Detection of Artifacts in Ambulatory Electrodermal Activity Data
Detection of Artifacts in Ambulatory Electrodermal Activity Data
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DOI：
10.1145/3397316
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发表时间：
2020-06-01
期刊：
影响因子：
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通讯作者：
Santini, Silvia
中科院分区：
文献类型：
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作者：
Gashi, Shkurta;Di Lascio, Elena;Santini, Silvia
最近的可穿戴设备能够在日常生活环境中长时间连续且不引人注目地监测人类的生理参数，例如皮肤电活动和心率。对这些参数的持续监控使得能够创建能够预测情感状态和压力的系统，以提供反馈来改善它们。在日常生活环境中部署此类系统仍然很复杂，并且由于工件的存在影响所收集的数据质量低下，因此容易出错。在本文中，我们提出了一种自动方法来检测长时间在野外收集的皮肤电活动 (EDA) 信号中的伪影。为此，我们首先进行系统的文献综述，并为人类注释者编写一套手动标记工件的指南，并使用这些标签作为事实来测试我们的自动方法。为了评估我们的方法，我们收集了 13 位野外参与者的生理数据，并由两名人类注释者标记了该数据集的 107.56 小时。我们根据要求向其他研究人员公开提供该数据集。我们的模型使用留一受试者交叉验证在野外收集的数据上实现了 98% 的干净和形状伪影分类召回率，比基线高 42 个百分点。我们表明，在使用完全野外数据进行测试时，最先进的方法不能很好地概括，即使在手动调整之后，也只能识别出数据集中存在的 17% 的伪影。我们使用留一天时间进一步测试我们的方法随着时间的推移的稳健性，并获得非常相似的性能。然后，我们引入了一个新的指标来评估 EDA 段的质量，该指标不仅考虑 EDA 形状的伪影的影响，还考虑环境温度变化或用户高强度运动产生的伪影的影响。我们的结果意味着我们可以消除对人类注释者的需求，或者显着减少他们标记数据所需的时间。此外，我们的方法可以以在线方式自动检测 EDA 信号中的伪影。
Recent wearable devices enable continuous and unobtrusive monitoring of human's physiological parameters, like e.g., electrodermal activity and heart rate, over long periods of time in everyday life settings. Continuous monitoring of these parameters enables the creation of systems able to predict affective states and stress with the goal of providing feedback to improve them. Deployment of such systems in everyday life settings is still complex and prone to errors due to the low quality of the collected data impacted by the presence of artifacts. In this paper we present an automatic approach to detect artifacts in electrodermal activity (EDA) signals collected in-the-wild over long periods of time. To this end we first perform a systematic literature review and compile a set of guidelines for human annotators to label artifacts manually and we use these labels as ground-truth to test our automatic approach. To evaluate our approach, we collect physiological data from 13 participants in-the-wild and two human annotators label 107.56 hours of this data set. We make the data set publicly available to other researchers upon request. Our model achieves a recall of 98% for clean and shape artifacts classification on data collected in-the-wild using leave-one-subject-out cross-validation, which is 42 percentage points higher than the baseline. We show that state of the art approaches do not generalize well when tested with completely in-the-wild data and identify only 17% of the artifacts present in our data set, even after manual adaption. We further test the robustness of our approach over time using leave-one-day-out and achieve very similar performance. We then introduce a new metric to evaluate the quality of EDA segments that considers the impact of not only artifacts in the shape of EDA but also artifacts generated by environmental temperature changes or user's high intensity movement. Our results imply that we can eliminate the need for human annotators or significantly reduce the time they need to label data. Also, our approach can be used in an online manner to automatically detect artifacts in EDA signals.