Hierarchical syntactic models for human activity recognition through mobility traces

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Abstract

Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known as grammatical inference. We also introduce a measure of similarity that accounts for the intrinsic hierarchical nature of such models, and allows to identify the common traits in the paths induced by different activities at various granularity levels. Our method has been validated on a dataset of real traces representing movements of users in a large metropolitan area. The experimental results show the effectiveness of our similarity measure to correctly identify a set of common coarse-grained activities, as well as their refinement at a finer level of granularity.

Publication
In Springer Personal and Ubiquitous Computing, Vol. 24 Issue 4, Pages 451-464, 2020
Enrico Casella
Enrico Casella
Ph.D. Candidate

PhD candidate conducting research on smart grids and power conservation, providing CS expertise in cross-field projects and supervising undergrad research.