Toward Robust Stress Prediction in the Age of Wearables: Modeling Perceived Stress in a Longitudinal Study With Information Workers
Toward Robust Stress Prediction in the Age of Wearables: Modeling Perceived Stress in a Longitudinal Study With Information Workers
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DOI：
10.1109/taffc.2022.3188006
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发表时间：
2022-10-01
影响因子：
11.2
通讯作者：
D'Mello, Sidney K.
中科院分区：
文献类型：
--
作者：
Booth, Brandon M.;Vrzakova, Hana;D'Mello, Sidney K.
鉴于压力的广泛不利后果--因当前的大流行而加剧--可穿戴式传感为自动压力跟踪提供了独特的机会，为福祉干预提供信息。然而，它在野外和规模上的成功取决于自动应力推理的健壮性和有效性，而这在当前的系统中是有限的。在这项工作中，我们列举了使用可穿戴传感器实现可行的自动应力推断所必需的健壮性和有效性的特性，并强调了构建和评估这些系统所面临的挑战。以这些标准作为指导原则，我们给出了对信息工作者进行的一项大型(N=606)现场纵向可穿戴和上下文感知研究的自动应力推断结果。使用包括可穿戴传感器、相对位置跟踪、智能手机使用和环境感知的多模式方法，我们训练回归模型以独立于参与者的方式预测每日自我报告的感知压力。我们的模型显著优于压力评分调整后的基线变量，并且与小到中等的效应一致。我们的发现突出了自动感知压力推理的稳健和有效方法与当前方法之间的性能差异，并表明进一步的性能提高可能需要比现有方法更多的感知模式和增强的上下文意识。
Given the widespread adverse outcomes of stress - exacerbated by the current pandemic - wearable sensing provides unique opportunities for automated stress tracking to inform well-being interventions. However, its success in the wild and at scale depends on the robustness and validity of automated stress inference, which is limited in current systems. In this work, we enumerate the properties of robustness and validity necessary for achieving viable automated stress inference using wearable sensors, and we underscore present challenges to constructing and evaluating these systems. Using these criteria as guiding principles, we present automated stress inference results from a large (N=606) in situ longitudinal wearable and contextual sensing study of information workers. Using a multimodal approach encompassing a wearable sensor, relative location tracking, smartphone usage, and environmental sensing, we trained regression models to predict daily self-reported perceived stress in a participant-independent fashion. Our models significantly outperformed baseline variants with shuffled stress scores and were consistent with small-to-moderate effects. Our findings highlight the performance disparity between robust and valid approaches to automated perceived stress inference and current approaches and suggest that further performance gains might require additional sensing modalities and enhanced contextual awareness than existing approaches.