Analysis of Physiological Signals for Emotion Recognition Based on Support Vector Machine

Vanny, Makara; Park, Seung Min; Ko, Kwang-Eun; Sim, Kwee-Bo

doi:10.1007/978-3-642-37374-9_12

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Meanwhile, Eigenvalue and Eigenvector methods [65] are another option for feature extraction methods. Eigenvector only obtains frequency data from the sinusoid signal.…”

Section: -2-1-feature Extractionmentioning

confidence: 99%

A Systematic Review on Emotion Recognition System Using Physiological Signals: Data Acquisition and Methodology

et al. 2022

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Emotion recognition systems (ERS) have become a popular research field to contribute to human-machine interaction in different areas. Different kinds of applications on ERS can serve different purposes. Artificial intelligence (AI) and the internet of things (IoT) are the technologies behind such applications. The main objective of this study is to enable researchers and developers to search for the most suitable options to develop an emotional state recognition system. More specifically, this paper presents work on ERS, which is built using physiological signals extracted from biosensors. It also presents details of how the extracted physiological signals are used to identify the user's emotional state. In this review, the sensors are categorized based on their modality: contact-based sensors and contactless sensors. Next, the ERS process is presented together with the reported results for each described technique. Articles from four different research databases were reviewed, of which 147 articles from 2009 to 2021 were referred to that are related to ERS using physiological signals. This paper should be significant for researchers developing systems that integrate human emotion recognition capability. The findings reported here can guide them in choosing suitable methods for their systems. Doi: 10.28991/ESJ-2022-06-05-017 Full Text: PDF

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“…Meanwhile, Eigenvalue and Eigenvector methods [65] are another option for feature extraction methods. Eigenvector only obtains frequency data from the sinusoid signal.…”

Section: -2-1-feature Extractionmentioning

confidence: 99%

A Systematic Review on Emotion Recognition System Using Physiological Signals: Data Acquisition and Methodology

et al. 2022

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“…Regarding the automated recognition of emotional states, it is usually performed based on two methodologies [ 2 , 10 , 11 ]: (1) Traditional Machine Learning (ML) techniques [ 12 , 13 , 14 ]; (2) Deep learning approaches [ 15 , 16 , 17 ]. Due to the limited size of existing datasets, most of the work focuses on traditional ML algorithms, in particular Supervised Learning (SL), such as Support Vector Machines (SVM) [ 18 , 19 , 20 ], k-Nearest Neighbour (kNN) [ 21 , 22 , 23 ], Decision Trees (DT) [ 24 , 25 ], and others [ 26 , 27 ], with the SVM method being the most commonly applied algorithm, showing overall good results and low computational complexity.…”

Section: State Of the Artmentioning

confidence: 99%

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Bota

Wang²,

Fred

et al. 2020

Sensors

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Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP). The main contribution of our work is a systematic study across five public datasets commonly used in the Emotion Recognition (ER) state-of-the-art, namely: (1) Classification performance analysis of ER benchmarking datasets in the arousal/valence space; (2) Summarising the ranges of the classification accuracy reported across the existing literature; (3) Characterising the results for diverse classifiers, sensor modalities and feature set combinations for ER using accuracy and F1-score; (4) Exploration of an extended feature set for each modality; (5) Systematic analysis of multimodal classification in DF and FF approaches. The experimental results showed that FF is the most competitive technique in terms of classification accuracy and computational complexity. We obtain superior or comparable results to those reported in the state-of-the-art for the selected datasets.

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Advances in Emotional Picture Classification

Zhang

2014

Encyclopedia of Information Science and Technology, Third Edition

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Analysis of Physiological Signals for Emotion Recognition Based on Support Vector Machine

Cited by 3 publications

References 4 publications

A Systematic Review on Emotion Recognition System Using Physiological Signals: Data Acquisition and Methodology

A Systematic Review on Emotion Recognition System Using Physiological Signals: Data Acquisition and Methodology

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Advances in Emotional Picture Classification

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