Stress State Evaluation by an Improved Support Vector Machine

Abstract

Effective methods of evaluation of psychological pressure can detect and assess real-time stress states, warning people to pay necessary attention to their health. This study is focused on the stress assessment issue using an improved support vector machine (SVM) algorithm based on surface electromyographic signals. After the samples were clustered, the cluster results were fed to the loss function of the SVM to screen training samples. With the imbalance among the training samples after screening, a weight was given to the loss function to reduce the prediction tendentiousness of the classifier and, therefore, to decrease the error of the training sample and make up for the influence of the unbalanced samples. This improved the algorithm, increased the classification accuracy from 73.79% to 81.38%, and reduced the running time from 1973.1 to 540.2 sec. The experimental results show that this algorithm can help to effectively avoid the influence of individual differences on the stress appraisal effect and to reduce the computational complexity during the training phase of the classifier.