Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • 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
Automatic annotation for human activity recognition in free living using a smartphone
Computer Science Research Institute, Ulster University, Newtownabbey BT370QB, UK.ORCID-id: 0000-0002-1870-0203
Computer Science Research Institute, Ulster University, Newtownabbey BT370QB, UK.
Computer Science Research Institute, Ulster University, Newtownabbey BT370QB, UK.
Computer Science Research Institute, Ulster University, Newtownabbey BT370QB, UK.
Vise andre og tillknytning
2018 (engelsk)Inngår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, nr 7, artikkel-id 2203Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Data annotation is a time-consuming process posing major limitations to the development of Human Activity Recognition (HAR) systems. The availability of a large amount of labeled data is required for supervised Machine Learning (ML) approaches, especially in the case of online and personalized approaches requiring user specific datasets to be labeled. The availability of such datasets has the potential to help address common problems of smartphone-based HAR, such as inter-person variability. In this work, we present (i) an automatic labeling method facilitating the collection of labeled datasets in free-living conditions using the smartphone, and (ii) we investigate the robustness of common supervised classification approaches under instances of noisy data. We evaluated the results with a dataset consisting of 38 days of manually labeled data collected in free living. The comparison between the manually and the automatically labeled ground truth demonstrated that it was possible to obtain labels automatically with an 80–85% average precision rate. Results obtained also show how a supervised approach trained using automatically generated labels achieved an 84% f-score (using Neural Networks and Random Forests); however, results also demonstrated how the presence of label noise could lower the f-score up to 64–74% depending on the classification approach (Nearest Centroid and Multi-Class Support Vector Machine).

sted, utgiver, år, opplag, sider
MDPI, 2018. Vol. 18, nr 7, artikkel-id 2203
HSV kategori
Forskningsprogram
Distribuerade datorsystem
Identifikatorer
URN: urn:nbn:se:ltu:diva-70137DOI: 10.3390/s18072203PubMedID: 29987218Scopus ID: 2-s2.0-85050029995OAI: oai:DiVA.org:ltu-70137DiVA, id: diva2:1233786
Merknad

Validerad;2018;Nivå 2;2018-07-19 (inah)

Tilgjengelig fra: 2018-07-19 Laget: 2018-07-19 Sist oppdatert: 2018-08-08bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstPubMedScopus

Personposter BETA

Synnes, KåreHallberg, Josef

Søk i DiVA

Av forfatter/redaktør
Cruciani, FredericoSynnes, KåreHallberg, Josef
Av organisasjonen
I samme tidsskrift
Sensors

Søk utenfor DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric

doi
pubmed
urn-nbn
Totalt: 65 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • 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