Stress State Evaluation by an Improved Support Vector Machine

• Date: 2016
• Main Authors: Xin, L., Zetao, Ch., Yunpeng, Zh., Jiali, X., Shuicai, W., Yanjun, Z.
• Format: Article
• Language: English
• Published: Інститут фізіології ім. О.О. Богомольця НАН України 2016
• Series: Нейрофизиология
• Online Access: https://nasplib.isofts.kiev.ua/handle/123456789/148339
• Journal Title: Digital Library of Periodicals of National Academy of Sciences of Ukraine
• Cite this: Stress State Evaluation by an Improved Support Vector Machine / L. Xin, Ch. Zetao, Zh. Yunpeng, X. Jiali, W. Shuicai, Z. Yanjun // Нейрофизиология. — 2016. — Т. 48, № 2. — С. 96-102. — Бібліогр.: 15 назв. — англ.

Effective methods of evaluation of the psychological pressure can detect and assess realtime 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 on the base of surface electromyographic signals. After the samples were clustered, the cluster results were given to the loss function of the SVM to screen training samples. With the imbalance amongst 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. Experimental results show that this algorithm can help to effectively avoid the influence of individual differences on a stress appraisal effect and to reduce the computational complexity during the training phase of the classifier