miun.sePublikationer
Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
IMU-based solution for automatic detection and classification of exercises in the fitness scenario
University of Brescia, Brescia, Italy.
University of Brescia, Brescia, Italy.
University of Brescia, Brescia, Italy.
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för elektronikkonstruktion. University of Brescia, Brescia, Italy.
Visa övriga samt affilieringar
2017 (Engelska)Ingår i: SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings, IEEE, 2017, artikel-id 7894068Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Causal relationship between physical activity and prevention of several diseases has been known for some time. Recently, attempts to quantify dose-response relationship between physical activity and health show that automatic tracking and quantification of the exercise efforts not only help in motivating people but improve health conditions as well. However, no commercial devices are available for weight training and calisthenics. This work tries to overcome this limit, exploiting machine learning technique (particularly Linear Discriminant Analysis, LDA) for analyzing data coming from wearable inertial measurement units, (IMUs) and classifying/counting such exercises. Computational requirements are compatible with embedded implementation and reported results confirm the feasibility of the proposed approach, offering an average accuracy in the detection of exercises on the order of 85%.

Ort, förlag, år, upplaga, sidor
IEEE, 2017. artikel-id 7894068
Nyckelord [en]
data classification, IMU, machine learning, Mhealth, wearables
Nationell ämneskategori
Elektroteknik och elektronik
Identifikatorer
URN: urn:nbn:se:miun:diva-33340DOI: 10.1109/SAS.2017.7894068ISI: 000403394500037Scopus ID: 2-s2.0-85018299226ISBN: 9781509032020 (tryckt)OAI: oai:DiVA.org:miun-33340DiVA, id: diva2:1192303
Konferens
12th IEEE Sensors Applications Symposium, SAS 2017, Glassboro, NJ, United States, 13 March 2017 through 15 March 2017
Tillgänglig från: 2018-03-22 Skapad: 2018-03-22 Senast uppdaterad: 2018-09-05Bibliografiskt granskad

Open Access i DiVA

Fulltext saknas i DiVA

Övriga länkar

Förlagets fulltextScopus

Personposter BETA

Sisinni, Emiliano

Sök vidare i DiVA

Av författaren/redaktören
Sisinni, Emiliano
Av organisationen
Avdelningen för elektronikkonstruktion
Elektroteknik och elektronik

Sök vidare utanför DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetricpoäng

doi
isbn
urn-nbn
Totalt: 168 träffar
RefereraExporteraLänk till posten
Permanent länk

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