Affiliation:
1. School of Design Jiangnan University, Jiangsu Wuxi, 214122, China
2. Art and Design Department Nanjing Institute of Technology, 211167, China
Abstract
At present, the demand for intelligentization of human-computer interaction systems (HCIS) has become increasingly prominent. Being able to recognize the emotions of users of interactive systems is a distinguishing feature of intelligent interactive systems. The intelligent HCIS can
analyze the emotional changes of patients with depression, complete the interaction with the patients in a more appropriate manner, and the recognition results can assist family members or medical personnel to make response measures based on the patient’s emotional changes. Based on
this background, this paper proposes a sentiment recognition method based on transfer support vector machines (TSVM) and EEG signals. The ER (ER) results based on this method are applied to HCIS. Such a HCIS is mainly used for the interaction of patients with depression. When a new field related
to a certain field appears, if the new field data is relabeled, the sample is expensive, and it is very wasteful to discard all the old field data. The main innovation of this research is that the introduced classification model is TSVM. TSVM is a transfer learning strategy based on SVM. Transfer
learning aims to solve related but different target domain problems by using a large amount of labeled source domain data. Therefore, the transfer support vector machine based on the transfer mechanism can use the small labeled data of the target domain and a large amount of old data in the
related domain to build a high-quality classification model for the target domain, which can effectively improve the accuracy of classification. Comparing the classification results with other classification models, it can be concluded that TSVM can effectively improve the accuracy of ER in
patients with depression. The HCIS based on the classification model has higher accuracy and better stability.
Publisher
American Scientific Publishers
Subject
Health Informatics,Radiology, Nuclear Medicine and imaging
Cited by
9 articles.
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