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Table 1 Summary of literature review on stress prediction systems

From: Medically-oriented design for explainable AI for stress prediction from physiological measurements

Measurements

Prediction model

Stress prediction accuracy

Paper

Accelerometer, 34 features from the time and frequency domains of accelerometer data

Naives Bayes, Decision Trees, and Random Forest Classifiers

Highest accuracy 71% using decision trees

[21]

Accelerometer, GSR, ECG and behavioral features

LDA (Linear Discriminant Analysis)-based classifier

Prediction based on the physiological data and the behavioral features was more accurate than prediction based on physiological data alone

[22]

Accelerometer, GSR, ECG

Decision Tree Classifier

92.4% for 10-fold cross validation

[23]

Accelerometer, video camera, pressure-sensitive touchscreens

J48 tree

78% in classifying touches as stressed versus not stressed

[24]

Call logs, Bluetooth data, and SMS data from users’ mobile phones

Random Forest Classifier

72.39% for binary classification, stressed versus not stressed

[25]

Physiological data collected from chest-worn and wrist-worn sensors

Deep Convolutional Neural Network

99.80% accuracy rates for binary classification for stress detection

[27]