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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.
Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology. University of Brescia, Brescia, Italy.
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2017 (English)In: SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings, IEEE, 2017, article id 7894068Conference paper, Published paper (Refereed)
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%.

Place, publisher, year, edition, pages
IEEE, 2017. article id 7894068
Keyword [en]
data classification, IMU, machine learning, Mhealth, wearables
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:miun:diva-33340DOI: 10.1109/SAS.2017.7894068ISI: 000403394500037Scopus ID: 2-s2.0-85018299226ISBN: 9781509032020 OAI: oai:DiVA.org:miun-33340DiVA, id: diva2:1192303
Conference
12th IEEE Sensors Applications Symposium, SAS 2017, Glassboro, NJ, United States, 13 March 2017 through 15 March 2017
Available from: 2018-03-22 Created: 2018-03-22 Last updated: 2018-03-22Bibliographically approved

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Sisinni, Emiliano

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf