Repository logo
 
Publication

Recognition of activities of daily living and environments using acoustic sensors embedded on mobile devices

2019Journal article Open access
Simple item page
dc.contributor.authorPires, Ivan Miguel
dc.contributor.authorMarques, Gonçalo
dc.contributor.authorNuno M. Garcia
dc.contributor.authorNuno Pombo
dc.contributor.authorFrancisco Flórez-Revuelta
dc.contributor.authorSusanna Spinsante
dc.contributor.authorTeixeira, M.C.C.
dc.contributor.authorEftim Zdravevski
dc.date.accessioned2025-04-09T15:24:52Z
dc.date.available2025-04-09T15:24:52Z
dc.date.issued2019
dc.descriptionThis article is based upon work from COST Action IC1303-AAPELE—Architectures, Algorithms and Protocols for Enhanced Living Environments and COST Action CA16226–SHELD-ON—Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology). More information in www.cost.eu.
dc.description.abstractThe identification of Activities of Daily Living (ADL) is intrinsic with the user’s environment recognition. This detection can be executed through standard sensors present in every-day mobile devices. On the one hand, the main proposal is to recognize users’ environment and standing activities. On the other hand, these features are included in a framework for the ADL and environment identification. Therefore, this paper is divided into two parts—firstly, acoustic sensors are used for the collection of data towards the recognition of the environment and, secondly, the information of the environment recognized is fused with the information gathered by motion and magnetic sensors. The environment and ADL recognition are performed by pattern recognition techniques that aim for the development of a system, including data collection, processing, fusion and classification procedures. These classification techniques include distinctive types of Artificial Neural Networks (ANN), analyzing various implementations of ANN and choosing the most suitable for further inclusion in the following different stages of the developed system. The results present 85.89% accuracy using Deep Neural Networks (DNN) with normalized data for the ADL recognition and 86.50% accuracy using Feedforward Neural Networks (FNN) with non-normalized data for environment recognition. Furthermore, the tests conducted present 100% accuracy for standing activities recognition using DNN with normalized data, which is the most suited for the intended purpose.eng
dc.description.sponsorshipThis work is funded by FCT/MEC through national funds and co-funded by FEDER-PT2020 partnership agreement under the project UID/EEA/50008/2019.
dc.identifier.citationPIRES, Ivan Miguel [et al.] (2019) - Recognition of activities of daily living and environments using acoustic sensors embedded on mobile devices. Electronics. Vol. 8:10. DOI: 10.3390/electronics8121499
dc.identifier.doi10.3390/electronics8121499
dc.identifier.issn2079-9292
dc.identifier.urihttp://hdl.handle.net/10400.11/10120
dc.language.isoeng
dc.peerreviewedyes
dc.publisherMDPI AG
dc.relation.ispartofElectronics
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectActivities of daily living (ADL)
dc.subjectData fusion
dc.subjectEnvironments
dc.subjectFeature extraction
dc.subjectPattern recognition
dc.subjectSensors
dc.titleRecognition of activities of daily living and environments using acoustic sensors embedded on mobile devicespor
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue12
oaire.citation.titleElectronics
oaire.citation.volume8
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameTeixeira
person.givenNameM.C.C.
person.identifier.ciencia-id0913-BC21-F66E
person.identifier.orcid0000-0002-8534-9484
relation.isAuthorOfPublication5c53ae6c-7865-4ef1-adea-ea2fa221aee4
relation.isAuthorOfPublication.latestForDiscovery5c53ae6c-7865-4ef1-adea-ea2fa221aee4

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
2019 - Recognition of Activities of Daily Living and Environments Using Acoustic Sensors Embedded on Mobile Devices.pdf
Size:
749.74 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.02 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ESACB - Artigos em revistas com arbitragem científica