Stress State Evaluation by Improved Support Vector Machine

Chronic mental pressure affects human health directly by causing a series of pathological and physiological risks. Effective methods of evaluating psychological pressure can detect and assess real-time stress states, warning people to pay close attention to their health. Focusing on stress assessment, this study improved the support vector machine (SVM) algorithm to assess the stress state via surface electromyographic signals. After the samples were clustered, the cluster results were given to the loss function of SVM to screen training samples. With the imbalance problem after screening, a weight was given to the loss function to reduce the prediction tendentiousness of the classifier, thereby decreasing the error of the training sample and compensating for the influence of the unbalanced samples. This improved algorithm increased the classification accuracy from 68% to 79% and reduced the running time from 2026.5 s to 541.3 s. Experimental results show that this algorithm can effectively avoid the influence of individual differences on stress appraisal effect and reduce the computational complexity during the training phase of the classifier.