Informatics and Telematics Institute, Centre for Research and Technology Hellas, Thermi, Thessaloniki, Greece.
PLoS One. 2012;7(9):e43571. doi: 10.1371/journal.pone.0043571. Epub 2012 Sep 19.
This paper introduces activity-related behavioural features that can be automatically extracted from a computer system, with the aim to increase the effectiveness of automatic stress detection. The proposed features are based on processing of appropriate video and accelerometer recordings taken from the monitored subjects. For the purposes of the present study, an experiment was conducted that utilized a stress-induction protocol based on the stroop colour word test. Video, accelerometer and biosignal (Electrocardiogram and Galvanic Skin Response) recordings were collected from nineteen participants. Then, an explorative study was conducted by following a methodology mainly based on spatiotemporal descriptors (Motion History Images) that are extracted from video sequences. A large set of activity-related behavioural features, potentially useful for automatic stress detection, were proposed and examined. Experimental evaluation showed that several of these behavioural features significantly correlate to self-reported stress. Moreover, it was found that the use of the proposed features can significantly enhance the performance of typical automatic stress detection systems, commonly based on biosignal processing.
本文介绍了可以从计算机系统中自动提取的与活动相关的行为特征，旨在提高自动压力检测的有效性。所提出的特征基于对从被监测对象获取的适当视频和加速度计记录的处理。为此目的，进行了一项实验，该实验利用基于斯特鲁普颜色词测试的应激诱导方案。从十九名参与者那里收集了视频、加速度计和生物信号（心电图和皮肤电反应）记录。然后，通过主要基于从视频序列中提取的时空描述符（运动历史图像）的方法进行了探索性研究。提出并检查了一组可能对自动压力检测有用的大量与活动相关的行为特征。实验评估表明，这些行为特征中的几个与自我报告的压力显著相关。此外，还发现使用所提出的特征可以显著提高基于生物信号处理的典型自动压力检测系统的性能。
