OpenHAR : a Matlab toolbox for easy access to publicly open human activity data sets
Siirtola, Pekka; Koskimäki, Heli; Röning, Juha (2018-10-08)
Pekka Siirtola, Heli Koskimäki, and Juha Röning. 2018. OpenHAR: A Matlab Toolbox for Easy Access to Publicly Open Human Activity Data Sets. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp '18). ACM, New York, NY, USA, 1396-1403. DOI: https://doi.org/10.1145/3267305.3267503
© ACM 2018. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (UbiComp '18), https://doi.org/10.1145/3267305.3267503.
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https://urn.fi/URN:NBN:fi-fe201901031246
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Abstract
This study introduces OpenHAR, a free Matlab toolbox to combine and unify publicly open data sets. It provides an easy access to accelerometer signals of ten publicly open human activity data sets. Data sets are easy to access as OpenHAR provides all the data sets in the same format. In addition, units, measurement range and labels are unified, as well as, body position IDs. Moreover, data sets with different sampling rates are unified using downsampling. What is more, data sets have been visually inspected to find visible errors, such as sensor in wrong orientation. OpenHAR improves re-usability of data sets by fixing these errors. Altogether OpenHAR contains over 65 million labeled data samples. This is equivalent to over 280 hours of data from 3D accelerometers. This includes data from 211 study subjects performing 17 daily human activities and wearing sensors in 14 different body positions.
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