Fabrication of a portable device for stress monitoring using wearable sensors and soft computing algorithms

摘要

Stress is an issue that everyone experiences in today's modern life. Prolonged exposure to stress can cause many mental and physical diseases. Accordingly, the stress management issue has become popular, and the need for personal healthcare devices has increased in recent years. Therefore, the aim of this research is to design and manufacture a portable stress monitoring system, based on photoplethysmography (PPG) and galvanic skin response (GSR) physiological signals, acquired by wearable sensors. To do so, we proposed a novel algorithm for continuous measurement of the stress index (SI) as well as the classification of stress levels. In order to estimate an accurate value for SI, various soft computing algorithms such as support vector regression, artificial neural networks (ANN), and adaptive neuro-fuzzy inference system (ANFIS) were adopted for modeling the stress based on the features extracted from normalized and non-normalized types of PPG and GSR signals and their combinations. Furthermore, K-nearest neighbor (KNN), ANNs, Naive Bayes, and support vector machine (SVM) were utilized to discriminate different levels of stress in subjects. The obtained results indicate that the ANFIS algorithm can estimate the SI training output with the correlation coefficient (CC) of 0.9281 and the average relative error of 0.23 on a subset of the combined features of PPG and GSR signals. Also, the best classification performance was for KNN (K=3) algorithm, with 85.3% accuracy. To evaluate the developed system, data of 16 subjects, out of the training dataset, participated in the experiment in the presence of the experts and psychologists, were used. The average CC of 0.81 and classification accuracy of 75% were obtained, using the implemented ANFIS model and KNN classifier.