Predicting human interruptibility with sensors

By James A. Fogarty, Scott E. Hudson, Christopher G. Atkeson, Daniel Avrahami, Jodi L. Forlizzi, Sara B. Kiesler, Johnny Chung Lee, Jie Yang. TCHI, 2005

392 citations recorded by the source corpus.

Subfields: Information Systems, Information Systems and Management, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/d8e1336e63a891e19fd161a3b09d37504755f4c7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

In Information Systems and Management

Citations in the corpus

InterruptMe: designing intelligent prompting mechanisms for pervasive applications (2014, methodology): “[13, 12] as well as by Ho and Intille [17] and Fischer et al.”
Assessing demand for intelligibility in context-aware applications (2009, methodology): “Our application uses the responsiveness prediction to determine the subject’s interruptibility [12], and either forwards or suppresses incoming IM messages.”
Human–robot interaction in industrial collaborative robotics: a literature review of the decade 2008–2017 (2019, background): “( The availability of a human is related to both his presence and interruptibility [59].”
An in-situ study of mobile phone notifications (2014, background): “[15] conducted a series of studies that show that in an office environment, simple, low-costs sensor, such as a microphone, can infer the interruptibility of an information worker with the same accuracy as humans.”
How to approach humans?-strategies for social robots to initiate interaction (2009, background): “analyzed human interruptibility in an office environment and demonstrated that even simple silence detectors could significantly estimate interruptibility [13].”
Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior (2008, background): “This has been explored in a large number of domains including classifying a user’s posture [17], tracking and classifying user exercises [5], classifying a user’s interruptibility [6] and detecting a variety of daily activities within the home [26, 24].”
Simultaneous Tracking and Activity Recognition (STAR) Using Many Anonymous, Binary Sensors (2005, methodology): “• ESM/EMA. The experience sampling method (ESM), also called ecological momentary assessment (EMA), prompts the occupant for input through a device in the environment, either at timed intervals [ 34 ] or when activity levels are above a threshold [38].”