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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.
Mittuniversitetet, Fakulteten för naturvetenskap, teknik och medier, Avdelningen för elektronikkonstruktion. University of Brescia, Brescia, Italy.
Vise andre og tillknytning
2017 (engelsk)Inngår i: SAS 2017 - 2017 IEEE Sensors Applications Symposium, Proceedings, IEEE, 2017, artikkel-id 7894068Konferansepaper, Publicerat paper (Fagfellevurdert)
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%.

sted, utgiver, år, opplag, sider
IEEE, 2017. artikkel-id 7894068
Emneord [en]
data classification, IMU, machine learning, Mhealth, wearables
HSV kategori
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
Konferanse
12th IEEE Sensors Applications Symposium, SAS 2017, Glassboro, NJ, United States, 13 March 2017 through 15 March 2017
Tilgjengelig fra: 2018-03-22 Laget: 2018-03-22 Sist oppdatert: 2018-09-05bibliografisk kontrollert

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