Casella, E, Ortolani, M ORCID: https://orcid.org/0000-0001-6759-7698, Silvestri, S and Das, SK (2019) Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces. Personal and Ubiquitous Computing, 24 (4). pp. 451-464.

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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.

Item Type: Article
Additional Information: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via Springer at http://doi.org/10.1007/s00779-019-01319-9 - please refer to any applicable terms of use of the publisher.
Uncontrolled Keywords: Grammatical inference; Mobility; Human activity recognition
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Depositing User: Symplectic
Date Deposited: 31 Oct 2019 10:19
Last Modified: 05 Sep 2020 01:30
URI: https://eprints.keele.ac.uk/id/eprint/7102

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