University of Oulu

Siirtola P., Koskimäki H., Röning J. (2019) OpenHAR: A Matlab Toolbox for Easy Access to Publicly Open Human Activity Data Sets—Introduction and Experimental Results. In: Kawaguchi N., Nishio N., Roggen D., Inoue S., Pirttikangas S., Van Laerhoven K. (eds) Human Activity Sensing. Springer Series in Adaptive Environments. Springer, Cham

OpenHAR : a Matlab toolbox for easy access to publicly open human activity data sets—introduction and experimental results

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Author: Siirtola, Pekka1; Koskimäki, Heli1; Röning, Juha1
Organizations: 1Biomimetics and Intelligent Systems Group, University of Oulu, FI-90014 Oulu, Finland
Format: article
Version: accepted version
Access: embargoed
Persistent link: http://urn.fi/urn:nbn:fi-fe202001071211
Language: English
Published: Springer Nature, 2019
Publish Date: 2021-09-10
Description:

Abstract

OpenHAR is a toolbox for Matlab to combine and unify 3D accelerometer data of ten publicly open data sets. This chapter introduces OpenHAR and provides initial experimental results based on it. Moreover, OpenHAR provides an easy access to these data sets by providing them in the same format, and in addition, units, measurement range, sampling rates, labels, and body position IDs are unified. Moreover, data sets have been visually inspected to fix visible errors, such as sensor in wrong orientation. For Matlab users OpenHAR provides code which user can use to easily select only desired parts of this data. This chapter also introduces OpenHAR to users without Matlab. For them, the whole OpenHAR data is provided as a one .txt-file. Altogether, OpenHAR contains over 280 h of accelerometer data from 211 study subjects performing 17 daily human activities and wearing sensors in 14 different body positions. This chapter shown the first experimental results based on OpenHAR data. The experiment was done using three classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and classification and regression tree (CART). The experiment showed that using LDA and QDA classifiers and OpenHAR data, as high recognition rates can be achieved in a previously unseen test data than by using a data set specially collected for this purpose. With CART the results obtained using OpenHAR data were slightly lower.

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Series: Springer Series in Adaptive Environments
ISSN: 2522-5529
ISSN-E: 2522-5529
ISBN: 978-3-030-13001-5
ISBN Print: 978-3-030-13000-8
Pages: 121 - 133
DOI: 10.1007/978-3-030-13001-5_9
OADOI: https://oadoi.org/10.1007/978-3-030-13001-5_9
Host publication: Human Activity Sensing : Corpus and Applications
Host publication editor: Kawaguchi, Nobuo
Nishio, Nobuhiko
Roggen, Daniel
Inoue, Sozo
Pirttikangas, Susanna
Van Laerhoven, Kristof
Type of Publication: A3 Book chapter
Field of Science: 213 Electronic, automation and communications engineering, electronics
Subjects:
Funding: The authors would like to thank Infotech Oulu for funding this work.
Copyright information: © Springer Nature Switzerland AG 2019. This is a post-peer-review, pre-copyedit version of an article published in Human Activity Sensing. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-13001-5_9.