6533b833fe1ef96bd129c2d7
RESEARCH PRODUCT
Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces
Marco OrtolaniEnrico CasellaSajal K. DasSimone Silvestrisubject
QA75Computer science02 engineering and technologyManagement Science and Operations ResearchSimilarity measureMachine learningcomputer.software_genreZA4050Set (abstract data type)Activity recognitionGrammatical inference Human activity recognition Mobility020204 information systemsSmart citySimilarity (psychology)0202 electrical engineering electronic engineering information engineeringSettore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniFinite-state machineT1business.industryGrammar inductionComputer Science ApplicationsHardware and Architecture020201 artificial intelligence & image processingArtificial intelligenceGranularitybusinesscomputerdescription
AbstractRecognizing 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 asgrammatical inference. We also introduce a measure ofsimilaritythat 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-11-25 |