Giakoumis Dimitris, Drosou Anastasios, Cipresso Pietro, Tzovaras Dimitrios, Hassapis George, Gaggioli Andrea, Riva Giuseppe
Centre for Research and Technology Hellas, Thermi, Thessaloniki, Greece.
Stud Health Technol Inform. 2012;181:287-91.
We have developed a system, allowing real-time monitoring of human gestures, which can be used for the automatic recognition of behavioural correlates of psychological stress. The system is based on a low-cost camera (Microsoft Kinect), which provides video recordings capturing the subject's upper body activity. Motion History Images (MHIs) are calculated in real-time from these recordings. Appropriate algorithms are thereafter applied over the MHIs, enabling the real-time calculation of activity-related behavioural parameters. The system's efficiency in real-time calculation of behavioural parameters has been tested in a pilot trial, involving monitoring of behavioural parameters during the induction of mental stress. Results showed that our prototype is capable to effectively calculate simultaneously eight different behavioural parameters in real-time. Statistical analysis indicated significant correlations between five of these parameters and self-reported stress. The preliminary findings suggest that our approach could potentially prove useful within systems targeting automatic stress detection, through unobtrusive monitoring of subjects.
我们开发了一个能够实时监测人类手势的系统，该系统可用于自动识别心理压力的行为关联。该系统基于一台低成本相机（微软Kinect），它能提供捕捉受试者上半身活动的视频记录。运动历史图像（MHI）从这些记录中实时计算得出。随后，合适的算法应用于运动历史图像上，从而能够实时计算与活动相关的行为参数。该系统在行为参数实时计算方面的效率已在一项初步试验中进行了测试，该试验涉及在诱发精神压力期间监测行为参数。结果表明，我们的原型能够有效地实时同时计算八个不同的行为参数。统计分析表明，其中五个参数与自我报告的压力之间存在显著相关性。初步研究结果表明，我们的方法通过对受试者进行非侵入式监测，在旨在自动检测压力的系统中可能会被证明是有用的。
