pax

People are subjected to a multitude of interruptions, which in some situations are detrimental to their work performance. Consequently, the capability to predict a person’s degree of interruptability (i.e., a measure of detrimental an interruption would be to her current work) can provide a basis for a ﬁltering mechanism. This paper introduces a novel approach to predict a person’s presence and interruptability in an ofﬁce-like environment based on audio, multi-sector motion detection using video, and the time of the day collected as sensor data. Conducting an experiment in a real ofﬁce environment over the length of more than 40 work days we show that the multisector motion detection data, which to our knowledge has been used for the ﬁrst time to this end, outperforms audio data both in presence and interruptability. We, furthermore, show, that the combination of all three data-streams improves the interruptability prediction accuracy and robustness. Finally, we use these data to predict a subject’s phone behavior (ignore or accept the incoming phone call) by combining interruptability and the estimated importance of call. We call such an application an artiﬁcial receptionist. Our analysis also show that the results improve when taking the temporal aspect of the context into account.

Towards an Artificial Receptionist: Anticipating a Persons ...

Towards an Artificial Receptionist: Anticipating a Persons Phone Behavior

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