Abstract
Mixed Reality (MR) is increasingly integrated into daily life, providing enhanced capabilities across various domains. However,
users face growing notification streams that disrupt their immersive experience. We present PersoNo, a personalised notification
urgency classifier for MR that intelligently classifies notifications
based on individual user preferences. Through a user study (N=18),
we created the first MR notification dataset containing both selflabelled and interaction-based data across activities with varying
cognitive demands. Our thematic analysis revealed that, unlike in
mobiles, the activity context is equally important as the content and
the sender in determining notification urgency in MR. Leveraging
these insights, we developed PersoNo using large language models that analyse users’ replying behaviour patterns. Our multi-agent
approach achieved 81.5% accuracy and significantly reduced false
negative rates (0.381) compared to baseline models. PersoNo has
the potential not only to reduce unnecessary interruptions but also
to offer users understanding and control of the system, adhering to
Human-Centered Artificial Intelligence design principles.
users face growing notification streams that disrupt their immersive experience. We present PersoNo, a personalised notification
urgency classifier for MR that intelligently classifies notifications
based on individual user preferences. Through a user study (N=18),
we created the first MR notification dataset containing both selflabelled and interaction-based data across activities with varying
cognitive demands. Our thematic analysis revealed that, unlike in
mobiles, the activity context is equally important as the content and
the sender in determining notification urgency in MR. Leveraging
these insights, we developed PersoNo using large language models that analyse users’ replying behaviour patterns. Our multi-agent
approach achieved 81.5% accuracy and significantly reduced false
negative rates (0.381) compared to baseline models. PersoNo has
the potential not only to reduce unnecessary interruptions but also
to offer users understanding and control of the system, adhering to
Human-Centered Artificial Intelligence design principles.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Symposium on Mixed and Augmented Reality (ISMAR) |
| Publication status | Published - 27 Aug 2025 |
Keywords
- Mixed Reality, Notification Classifier, Human Centered Artificial Intelligence.