Attention is the most precious of the resources we have. From communication services (chat, SMS), to online social networks (Facebook, Twitter), and warning systems (e.g. "battery low"), different actors compete for our attention. In our research we aim to understand mechanisms that guide user attention and to develop technical solutions for attention management.
Our goal is to advance human-computer interaction to the point defined by Mark Weiser's 1991 quote:
machines that fit the human environment instead of forcing humans to enter theirs will make using computing as refreshing as taking a walk in the woods
From the practical point of view, attention management systems built towards the above vision will enable us to:
- Reduce user frustration and application churn rate.
- Increase user engagement with the delivered content.
- Increase compliance with suggestions in mHealth and behaviour change intervention applications
- Improve safety in ubiquitous computing environments (e.g. by not interrupting while a user is driving).
- Reduce unwanted data traffic.
- Open doors for anticipatory computing, where future activities could be intelligently steered through predictive attention management.
In our research, through mobile sensing, we have thoroughly investigated factors that impact a user's interruptibility (see our UbiComp'14
, and CHI'16
papers, for example). Further, we built an Android library InterruptMe
that uses sensors on a smartphone to recognize the user's context and infer if a user is interruptible or not
. InterruptMe is available as open source software