文献类型：期刊文献

英文题名：Intelligent Emotion Detection Method Based on Deep Learning in Medical and Health Data

作者：Xu, Jianqiang[1];Hu, Zhujiao[2];Zou, Junzhong[1];Bi, Anqi[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Fudan Univ, Sch Microelect, Shanghai 201203, Peoples R China;[3]Changshu Inst Technol, Sch Comp Sci & Engn, Suzhou 215500, Peoples R China

年份：2020

卷号：8

起止页码：3802

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20200408087108);WOS:【SCI-EXPANDED(收录号:WOS:000549765500001)】；

基金：This work was supported in part by the Fund of Minhang District Human Resources and Social Security Bureau, in part by the Wireless Intelligent Handheld Terminal Based on RFID Technology under Grant 11C26213100798 and in part by the RFID intelligent handheld mobile terminal and solution for food and drug traceability system under Grant 1401H122500, in part by the Jiangsu University Natural Science Research Project under Grant 18kjb5200001, and in part by the Project Fund of Shanghai Economic and Information Commission and the application of artifIcial intelligence in new retail.

语种：英文

外文关键词：Emotion detection model; multi-channel convolutional aotoencoder (MCAE); medical health; deep learning; emotional text features; intelligent data analysis

摘要：Emotional abnormality may be brought out by physiological fatigue. In order to solve the problem, an emotion detection method based on deep learning in medical and health data is proposed in this paper. First of all, the related content of emotional fatigue is studied. The concept and the classification of emotional fatigue are introduced. Then, a multi-modal data emotional fatigue detection system is designed. In the system, multi-channel convolutional aotoencoder neural network is used to extract electrocardiograms (ECG) data features and emotional text features for emotional fatigue detection. Secondly, the network structure of learning ECG features by multi-channel convolutional aotoencoder model is introduced in detail. And the network structure of learning emotional text features by convolutional aotoencoder model is also described in detail. Finally, multi-modal data features are combined for emotional detection. It is shown by the experimental results that the proposed model has an average accuracy of more than 85% in predicting emotional fatigue.