Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Min Seop | - |
dc.contributor.author | Lee, Yun Kyu | - |
dc.contributor.author | Pae, Dong Sung | - |
dc.contributor.author | Lim, Myo Taeg | - |
dc.contributor.author | Kim, Dong Won | - |
dc.contributor.author | Kang, Tae Koo | - |
dc.date.accessioned | 2021-09-01T10:10:28Z | - |
dc.date.available | 2021-09-01T10:10:28Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2019-08 | - |
dc.identifier.issn | 2076-3417 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/63659 | - |
dc.description.abstract | Physiological signals contain considerable information regarding emotions. This paper investigated the ability of photoplethysmogram (PPG) signals to recognize emotion, adopting a two-dimensional emotion model based on valence and arousal to represent human feelings. The main purpose was to recognize short term emotion using a single PPG signal pulse. We used a one-dimensional convolutional neural network (1D CNN) to extract PPG signal features to classify the valence and arousal. We split the PPG signal into a single 1.1 s pulse and normalized it for input to the neural network based on the personal maximum and minimum values. We chose the dataset for emotion analysis using physiological (DEAP) signals for the experiment and tested the 1D CNN as a binary classification (high or low valence and arousal), achieving the short-term emotion recognition of 1.1 s with 75.3% and 76.2% valence and arousal accuracies, respectively, on the DEAP data. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.subject | CLASSIFICATION | - |
dc.subject | EXTRACTION | - |
dc.title | Fast Emotion Recognition Based on Single Pulse PPG Signal with Convolutional Neural Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Lim, Myo Taeg | - |
dc.identifier.doi | 10.3390/app9163355 | - |
dc.identifier.scopusid | 2-s2.0-85070859847 | - |
dc.identifier.wosid | 000484444100149 | - |
dc.identifier.bibliographicCitation | APPLIED SCIENCES-BASEL, v.9, no.16 | - |
dc.relation.isPartOf | APPLIED SCIENCES-BASEL | - |
dc.citation.title | APPLIED SCIENCES-BASEL | - |
dc.citation.volume | 9 | - |
dc.citation.number | 16 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Materials Science | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Materials Science, Multidisciplinary | - |
dc.relation.journalWebOfScienceCategory | Physics, Applied | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | EXTRACTION | - |
dc.subject.keywordAuthor | short term emotion recognition | - |
dc.subject.keywordAuthor | one-dimensional convolutional neural network | - |
dc.subject.keywordAuthor | PPG | - |
dc.subject.keywordAuthor | personal normalization | - |
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