Abstract
While people can typically make a rapid assessment of another person’s interruptibility,
current systems generally have no way to consider whether an interruption is appropriate.
Systems therefore tend to interrupt at inappropriate times or unduly demand attention.
Sensor-based statistical models of human interruptibility are one approach to addressing
this problem. In a series of studies, we examine the feasibility and robustness of
sensor-based statistical models of human interruptibility, creating models that perform
better than human observers. We then present a tool to enable non-expert development
of applications that use sensor-based statistical models of human situations.
Our first study collects audio and video recordings in the normal work environments
of several office workers. We measure their interruptibility by collecting interruptibility
self-reports via experience sampling. We then use a Wizard of Oz method to examine the
recordings and simulate many potential sensors. Building statistical models from these
simulated sensors, we are able to evaluate potential sensors without actually building
them. In our second study, human observers view the recordings and estimate the
interruptibility of the office workers. Statistical models based on our simulated sensors
perform better than these human observers. Our third study examines the robustness of
this result by implementing and deploying real sensors with a more diverse set of office
workers. While different sensors are more predictive for different types of office
workers, even a general model performs better than the human observers. Because these
first three studies are dominated by social engagement, our fourth study explicitly
examines task engagement. We show that low-level programming environment events
can be used to model when a programmer will choose to defer an interruption.
We then develop Subtle, a tool to enable further research into how human computer
interaction can best benefit from sensor-based statistical models of human situations.
With an extensible sensing library, fully-automated iterative feature generation, and
support for model deployment, Subtle enables non-expert development of applications
that use sensor-based statistical models of human situations. Subtle allows human
computer interaction researchers to focus on compelling applications and datasets, rather
than the difficulties of collecting appropriate sensor data and learning statistical models.
Finally, we present a summary of contributions and plans for future work.
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