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Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data

Zhang, Yuning
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1501876131092933

Abstract Details

2017, Master of Science, University of Toledo, Electrical Engineering.
One of the main benefits of a wrist-worn computer compared to other computing platforms is its ability to collect a variety of physiological data in a minimally intrusive manner. Among these physiological data, electrodermal activity (EDA) is readily collected and provides a window into a person's emotional and sympathetic responses. Unfortunately, EDA data collected using a wearable wristband are easily influenced by motion artifacts (MAs) that may significantly distort the data and degrade the quality of analyses performed on the data if not identified and removed. Prior work has demonstrated that MAs can be successfully detected using supervised machine learning algorithms on a small data set collected in a lab setting. In this thesis, we demonstrate that unsupervised learning algorithms perform competitively and sometimes \emph{even better} than supervised algorithms for detecting MAs on EDA data collected in both a lab-based and a real-world data set comprising about 23 hours of data. We also find, somewhat surprisingly, that accelerometer data do not appear to be very useful in detecting MAs in EDA, incorporating accelerometer data as well as EDA improves detection accuracy only slightly for supervised algorithms and significantly degrades the accuracy of unsupervised algorithms
Kevin Xu (Advisor)
Mansoor Alam (Committee Member)
Scott Pappada (Committee Member)
Computer Science

Recommended Citations

Citations

  • Zhang, Y. (2017). Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data [Master's thesis, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1501876131092933

    APA Style (7th edition)

  • Zhang, Yuning. Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data. 2017. University of Toledo, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1501876131092933.

    MLA Style (8th edition)

  • Zhang, Yuning. "Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data." Master's thesis, University of Toledo, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1501876131092933

    Chicago Manual of Style (17th edition)

toledo1501876131092933
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© , some rights reserved.Creative Commons License Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data by Yuning Zhang is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Toledo and OhioLINK.