Recent Survey on Emotion Recognition Using Physiological Signals

Joy, Emily; Joseph, Rehna Baby; Lakshmi, M.; Joseph, Willson; Rajeswari, M.

doi:10.1109/icaccs51430.2021.9441999

Cited by 8 publications

(5 citation statements)

References 12 publications

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“…In addition to conveying the desired information from the sound of words, the speaker also shares information via tone of voice, energy, speed, and other acoustic properties, which help the receiver gauge the intentions and emotions of that communication. On the other hand, facial expressions and body gestures are the most common ways of identifying [ 29 ] emotion. Research from the literature supports the existence of a universally recognized set of facial expressions for emotions such as happiness, surprise, fear, sadness, anger, and disgust [ 29 ].…”

Section: Emotion Recognitionmentioning

confidence: 99%

“…On the other hand, facial expressions and body gestures are the most common ways of identifying [ 29 ] emotion. Research from the literature supports the existence of a universally recognized set of facial expressions for emotions such as happiness, surprise, fear, sadness, anger, and disgust [ 29 ]. In addition, research based on body movement, posture, and gestures have grown in recent years, given the possibility of recognizing emotions at a distance.…”

Section: Emotion Recognitionmentioning

confidence: 99%

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Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals

Quispe

Utyiama

Santos

et al. 2022

Sensors

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The use of machine learning (ML) techniques in affective computing applications focuses on improving the user experience in emotion recognition. The collection of input data (e.g., physiological signals), together with expert annotations are part of the established standard supervised learning methodology used to train human emotion recognition models. However, these models generally require large amounts of labeled data, which is expensive and impractical in the healthcare context, in which data annotation requires even more expert knowledge. To address this problem, this paper explores the use of the self-supervised learning (SSL) paradigm in the development of emotion recognition methods. This approach makes it possible to learn representations directly from unlabeled signals and subsequently use them to classify affective states. This paper presents the key concepts of emotions and how SSL methods can be applied to recognize affective states. We experimentally analyze and compare self-supervised and fully supervised training of a convolutional neural network designed to recognize emotions. The experimental results using three emotion datasets demonstrate that self-supervised representations can learn widely useful features that improve data efficiency, are widely transferable, are competitive when compared to their fully supervised counterparts, and do not require the data to be labeled for learning.

show abstract

Section: Emotion Recognitionmentioning

confidence: 99%

Section: Emotion Recognitionmentioning

confidence: 99%

Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals

Quispe

Utyiama

Santos

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The authors also discuss selected works on emotional recognition by physiological signals in wearable devices. In [ 28 ], different emotion recognition methods using physiological signals by machine learning techniques were explained. This paper also reviewed different stages such as data collection, data processing, feature extraction and classification models for each recognition method.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Physiological Signal-Based Emotion Recognition

Ahmad

Khan

2022

Bioengineering

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Physiological signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as pre-processing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data pre-processing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally, we discuss key challenges and future directions in this field.

show abstract

“…The authors also discuss selected works on emotional recognition by physiological signals in wearable devices. In [25], different emotion recognition methods using physiological signals by machine learning techniques were explained. This paper also reviewed different stages like data collection, data processing, feature extraction and classification models for each recognition method.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Physiological Signal Based Emotion Recognition

Ahmad¹,

Khan²

2022

Preprint

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Physiological Signals are the most reliable form of signals for emotion recognition, as they cannot be controlled deliberately by the subject. Existing review papers on emotion recognition based on physiological signals surveyed only the regular steps involved in the workflow of emotion recognition such as preprocessing, feature extraction, and classification. While these are important steps, such steps are required for any signal processing application. Emotion recognition poses its own set of challenges that are very important to address for a robust system. Thus, to bridge the gap in the existing literature, in this paper, we review the effect of inter-subject data variance on emotion recognition, important data annotation techniques for emotion recognition and their comparison, data preprocessing techniques for each physiological signal, data splitting techniques for improving the generalization of emotion recognition models and different multimodal fusion techniques and their comparison. Finally we discuss key challenges and future directions in this field.

show abstract

Recent Survey on Emotion Recognition Using Physiological Signals

Cited by 8 publications

References 12 publications

Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals

Applying Self-Supervised Representation Learning for Emotion Recognition Using Physiological Signals

A Survey on Physiological Signal-Based Emotion Recognition

A Survey on Physiological Signal Based Emotion Recognition

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