Personalized Stress Monitoring using Wearable Sensors in Everyday Settings
Personalized Stress Monitoring using Wearable Sensors in Everyday Settings
复制标题
在日常环境中使用可穿戴传感器进行个性化压力监测
DOI：
10.1109/embc46164.2021.9630224
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Levorato, Marco
中科院分区：
文献类型：
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作者：
Tazarv, Ali;Labbaf, Sina;Reich, Stephanie M.;Dutt, Nikil;Rahmani, Amir M.;Levorato, Marco
由于压力会导致广泛的心理和身体健康问题，因此对压力的客观评估对于行为和生理研究至关重要。虽然有几项研究已经评估了控制环境中的压力水平，但由于混淆的背景因素和自我报告的依从性有限，日常环境中的客观压力评估在很大程度上仍然没有得到充分探索。在本文中，我们探索了基于心率（HR）和心率变异性（HRV）的日常环境中压力水平的客观预测，这些心率和心率变异性通过低成本和易于佩戴的光电体积描记（PPG）传感器捕获，这些传感器在较新的智能可穿戴设备上广泛使用。我们提出了一个分层的系统架构，个性化的压力监测，支持可调的收集数据样本的标签，并提出了一种方法，用于选择信息样本的实时数据流的标签。我们通过自我报告的问卷调查在1 - 3个月的时间内捕获了14名志愿者的压力水平，并使用机器学习方法探索了基于HR和HRV的二元压力检测。鉴于数据集是在日常环境的挑战性环境中收集的，我们观察到了有希望的初步结果。二元压力检测器相当准确，可以检测压力与非压力样本，macroF1得分高达%76。我们的研究为更复杂的标签策略奠定了基础，这些策略可以生成情境感知的个性化模型，使卫生专业人员能够提供个性化干预。
Since stress contributes to a broad range of mental and physical health problems, the objective assessment of stress is essential for behavioral and physiological studies. Although several studies have evaluated stress levels in controlled settings, objective stress assessment in everyday settings is still largely under-explored due to challenges arising from confounding contextual factors and limited adherence for self-reports. In this paper, we explore the objective prediction of stress levels in everyday settings based on heart rate (HR) and heart rate variability (HRV) captured via low-cost and easy-to-wear photoplethysmography (PPG) sensors that are widely available on newer smart wearable devices. We present a layered system architecture for personalized stress monitoring that supports a tunable collection of data samples for labeling, and present a method for selecting informative samples from the stream of real-time data for labeling. We captured the stress levels of fourteen volunteers through self-reported questionnaires over periods of between 1-3 months, and explored binary stress detection based on HR and HRV using Machine Learning methods. We observe promising preliminary results given that the dataset is collected in the challenging environments of everyday settings. The binary stress detector is fairly accurate and can detect stressful vs non-stressful samples with a macroF1 score of up to %76. Our study lays the groundwork for more sophisticated labeling strategies that generate context-aware, personalized models that will empower health professionals to provide personalized interventions.
影响因子：
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作者：
Han, Hee Jeong;Labbaf, Sina;Rahmani, Amir M.
通讯作者：
Rahmani, Amir M.