Acute Stress State Classification Based on Electrodermal Activity Modeling

Abstract

Acute stress is a physiological condition that may induce several neural dysfunctions with a significant impact on life quality. Accordingly, it would be important to monitor stress in everyday life unobtrusively and inexpensively. In this paper, we presented a new methodological pipeline to recognize acute stress conditions using electrodermal activity (EDA) exclusively. Particularly, we combined a rigorous and robust model (cvxEDA) for EDA processing and decomposition, with an algorithm based on a support vector machine to classify the stress state at a singlesubject level. Indeed, our method, based on a single sensor, is robust to noise, applies a rigorous phasic decomposition, and implements an unbiased multiclass classification. To this end, we analyzed the EDA of 65 volunteers subjected to different acute stress stimuli induced by a modified version of the Trier Social Stress Test. Our results show that stress is successfully detected with an average accuracy of 94.62%. Besides, we proposed a further 4-class pattern recognition system able to distinguish between non-stress condition and three different stressful stimuli achieving an average accuracy as high as 75.00%. These results, obtained under controlled conditions, are the first step towards applications in ecological scenarios.

Towards a Contactless Stress Classification Using Thermal Imaging

“…To study the effect on the SNS activity, we based on the analysis of the EDA. Indeed, many of these features were found to be particularly suitable for stress monitoring [ 10 , 55 ]. In our study, we observed an increase in the mean value of the tonic component ( TonicMean ) during the Stroop session, which is likely to reflect the overall increase of the subject’s arousal level.…”

“…Accordingly, many studies have attempted to infer the psychological state of a subject in a more reliable and unbiased manner by analyzing ANS peripheral correlates [ 9 ]. Among these, the electrodermal activity (EDA) could provide a reliable metric of stress, and it has been used as a ground-truth to compare the performance of other signals [ 10 , 11 , 12 , 13 ]. However, in view of a more comprehensive ANS assessment, a combination of autonomic measures, such as respiratory (RESP), cardiac, and EDA signals, is often adopted in the scientific literature, by using multiple wearable devices [ 14 , 15 ].…”

Mental Stress Assessment Using Ultra Short Term HRV Analysis Based on Non-Linear Method

“…Stress measurement using physiological signals can compensate for the shortcomings of other methods and has been actively studied in recent years to detect the physiological responses that change due to the presence of a stressor. Stress measurement studies are mainly conducted on physiological signals, such as ECG [ 11 , 12 ], GSR [ 13 , 14 ], and EEG [ 15 , 16 ], and some studies have described the combined use of several physiological signals [ 17 , 18 ].…”

“…When non-stress and stress groups were classified, the highest reported accuracy was 98.76% [ 15 ]. Another stress classification study using GSR signals obtained an average classification accuracy of 94.62% based on the cvxEDA method, which is a rigorous and robust model [ 14 ]. Most previous studies were conducted using a single physiological signal, such as EEG or GSR; have a small number of subjects or data imbalance; and their application might not be suitable for real-life scenarios.…”

Longitudinal estimation of stress-related states through bio-sensor data

“…Algorithms for stress identification may follow supervised [10,11] or unsupervised [2,3] learning approaches. They involve individual [12,13] and ensemble learning [14,15] models. Algorithmic training can be conducted in a laboratory (LAB) setting [13,16] or in an uncontrolled open environment, also known as "in the wild" (IW) [11,15,17].…”