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. 2019 Feb 6;7(2):e11270. doi: 10.2196/11270

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©Anis Davoudi, Amal Asiri Wanigatunga, Matin Kheirkhahan, Duane Benjamin Corbett, Tonatiuh Mendoza, Manoj Battula, Sanjay Ranka, Roger Benton Fillingim, Todd Matthew Manini, Parisa Rashidi. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 06.02.2019.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.

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Importance of features in terms of the mean decrease in the Gini index in each classification task and in terms of the increase in node purity for the metabolic equivalent scores estimation regression task, as reported by the random forest model and 16-second window length for feature extraction (note the different range for the x-axis). METs: metabolic equivalent scores; IncNodePurity: Increase in node purity; cor_x_z: correlation between x-axis and z-axis; cor_y_z: correlation between y-axis and z-axis; cor_x_y: correlation between x-axis and y-axis; cv: covariance of the vector magnitude; sd_a: SD of angle; mean_a: mean of angle; pow.625: percentage of the power of the vector magnitude that is in the range of 0.6-2.5 Hz; dom.freq: dominant frequency of vector magnitude.