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. 2020 Apr 20;17:52. doi: 10.1186/s12984-020-00684-4

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 3 — Effect of sampling rate. Model performance (AUROC) for (a) Binary and (b) Multiclass models of tremor and bradykinesia, using the previously determined sensor set for each symptom. Shaded regions depict a 95% confidence interval on the average AUROC centered at the original sampling rate. Decreasing sampling rate reduces ability to classify tremor beyond 20–30 Hz, with only slight impact on classifying bradykinesia. A 30-Hz sampling rate is sufficient to classify both symptoms using the BioStampRC sensor (Sensor) or smart watch (Watch)