Wu Min, Cao Hong, Nguyen Hai-Long, Surmacz Karl, Hargrove Caroline
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:1625-8. doi: 10.1109/EMBC.2015.7318686.
Discovering and modeling of stress patterns of human beings is a key step towards achieving automatic stress monitoring, stress management and healthy lifestyle. As various wearable sensors become popular, it becomes possible for individuals to acquire their own relevant sensory data and to automatically assess their stress level on the go. Previous studies for stress analysis were conducted in the controlled laboratory and clinic settings. These studies are not suitable for stress monitoring in one's daily life as various physical activities may affect the physiological signals. In this paper, we address such issue by integrating two modalities of sensors, i.e., HRV sensors and accelerometers, to monitor the perceived stress levels in daily life. We gathered both the heart and the motion data from 8 participants continuously for about 2 weeks. We then extracted features from both sensory data and compared the existing machine learning methods for learning personalized models to interpret the perceived stress levels. Experimental results showed that Bagging classifier with feature selection is able to achieve a prediction accuracy 85.7%, indicating our stress monitoring on daily basis is fairly practical.
发现并建模人类的压力模式是实现自动压力监测、压力管理和健康生活方式的关键一步。随着各种可穿戴传感器的普及，个人有可能获取自己的相关传感数据，并在移动过程中自动评估自己的压力水平。以往的压力分析研究是在受控的实验室和临床环境中进行的。由于各种身体活动可能会影响生理信号，这些研究并不适合在日常生活中进行压力监测。在本文中，我们通过整合两种传感器模式，即心率变异性（HRV）传感器和加速度计，来解决这个问题，以监测日常生活中的感知压力水平。我们连续约两周收集了8名参与者的心脏和运动数据。然后，我们从两种传感数据中提取特征，并比较现有的机器学习方法来学习个性化模型，以解释感知到的压力水平。实验结果表明，带有特征选择的Bagging分类器能够达到85.7%的预测准确率，表明我们的日常压力监测相当实用。
