Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography

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Toolkit to support intelligibility in context-aware applications (2010, methodology): “HMMs have been used to model: physical ( e.g. , [9]) and domestic activity ( e.g. , [21]), gaze ( e.g. , [7]).”
Eye Tracking and Eye-Based Human–Computer Interaction (2014, background): “For example, Bulling et al. demonstrated for the ﬁrst time that a variety of visual and non-visual human activities, such as reading (Bulling et al. 2008a, 2012b) or common ofﬁce activities (Bulling et al. 2009c, 2011b), could be spotted and recognized automatically by analyzing features based…”
A tutorial on human activity recognition using body-worn inertial sensors (2014, methodology): “For more complex data exhibiting temporal dependencies, temporal probabilistic models such as Hidden Markov Models (HMMs) [Rabiner 1989; Bulling et al. 2008; Fink 2008], Conditional Random Fields (CRFs) [Liao et al.”