Learning and reasoning about interruption

Horvitz, Eric; Apacible, Johnson

doi:10.1145/958439.958440

Cited by 58 publications

(77 citation statements)

References 6 publications

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“…Studies have shown that human interruption in offices can be captured accurately by simple sensors such as these [6,9], and other studies have found that users decide whether to answer their phones based on their activity, location, and who is callingall of which are becoming more observable using current phone sensors [7,15,16]. With new applications to classify interruption preferences and react based on these predictions, it is not clear what accuracy level is acceptable for users.…”

Section: Related Workmentioning

confidence: 99%

Using Decision-Theoretic Experience Sampling to Build Personalized Mobile Phone Interruption Models

Rosenthal

Dey

Veloso

2011

Lecture Notes in Computer Science

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Abstract. We contribute a method for approximating users' interruptibility costs to use for experience sampling and validate the method in an application that learns when to automatically turn off and on the phone volume to avoid embarrassing phone interruptions. We demonstrate that users have varying costs associated with interruptions which indicates the need for personalized cost approximations. We compare different experience sampling techniques to learn users' volume preferences and show those that ask when our cost approximation is low reduce the number of embarrassing interruptions and result in more accurate volume classifiers when deployed for long-term use.

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Section: Related Workmentioning

confidence: 99%

Using Decision-Theoretic Experience Sampling to Build Personalized Mobile Phone Interruption Models

Rosenthal

Dey

Veloso

2011

Lecture Notes in Computer Science

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“…For this reason, dealing with uncertainty remains an important aspect of users' attention detection. A set of tools included in the Interruption Workbench (Horvitz & Apacible, 2003) implement a promising approach allowing the capture of events in the user's environment that are later used to build statistical models of user's interruptibility in various situations. Users are asked to assign a dollar value -how much they would be willing to pay -to avoid being interrupted by a certain event in a certain situation.…”

Section: Non-sensory Based Mechanisms For Detection Of Attentional Stmentioning

confidence: 99%

Attention aware systems: Theories, applications, and research agenda

Roda

Thomas

2006

Computers in Human Behavior

135

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Human perceptual and cognitive abilities are limited resources. Attention is the mechanism used to allocate such resources in the most effective way. Current technologies, in addition to allowing fast access to information and people, should be designed to support human attentional processes on which they impose further strain. This paper analyses the issues related to the design of systems capable of such support: attention aware systems. We introduce the research aimed at understanding and modelling human attentional processes, including perceptual and cognitive processes as studied in cognitive psychology, as well as rhetorical, aesthetic, and social aspects related to attentional mechanisms. We analyse current approaches to the design of attention aware systems along three major features: detection of user's current attentional state, detection and evaluation of possible alternative attentional states, strategies for focus switch or maintenance. Finally, we discuss the most promising research direction for the development of systems capable of supporting human attentional mechanisms.

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“…In the last decade, a number of research groups have presented a lot of work around interruption and recovery with the goal of having a highly efficient interrupt with low intrusion [4,7,8,12,13,14,20].…”

Section: Interruption and Feedbackmentioning

confidence: 99%

Finding the Right Way for Interrupting People Improving Their Sitting Posture

Haller

Richter

Brandl

et al. 2011

Lecture Notes in Computer Science

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Abstract. In this paper, we present three different ways of interrupting people to posture guidance. We developed an ergonomically adjustable office chair equipped with four sensors measuring the office worker's posture. It is important that users do some training after bad posture and be alerted of this; therefore, we implemented three different alert modalities (Graphical Feedback, Physical Feedback, and Vibrotactile Feedback), with the goal to find out which of the techniques is the most effective interruption modality without causing a huge disruption effect. To measure the task-performance, we conducted a formal user study. Our user study results show there are different effects on performance and disruptiveness caused by the three interruption techniques. While the vibrotactile feedback might have higher information awareness benefits at the beginning, it causes a huge intrusion side-effect. Thus, the physical feedback was rated less disruptive to the workflow as the other two feedback modalities.

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Learning and reasoning about interruption

Cited by 58 publications

References 6 publications

Using Decision-Theoretic Experience Sampling to Build Personalized Mobile Phone Interruption Models

Using Decision-Theoretic Experience Sampling to Build Personalized Mobile Phone Interruption Models

Attention aware systems: Theories, applications, and research agenda

Finding the Right Way for Interrupting People Improving Their Sitting Posture

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