Stress Detection in computer users baed on DSP of noninvasive physiological signals

Feature Extraction Algorithm (flowchart)

Comments on " Stress Detection in Computer Users based on digital signal processing of Noninvasive physiological variables," Jing Zhai, Armando Barreto, Proceedings of the 28th IEEE EMBS Annual International Conference, New York City, USA, Aug 30-Sept 3, 2006. pp. 1335-1358.

Detect: mental or cognitive stress associated with computer interaction.

physiological signals:
Galvanic Skin response (GSR)
Blood Volume Pulse (BVP)
Pupil Diameter (PD)
Skin temperature (ST)

classification strategy: SVM based, to classify between "stressed" and "relaxed" response.

Dataset:
32 students (ages 21-42).

Procedure:
first 5 minutes, subjects were shown 30 still emotionally neutral pictures to relax.
then subjected to "Paced Stroop Test" (http://en.wikipedia.org/wiki/Stroop_task). The subjects had 3 seconds to answer with a mouse click.

Features:
sampling rate: 360 Hz.
from BVP: based on the InterbeatInterval (IBI calculations) and power spectrum analysis: (4 features)
L/H ratio (low frequency: 0.05-0.15Hz, high frequency: 0.16-0.40Hz)
Mean IBI
standard deviation of IBI
amplitude of BV
from GSR: based on response detection: (5 features)
# of response
mean value of GSR
amplitude response
rising time of response
energy of response
from ST: after low pass filtering: (1 feature)
slope of ST
from PD: based on linear interpolation of PD samples (1 feature)
mean value of PD

"to account for differences in the initial arousal levels due to individual differences, normalization of the data was needed prior to use of features, between [0, 1]"

Classification: Support Vector machines (weka software). the classification performance was evaluated using 20-fold cross validation, 20 samples were pulled out as test samples, and the remaining samples were sued to train the classifiers.

the authors have also compared SVM based classifier with naive-based classifier and a decision tree classifier.
The authors were mainly interested in determining the added recognition capability that can achieved with pupil diameter measurements (in junction with other physiological signals).