A Convolution Neural Network Based Emotion Recognition System using Multimodal Physiological Signals

Cheng Jie Yang, Nicolas Fahier, Wei Chih Li, Wai Chi Fang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The detection and recognition of human emotional states have raised recent research interests for various applications from e-learning to chronic health conditions prevention. In this paper, we proposed an emotion recognition system based on the electrocardiogram (ECG) and photoplethysmogram (PPG) signals as objectives data input sources. Three emotion states (positive, neutral, negative) were defined as classification outputs. The training and validation data were collected by Kaohsiung Medical University (KMU) from 47 participants aged from 30 to 50 years old diagnosed with chronic cardiovascular health conditions. A convolution neural network (CNN) was built to efficiently map the subject's emotions with the extracted features from both ECG and PPG signals. This emotion recognition system achieved an accuracy of 75.4% for 3 classes outputs higher or similar than other models used in other works.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173993
DOIs
StatePublished - 28 Sep 2020
Event7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020 - Taoyuan, Taiwan
Duration: 28 Sep 202030 Sep 2020

Publication series

Name2020 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020

Conference

Conference7th IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2020
CountryTaiwan
CityTaoyuan
Period28/09/2030/09/20

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