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Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data

机译:手腕测量的电外伤中的无监督运动伪影检测   活动数据

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摘要

One of the main benefits of a wrist-worn computer is its ability to collect avariety of physiological data in a minimally intrusive manner. Among thesedata, electrodermal activity (EDA) is readily collected and provides a windowinto a person's emotional and sympathetic responses. EDA data collected using awearable wristband are easily influenced by motion artifacts (MAs) that maysignificantly distort the data and degrade the quality of analyses performed onthe data if not identified and removed. Prior work has demonstrated that MAscan be successfully detected using supervised machine learning algorithms on asmall data set collected in a lab setting. In this paper, we demonstrate thatunsupervised learning algorithms perform competitively with supervisedalgorithms for detecting MAs on EDA data collected in both a lab-based settingand a real-world setting comprising about 23 hours of data. We also find,somewhat surprisingly, that incorporating accelerometer data as well as EDAimproves detection accuracy only slightly for supervised algorithms andsignificantly degrades the accuracy of unsupervised algorithms.
机译:腕戴式计算机的主要优点之一是能够以最小程度的干扰方式收集各种生理数据。在这些数据中,皮肤电活动(EDA)很容易被收集,并为人们的情感和同情反应提供了一个窗口。使用可穿戴腕带收集的EDA数据很容易受到运动伪影(MA)的影响,这些伪影可能会严重扭曲数据,并且如果未识别和删除数据,则会降低对数据执行的分析质量。先前的工作表明,可以使用监督的机器学习算法对实验室环境中收集的少量数据集成功检测到MAscan。在本文中,我们证明了无监督学习算法与监督算法在检测基于实验室设置和包含约23小时数据的真实环境中收集的EDA数据上的MA方面具有竞争性。我们还发现,令人惊讶的是,将加速度计数据和EDA结合起来,对于有监督的算法仅能稍微提高检测精度,并且会大大降低无监督算法的准确性。

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