Automatic Stress Detection in Working Environments From Smartphones' Accelerometer Data: A First Step
Automatic Stress Detection in Working Environments From Smartphones' Accelerometer Data: A First Step
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DOI：
10.1109/jbhi.2015.2446195
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发表时间：
2016-07-01
影响因子：
7.7
通讯作者：
Mayora, Oscar
中科院分区：
文献类型：
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作者：
Garcia-Ceja, Enrique;Osmani, Venet;Mayora, Oscar
许多组织工作量的增加以及随之而来的职业压力的增加对工作人员的健康产生了负面影响。由于自我报告的主观性以及个体之间和个体内部的可变性，测量压力和其他人类心理动力学是困难的。随着智能手机的出现，现在可以监控人类行为的各个方面，包括与心理状态和压力相关的客观测量行为。我们使用智能手机内置加速计的数据来检测与受试者压力水平相关的行为。选择加速度计传感器是因为它引起较少的隐私问题（例如，与位置、视频或音频记录相比），并且因为其低功耗使其适合嵌入在较小的可穿戴设备中，例如健身跟踪器。来自两个不同组织的约30名受试者配备了智能手机。这项研究持续了八周，在真实的工作环境中进行，对智能手机的使用没有任何限制。受试者在工作时间内报告了三次他们的压力水平。使用统计模型的组合来对自我报告的压力水平进行分类，我们实现了用户特定模型的最大总体准确度为71%，使用类似用户模型的准确度为60%，仅依赖于来自单个加速度计的数据。
Increase in workload across many organizations and consequent increase in occupational stress are negatively affecting the health of the workforce. Measuring stress and other human psychological dynamics is difficult due to subjective nature of selfreporting and variability between and within individuals. With the advent of smartphones, it is now possible to monitor diverse aspects of human behavior, including objectively measured behavior related to psychological state and consequently stress. We have used data from the smartphone's built-in accelerometer to detect behavior that correlates with subjects stress levels. Accelerometer sensor was chosen because it raises fewer privacy concerns (e.g., in comparison to location, video, or audio recording), and because its low-power consumption makes it suitable to be embedded in smaller wearable devices, such as fitness trackers. About 30 subjects from two different organizations were provided with smartphones. The study lasted for eight weeks and was conducted in real working environments, with no constraints whatsoever placed upon smartphone usage. The subjects reported their perceived stress levels three times during their working hours. Using combination of statistical models to classify selfreported stress levels, we achieved a maximum overall accuracy of 71% for user-specific models and an accuracy of 60% for the use of similar-users models, relying solely on data from a single accelerometer.