کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4965496 | 1448369 | 2017 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Respiration-based emotion recognition with deep learning
ترجمه فارسی عنوان
شناخت احساسات مبتنی بر احساس با درک عمیق
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کلمات کلیدی
شناخت احساسی، یادگیری عمیق، محاسبات پوشیدنی، تنفس، نظریه تحریک و تحریک
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Different physiological signals are of different origins and may describe different functions of the human body. This paper studied respiration (RSP) signals alone to figure out its ability in detecting psychological activity. A deep learning framework is proposed to extract and recognize emotional information of respiration. An arousal-valence theory helps recognize emotions by mapping emotions into a two-dimension space. The deep learning framework includes a sparse auto-encoder (SAE) to extract emotion-related features, and two logistic regression with one for arousal classification and the other for valence classification. For the development of this work an international database for emotion classification known as Dataset for Emotion Analysis using Physiological signals (DEAP) is adopted for model establishment. To further evaluate the proposed method on other people, after model establishment, we used the affection database established by Augsburg University in Germany. The accuracies for valence and arousal classification on DEAP are 73.06% and 80.78% respectively, and the mean accuracy on Augsburg dataset is 80.22%. This study demonstrates the potential to use respiration collected from wearable deices to recognize human emotions.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Industry - Volumes 92â93, November 2017, Pages 84-90
Journal: Computers in Industry - Volumes 92â93, November 2017, Pages 84-90
نویسندگان
Qiang Zhang, Xianxiang Chen, Qingyuan Zhan, Ting Yang, Shanhong Xia,