The normal effects of aging include some decline in cognitive, perceptual, and motor abilities. This can have a negative effect on the performance of a number of tasks, including basic pointing and selection tasks common to today's graphical user interfaces. This paper describes a study of the effectiveness of two interaction techniques: area cursors and sticky icons, in improving the performance of older adults in basic selection tasks. The study described here indicates that when combined, these techniques can decrease target selection times for older adults by as much as 50°/0 when applied to the most difficult cases (smallest selection targets).At the same time these techniques are shown not to impede performance in cases known to be problematical for related techniques (e.g., differentiation between closely spaced targets) and to provide similar but smaller benefits for younger users.

A person seeking another person's attention is normally able to quickly assess how interruptible the other person currently is. Such assessments allow behavior that we consider natural, socially appropriate, or simply polite. This is in sharp contrast to current computer and communication systems, which are largely unaware of the social situations surrounding their usage and the impact that their actions have on these situations. If systems could model human interruptibility, they could use this information to negotiate interruptions at appropriate times, thus improving human computer interaction.This article presents a series of studies that quantitatively demonstrate that simple sensors can support the construction of models that estimate human interruptibility as well as people do. These models can be constructed without using complex sensors, such as vision-based techniques, and therefore their use in everyday office environments is both practical and affordable. Although currently based on a demographically limited sample, our results indicate a substantial opportunity for future research to validate these results over larger groups of office workers. Our results also motivate the development of systems that use these models to negotiate interruptions at socially appropriate times.

A user interface software tool helps developers design and implement the user interface. Research on past tools has had enormous impact on today's developers—virtually all applications today are built using some form of user interface tool. In this article, we consider cases of both success and failure in past user interface tools. From these cases we extract a set of themes which can serve as lessons for future work. Using these themes, past tools can be characterized by what aspects of the user interface they addressed, their threshold and ceiling, what path of least resistance they offer, how predictable they are to use, and whether they addressed a target that became irrelevant. We believe the lessons of these past themes are particularly important now, because increasingly rapid technological changes are likely to significantly change user interfaces. We are at the dawn of an era where user interfaces are about to break out of the “desktop” box where they have been stuck for the past 15 years. The next millenium will open with an increasing diversity of user interface on an increasing diversity of computerized devices. These devices include hand-held personal digital assistants (PDAs), cell phones, pages, computerized pens, computerized notepads, and various kinds of desk and wall size-computers, as well as devices in everyday objects (such as mounted on refridgerators, or even embedded in truck tires). The increased connectivity of computers, initially evidenced by the World Wide Web, but spreading also with technologies such as personal-area networks, will also have a profound effect on the user interface to computers. Another important force will be recognition-based user interfaces, especially speech, and camera-based vision systems. Other changes we see are an increasing need for 3D and end-user customization, programming, and scripting. All of these changes will require significant support from the underlying user interface sofware tools.

This paper describes a fundamental dual tradeoff that occurs in systems supporting awareness for distributed work groups, and presents several specific new techniques which illustrate good compromise points within this tradeoff space. This dual tradeoff is between privacy and awareness, and between awareness and disturbance. Simply stated, the more information about oneself that leaves your work area, the more potential for awareness of you exists for your colleagues. Unfortunately, this also represents the greatest potential for intrusion on your privacy.Similarly, the more information that is received about the activities of colleagues, the more potential awareness we have of them. However, at the same time, the more information we receive, the greater the chance that the information will become a disturbance to our normal work.This dual tradeoff seems to be a fundamental one. However, by carefully examining awareness problems in the light of this tradeoff it is possible to devise techniques which expose new points in the design space. These new points provide different types and quantities of information so that awareness can be achieved without invading the privacy of the sender, or creating a disturbance for the receiver. This paper presents four such techniques, each based on a careful selection of the information transmitted.

A person seeking someone else's attention is normally able to quickly assess how interruptible they are. This assessment allows for behavior we perceive as natural, socially appropriate, or simply polite. On the other hand, today's computer systems are almost entirely oblivious to the human world they operate in, and typically have no way to take into account the interruptibility of the user. This paper presents a Wizard of Oz study exploring whether, and how, robust sensor-based predictions of interruptibility might be constructed, which sensors might be most useful to such predictions, and how simple such sensors might be.The study simulates a range of possible sensors through human coding of audio and video recordings. Experience sampling is used to simultaneously collect randomly distributed self-reports of interruptibility. Based on these simulated sensors, we construct statistical models predicting human interruptibility and compare their predictions with the collected self-report data. The results of these models, although covering a demographically limited sample, are very promising, with the overall accuracy of several models reaching about 78%. Additionally, a model tuned to avoiding unwanted interruptions does so for 90% of its predictions, while retaining 75% overall accuracy.