Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Open-Source Data Collection and Data Sets for Activity Recognition in Smart Homes
Örebro University, Sweden.
Örebro University, Sweden.
Örebro University, Sweden.
RISE Research Institutes of Sweden, Digitala system, Datavetenskap. (Connected Intelligence)ORCID-id: 0000-0003-3139-2564
Vise andre og tillknytning
2020 (engelsk)Inngår i: Sensors, E-ISSN 1424-8220, Vol. 20, nr 3, artikkel-id 879Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

As research in smart homes and activity recognition is increasing, it is of ever increasing importance to have benchmarks systems and data upon which researchers can compare methods.While synthetic data can be useful for certain method developments, real data sets that are open and shared are equally as important. This paper presents the E-care@home system, its installation in a real home setting, and a series of data sets that were collected using the E-care@home system. Our first contribution, the E-care@home system, is a collection of software modules for data collection, labeling, and various reasoning tasks such as activity recognition, person counting, and configuration planning.It supports a heterogeneous set of sensors that can be extended easily and connects collected sensor data to higher-level Artificial Intelligence (AI) reasoning modules. Our second contribution is a series of open data sets which can be used to recognize activities of daily living. In addition to these data sets, we describe the technical infrastructure that we have developed to collect the data and the physical environment. Each data set is annotated with ground-truth information, making it relevant for researchers interested in benchmarking different algorithms for activity recognition.

sted, utgiver, år, opplag, sider
MDPI, 2020. Vol. 20, nr 3, artikkel-id 879
Emneord [en]
smart home data sets; data collection software; prototype installation
HSV kategori
Identifikatorer
URN: urn:nbn:se:ri:diva-43882DOI: 10.3390/s20030879Scopus ID: 2-s2.0-85079189175OAI: oai:DiVA.org:ri-43882DiVA, id: diva2:1392847
Forskningsfinansiär
Knowledge FoundationTilgjengelig fra: 2020-02-13 Laget: 2020-02-13 Sist oppdatert: 2023-05-26bibliografisk kontrollert

Open Access i DiVA

fulltext(1800 kB)117 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 1800 kBChecksum SHA-512
e501bd639283e42bfb343d5bc618d076a7c60ee90fa08233ee339518570bf411a209e2ca678d71589bff122fc92560be60f0a3c096cc1a7392337460d7680df5
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekstScopus

Person

Tsiftes, Nicolas

Søk i DiVA

Av forfatter/redaktør
Tsiftes, Nicolas
Av organisasjonen
I samme tidsskrift
Sensors

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 117 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 55 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
v. 2.43.0