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.

中文翻译：

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在可穿戴设备时代实现稳健的压力预测：在信息工作者的纵向研究中模拟感知压力

鉴于压力的广泛不良后果——当前的流行病加剧了这种后果——可穿戴传感为自动压力跟踪提供了独特的机会，以告知健康干预措施。然而，它在野外和大规模的成功取决于自动压力推断的稳健性和有效性，这在当前系统中是有限的。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压力推断所必需的稳健性和有效性的属性，并强调了构建和评估这些系统所面临的挑战。使用这些标准作为指导原则，我们展示了来自大型 (N=606) 的自动压力推断结果信息工作者的原位纵向可穿戴和情境感知研究。使用包含可穿戴传感器、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。我们的模型显着优于具有随机压力评分的基线变体，并且与小到中等效果一致。我们的研究结果强调了自动感知压力推断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升可能需要额外的传感方式和比现有方法增强的情境意识。