Daniel Avrahami

Flowers or a robot army?: encouraging awareness & activity with personal, mobile displays

Personal, mobile displays, such as those on mobile phones, are ubiquitous, yet for the most part, underutilized. We present results from a field experiment that investigated the effectiveness of these displays as a means for improving awareness of daily life (in our case, self-monitoring of physical activity). Twenty-eight participants in three experimental conditions used our UbiFit system for a period of three months in their day-to-day lives over the winter holiday season. Our results show, for example, that participants who had an awareness display were able to maintain their physical activity level (even during the holidays), while the level of physical activity for participants who did not have an awareness display dropped significantly. We discuss our results and their general implications for the use of everyday mobile devices as awareness displays.

Predicting human interruptibility with sensors

A person seeking another persons 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.

Predicting human interruptibility with sensors: a Wizard of Oz feasibility study

A person seeking someone elses 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, todays 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.

Why and why not explanations improve the intelligibility of context-aware intelligent systems

Context-aware intelligent systems employ implicit inputs, and make decisions based on complex rules and machine learning models that are rarely clear to users. Such lack of system intelligibility can lead to loss of user trust, satisfaction and acceptance of these systems. However, automatically providing explanations about a systems decision process can help mitigate this problem. In this paper we present results from a controlled study with over 200 participants in which the effectiveness of different types of explanations was examined. Participants were shown examples of a systems operation along with various automatically generated explanations, and then tested on their understanding of the system. We show, for example, that explanations describing why the system behaved a certain way resulted in better understanding and stronger feelings of trust. Explanations describing why the system did not behave a certain way, resulted in lower understanding yet adequate performance. We discuss implications for the use of our findings in real-world context-aware applications.

Bonfire: a nomadic system for hybrid laptop-tabletop interaction

We present Bonfire, a self-contained mobile computing system that uses two laptop-mounted laser micro-projectors to project an interactive display space to either side of a laptop keyboard. Coupled with each micro-projector is a camera to enable hand gesture tracking, object recognition, and information transfer within the projected space. Thus, Bonfire is neither a pure laptop system nor a pure tabletop system, but an integration of the two into one new nomadic computing platform. This integration (1) enables observing the periphery and responding appropriately, e.g., to the casual placement of objects within its field of view, (2) enables integration between physical and digital objects via computer vision, (3) provides a horizontal surface in tandem with the usual vertical laptop display, allowing direct pointing and gestures, and (4) enlarges the input/output space to enrich existing applications. We describe Bonfires architecture, and offer scenarios that highlight Bonfires advantages. We also include lessons learned and insights for further development and use.

Responsiveness in instant messaging: predictive models supporting inter-personal communication

For the majority of us, inter-personal communication is an essential part of our daily lives. Instant Messaging, or IM, has been growing in popularity for personal and work-related communication. The low cost of sending a message, combined with the limited awareness provided by current IM systems result in messages often arriving at inconvenient or disruptive times. In a step towards solving this problem, we created statistical models that successfully predict responsiveness to incoming instant messages -- simply put: whether the receiver is likely to respond to a message within a certain time period. These models were constructed using a large corpus of real IM interaction collected from 16 participants, including over 90,000 messages. The models we present can predict, with accuracy as high as 90.1%, whether a message sent to begin a new session of communication would get a response within 30 seconds, 1, 2, 5, and 10 minutes. This type of prediction can be used, for example, to drive online-status indicators, or in services aimed at finding potential communicators.

Communication characteristics of instant messaging: effects and predictions of interpersonal relationships

Instant Messaging is a popular medium for both social and work-related communication. In this paper we report an investigation of the effect of interpersonal relationship on underlying basic communication characteristics (such as messaging rate and duration) using a large corpus of instant messages. Our results show that communication characteristics differ significantly for communications between users who are in a work relationship and between users who are in a social relationship. We used our findings to inform the creation of statistical models that predict the relationship between users without the use of message content -- achieving an accuracy of nearly 80% for one such model. We discuss the results of our analyses and potential uses of these models.

QnA: augmenting an instant messaging client to balance user responsiveness and performance

The growing use of Instant Messaging for social and work-related communication has created a situation where incoming messages often become a distraction to users while they are performing important tasks. Staying on task at the expense of responsiveness to IM buddies may portray the users as impolite or even rude. Constantly attending to IM, on the other hand, may prevent users from performing tasks efficiently, leaving them frustrated. In this paper we present a tool that augments a commercial IM client by automatically increasing the salience of incoming messages that may deserve immediate attention, helping users decide whether or not to stay on task.

The haptic laser: multi-sensation tactile feedback for at-a-distance physical space perception and interaction

We present the Haptic Laser, a system for providing a range of tactile sensations to represent a physical environment at-a-distance. The Haptic Laser is a handheld device that simulates interaction with physical surfaces as a user targets objects of interest (e.g., a light switch, TV, etc). Using simple computer vision techniques for scene analysis and laser range finding for calculating distance, the Haptic Laser extracts information about the physical environment and conveys it haptically through a collection of hardware actuators. Pointing the Haptic Laser around a room, for example, presents the user with information about the presence of objects, transitions, and edges through touch rather than, or in addition to, vision. The Haptic Laser extends current work on haptic touch screens and pens, and is designed to allow for haptic feedback from a distance using multiple feedback channels.

Taking 5: Work-Breaks, Productivity, and Opportunities for Personal Informatics for Knowledge Workers

Taking breaks from work is an essential and universal practice. In this paper, we extend current research on productivity in the workplace to consider the break habits of knowledge workers and explore opportunities of break logging for personal informatics. We report on three studies. Through a survey of 147 U.S.-based knowledge workers, we investigate what activities respondents consider to be breaks from work, and offer an understanding of the benefit workers desire when they take breaks. We then present results from a two-week in-situ diary study with 28 participants in the U.S. who logged 800 breaks, offering insights into the effect of work breaks on productivity. We finally explore the space of information visualization of work breaks and productivity in a third study. We conclude with a discussion of implications for break recommendation systems, availability and interuptibility research, and the quantified workplace.