Martinez R, Irigoyen E, Arruti A, Martin J I, Muguerza J
University of the Basque Country (UPV/EHU), Bilbao, Spain.
Comput Methods Programs Biomed. 2017 Sep;148:81-90. doi: 10.1016/j.cmpb.2017.06.010. Epub 2017 Jun 29.
Detection and labelling of an increment in the human stress level is a contribution focused principally on improving the quality of life of people. This work is aimed to develop a biophysical real-time stress identification and classification system, analysing two noninvasive signals, the galvanic skin response and the heart rate variability.
An experimental procedure was designed and configured in order to elicit a stressful situation that is similar to those found in real cases. A total of 166 subjects participated in this experimental stage. The set of registered signals of each subject was considered as one experiment. A preliminary qualitative analysis of the signals collected was made, based on previous counselling received from neurophysiologists and psychologists. This study revealed a relationship between changes in the temporal signals and the induced stress states in each subject. To identify and classify such states, a subsequent quantitative analysis was performed in order to determine specific numerical information related to the above mentioned relationship. This second analysis gives the particular details to design the finally proposed classification algorithm, based on a Finite State Machine.
The proposed system is able to classify the detected stress stages at three levels: low, medium, and high. Furthermore, the system identifies persistent stress situations or momentary alerts, depending on the subject's arousal. The system reaches an F score of 0.984 in the case of high level, an F score of 0.970 for medium level, and an F score of 0.943 for low level.
The resulting system is able to detect and classify different stress stages only based on two non invasive signals. These signals can be collected in people during their monitoring and be processed in a real-time sense, as the system can be previously preconfigured. Therefore, it could easily be implemented in a wearable prototype that could be worn by end users without feeling to be monitored. Besides, due to its low computational, the computation of the signals slopes is easy to do and its deployment in real-time applications is feasible.
检测并标记人类压力水平的增加主要是为了提高人们的生活质量。这项工作旨在开发一种生物物理实时压力识别与分类系统，分析两种非侵入性信号，即皮肤电反应和心率变异性。
设计并配置了一个实验程序，以引发与实际情况中类似的压力情境。共有166名受试者参与了这个实验阶段。每个受试者的一组注册信号被视为一次实验。基于从神经生理学家和心理学家那里获得的先前建议，对收集到的信号进行了初步定性分析。这项研究揭示了时间信号的变化与每个受试者的诱发压力状态之间的关系。为了识别和分类这些状态，随后进行了定量分析，以确定与上述关系相关的特定数值信息。这第二次分析给出了设计最终提出的基于有限状态机的分类算法的具体细节。
所提出的系统能够将检测到的压力阶段分为三个级别：低、中、高。此外，该系统根据受试者的唤醒程度识别持续的压力情况或瞬间警报。该系统在高级别情况下的F分数为0.984，中级别为0.970，低级别为0.943。
最终的系统仅基于两种非侵入性信号就能检测和分类不同的压力阶段。这些信号可以在人们被监测期间收集，并实时进行处理，因为该系统可以预先进行预配置。因此，它可以很容易地在可穿戴原型中实现，终端用户佩戴时不会感觉被监测。此外，由于其计算量低，信号斜率的计算很容易进行，并且在实时应用中的部署是可行的。
