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

By Scott E. Hudson, James A. Fogarty, Christopher G. Atkeson, Daniel Avrahami, Jodi L. Forlizzi, Sara B. Kiesler, Johnny Chung Lee, Jie Yang. International Conference on Human Factors in Computing Systems, 2003

352 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems and Management, Social Psychology

View Paper: https://www.semanticscholar.org/paper/e068d6e99a64630abc084745220ae0b67a2f44f4

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Citations in the corpus

SenSay: a context-aware mobile phone (2003, background): “In a much more limited context the idea of smart appliances and phones was explored in [1], [3], [4] and [5].”
Using context-aware computing to reduce the perceived burden of interruptions from mobile devices (2005, background): “Detecting interruptibility with sensors Fortunately, recent prior work suggests that a few key sensors placed in an office environment may provide enough information to improve interruptibility detection without explicit detection of each of the 11 factors in Table 1 [ 17 ].”
a CAPpella: programming by demonstration of context-aware applications (2004, background): “Recognizing human activity is a rapidly growing subfield and has become more prominent in the HCI community of late [ 10 ,19,] While most research focuses on techniques for programmers to build useful recognizers, there are a number of systems that investigate the idea of putting a human “in the loo”
Designing robots with movement in mind (2014, background): “It can inform system engineering, for example by identifying which features and gestures need to be recognized by a computer vision system (Ho¨ysniemi, Ha¨ma¨la¨inen, & Turkki, 2004), or which sensors best predict contextual needs (Hudson et al., 2003).”
UX Design Innovation: Challenges for Working with Machine Learning as a Design Material (2017, methodology): “They have used ML to infer people’s states, like their interruptablity [31], their routines [14], and other important contextual information [11], which can mediate computer interaction.”
World-Driven Access Control for Continuous Sensing (2014, background): “Other examples of work in this area are Legion:AR [21], which uses a panel of humans to recognize activities, and work on predicting the cost of interruption from sensors [17].”
Understanding mobile contexts (2003, background): “[32] in the office environment to address this question.”