Bogazici University, Computer Engineering Dept., Bebek, Istanbul, Turkey.
Sensors (Basel). 2020 Feb 4;20(3):838. doi: 10.3390/s20030838.
Chronic stress leads to poor well-being, and it has effects on life quality and health. Societymay have significant benefits from an automatic daily life stress detection system using unobtrusivewearable devices using physiological signals. However, the performance of these systems is notsufficiently accurate when they are used in unrestricted daily life compared to the systems testedin controlled real-life and laboratory conditions. To test our stress level detection system thatpreprocesses noisy physiological signals, extracts features, and applies machine learning classificationtechniques, we used a laboratory experiment and ecological momentary assessment based datacollection with smartwatches in daily life. We investigated the effect of different labeling techniquesand different training and test environments. In the laboratory environments, we had more controlledsituations, and we could validate the perceived stress from self-reports. When machine learningmodels were trained in the laboratory instead of training them with the data coming from daily life,the accuracy of the system when tested in daily life improved significantly. The subjectivity effectcoming from the self-reports in daily life could be eliminated. Our system obtained higher stresslevel detection accuracy results compared to most of the previous daily life studies.
慢性压力会导致健康状况不佳，影响生活质量和健康。社会可能会从使用非侵入式可穿戴设备和生理信号的自动日常生活压力检测系统中获得巨大的好处。然而，与在受控的现实生活和实验室条件下测试的系统相比，这些系统在不受限制的日常生活中的性能不够准确。为了测试我们的预处理嘈杂生理信号、提取特征和应用机器学习分类技术的压力水平检测系统，我们在日常生活中使用智能手表进行了基于实验室实验和生态瞬间评估的数据收集。我们研究了不同标记技术和不同训练和测试环境的影响。在实验室环境中，我们有更可控的情况，并且可以从自我报告中验证感知到的压力。当机器学习模型在实验室中进行训练，而不是使用来自日常生活的数据进行训练时，系统在日常生活中的测试准确性显著提高。可以消除日常生活中自我报告带来的主观性影响。与大多数以前的日常生活研究相比，我们的系统获得了更高的压力水平检测准确性结果。
