one of the four gestures on the digit keys. It offers in total 6250000 unique four-symbol password combinations, which is substantially more than the conventional techniques. Results of a pilot study showed that the new technique was slower and more error prone than the digit lock technique. However, we believe with practice it could get faster and more accurate. Also, most users were comfortable and all of them felt more secured while using the new technique.

A Survey of Text Entry Techniques for Smartwatches

The growing interest in wearable devices has resulted in a wave of novel and improved text entry techniques for smartwatches and other ultra-small devices. These techniques are not only diverse in nature but also evaluated in different experimental conditions. This makes it difficult for designers and researchers to compare the techniques, and their performances in terms of speed and accuracy. This paper reviews the most important text entry techniques for smartwatches and other ultra-small devices. It categorizes all techniques based on whether they use a variant of the standard Qwerty keyboard, a novel keypad or keyboard, or handwriting recognition, and discusses the design and evaluation of the techniques. It includes a table that displays the performances of these techniques in the most common text entry performance metrics.

Error behaviours in an unreliable in-air gesture recognizer

This article presents results of two pilot studies that investigated error behaviours with an unreliable in-air gesture recognizer. During the studies, users performed a small set of simple in-air gestures. In the first study, these gestures were abstract. The second study associated concrete tasks with each gesture. The error patterns in the two studies were substantially different.

Learning New Words and Spelling with Autocorrections

We present a novel color-coded feedback method that highlights the types of corrections made by autocorrections. We then present results of a longitudinal user study with 7-8-year-old children that compared the new method with the conventional autocorrection feedback method, in terms of leaning new words and spelling. Results suggested that the new method better accommodates learning new words. Interestingly, learning was observed with the conventional feedback method as well, demanding further investigation into whether predictive methods are truly a barrier to learning new words and spelling.

Text Entry on the Edge

The primary focus of our workshop is to challenge the expanding text entry community to move beyond the mobile phone and start exploring novel and emerging technologies, designing systems for non-traditional users, and expand into unexplored domains and contexts of use. We hope to engage in setting a new agenda for our research community through the identification, collection, and presentation of text entry edge cases. As such, our workshop has two specific foci. First, to strengthen the text entry community by bringing text entry researchers working in various disciplines together in hopes of sharing knowledge across disciplines and establishing a set of best practices that can be used to build our community. Second, to set a research agenda around these edge cases that can be used to drive the field forwards and unite the field in a common direction so that our combined efforts can help bring novel and impactful text entry solutions to new and emerging technologies as well as underserved communities of users and research domains.

Active Pathways: Using Active Tangibles and Interactive Tabletops for Collaborative Modeling in Systems Biology

We present Active Pathways, an active tangible and tabletop system that aims to support collaborative discovery and learning in biochemical modeling. Our work extends ideas from embodied cognition that suggest that interactive systems that support model building by coupling actions made with the motor system to the dynamic properties of external and internal models can enhance the potential for insight and discoveries. We describe the motivation for our work and the design and development of our system. We also present the results of a user study with pairs of novice modelers, which suggest that our system is not only easy to use and learn but also successful at facilitating an understanding of complex systems and supporting collaboration.