Search results for " Human Activity Recognition"

showing 3 items of 3 documents

Hierarchical Syntactic Models for Human Activity Recognition through Mobility Traces

2019

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 su…

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 intelligenceGranularitybusinesscomputer
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A Federated Learning Approach for Distributed Human Activity Recognition

2022

In recent years, the widespread diffusion of smart pervasive devices able to provide AI-based services has encouraged research in the definition of new distributed learning paradigms. Federated Learning (FL) is one of the most recent approaches which allows devices to collaborate to train AI-based models, whereas guarantying privacy and lower communication costs. Although different studies on FL have been conducted, a general and modular architecture capable of performing well in different scenarios is still missing. Following this direction, this paper proposes a general FL framework whose validity is assessed by considering a distributed activity recognition scenario in which users' perso…

Settore ING-INF/05 - Sistemi Di Elaborazione Delle InformazioniFederated Learning Distributed Computing Machine Learning Human Activity Recognition
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Human Activity Signatures Captured under Different Directions Using SISO and MIMO Radar Systems

2022

In this paper, we highlight and resolve the shortcomings of single-input single-output (SISO) millimeter wave (mm-Wave) radar systems for human activity recognition (HAR). A 2×2 distributed multiple-input multiple-output (MIMO) radar framework is presented to capture human activity signatures under realistic conditions in indoor environments. We propose to distribute the two pairs of collocated transmitter–receiver antennas in order to illuminate the indoor environment from different perspectives. For the proposed MIMO system, we measure the time-variant (TV) radial velocity distribution and TV mean radial velocity to observe the signatures of human activities. We deploy the Anc…

direction-independent human activity recognition; fall detection; distributed MIMO; FMCW radar; micro-Doppler signatures; aspect angle; multistatic radar systems; passive step counter; DTW; velocity estimationFluid Flow and Transfer ProcessesProcess Chemistry and TechnologyGeneral EngineeringVDP::Medisinske Fag: 700General Materials ScienceVDP::Matematikk og Naturvitenskap: 400InstrumentationComputer Science::Information TheoryComputer Science ApplicationsApplied Sciences
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