The emotional state classification using physiological signal interpretation framework

Rattanadoung, Kitimapond; Champrasert, Paskorn; Aramkul, Somrawee

doi:10.1109/icsigsys.2018.8373573

Cited by 3 publications

(5 citation statements)

References 23 publications

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“…A physiological signal interpretation framework, Emo-CSI, is presented for emotional classification in [37] which uses heart rate, respiration pattern, skin humidity, and strength to recognize emotions of pleasure, displeasure, calm, neutral, and excited emotions. Twentythree subjects with ages from 20 to 27 are included in the data collection process seconds comprising ten males and thirteen females.…”

Section: Related Workmentioning

confidence: 99%

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Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

Siddiqui

Shahzad

Saleem

et al. 2021

Sensors

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Emotion recognition gained increasingly prominent attraction from a multitude of fields recently due to their wide use in human-computer interaction interface, therapy, and advanced robotics, etc. Human speech, gestures, facial expressions, and physiological signals can be used to recognize different emotions. Despite the discriminating properties to recognize emotions, the first three methods have been regarded as ineffective as the probability of human’s voluntary and involuntary concealing the real emotions can not be ignored. Physiological signals, on the other hand, are capable of providing more objective, and reliable emotion recognition. Based on physiological signals, several methods have been introduced for emotion recognition, yet, predominantly such approaches are invasive involving the placement of on-body sensors. The efficacy and accuracy of these approaches are hindered by the sensor malfunctioning and erroneous data due to human limbs movement. This study presents a non-invasive approach where machine learning complements the impulse radio ultra-wideband (IR-UWB) signals for emotion recognition. First, the feasibility of using IR-UWB for emotion recognition is analyzed followed by determining the state of emotions into happiness, disgust, and fear. These emotions are triggered using carefully selected video clips to human subjects involving both males and females. The convincing evidence that different breathing patterns are linked with different emotions has been leveraged to discriminate between different emotions. Chest movement of thirty-five subjects is obtained using IR-UWB radar while watching the video clips in solitude. Extensive signal processing is applied to the obtained chest movement signals to estimate respiration rate per minute (RPM). The RPM estimated by the algorithm is validated by repeated measurements by a commercially available Pulse Oximeter. A dataset is maintained comprising gender, RPM, age, and associated emotions which are further used with several machine learning algorithms for automatic recognition of human emotions. Experiments reveal that IR-UWB possesses the potential to differentiate between different human emotions with a decent accuracy of 76% without placing any on-body sensors. Separate analysis for male and female participants reveals that males experience high arousal for happiness while females experience intense fear emotions. For disgust emotion, no large difference is found for male and female participants. To the best of the authors’ knowledge, this study presents the first non-invasive approach using the IR-UWB radar for emotion recognition.

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Section: Related Workmentioning

confidence: 99%

“…68.5% for arousal, 68.75% for valence [37] Statistical features average, maximum, minimum, and standard deviation etc.…”

Section: Referencementioning

confidence: 99%

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

Siddiqui

Shahzad

Saleem

et al. 2021

Sensors

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“…The Self-Assessment Manikin (SAM) questionnaire is adopted in [28] to assess the participants' emotions for training the emotion classifier. The selected emotions are: Displeasure, Neutral, Pleasure, Calm, Medium, and Excited measured in the Valence-Arousal model.…”

Section: Background and Related Workmentioning

confidence: 99%

A user-centered approach for detecting emotions with low-cost sensors

Francese

Risi

Tortora

2020

Multimed Tools Appl

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Detecting emotions is very useful in many fields, from health-care to human-computer interaction. In this paper, we propose an iterative user-centered methodology for supporting the development of an emotion detection system based on low-cost sensors. Artificial Intelligence techniques have been adopted for emotion classification. Different kind of Machine Learning classifiers have been experimentally trained on the users’ biometrics data, such as hearth rate, movement and audio. The system has been developed in two iterations and, at the end of each of them, the performance of classifiers (MLP, CNN, LSTM, Bidirectional-LSTM and Decision Tree) has been compared. After the experiment, the SAM questionnaire is proposed to evaluate the user’s affective state when using the system. In the first experiment we gathered data from 47 participants, in the second one an improved version of the system has been trained and validated by 107 people. The emotional analysis conducted at the end of each iteration suggests that reducing the device invasiveness may affect the user perceptions and also improve the classification performance.

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“…The manuscript lacks demographic details about the subjects as well as the ratio of training to testing datasets. In the study [19], five intrusive sensors are used to classify emotions. The research includes 23 subjects ranging in age from 20 to 27 years.…”

Section: Discussion and Analysismentioning

confidence: 99%

“…The physiological signal interpretation framework (Emo-CSI) was presented for emotion classification using physiological signals [19]. The Emo-CSI framework uses heart rate, respiration pattern, skin humidity, and conductivity to classify emotions into displeasure, neutral, pleasure, calm, and excited emotions.…”

Section: Literature Reviewmentioning

confidence: 99%

A Review on Physiological Signal Based Emotion Detection

Shahzad¹,

Saleem²,

Ahmed³

et al. 2021

AETiC

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Emotions are feelings that are the result of biochemical processes in the body that are influenced by a variety of factors such as one's state of mind, situations, experiences, and surrounding environment. Emotions have an impact on one's ability to think and act. People interact with each other to share their thoughts and feelings. Emotions play a vital role in the field of medicine and can also strengthen the human computer interaction. There are different techniques being used to detect emotions based on facial features, texts, speech, and physiological signals. One of the physiological signal breathing is a parameter which represents an emotion. The rational belief that different breathing habits are correlated with different emotions has expanded the evidence for a connection between breathing and emotion. In this manuscript different recent investigations about the emotion recognition using respiration patterns have been reviewed. The aim of the survey is to sum up the latest technologies and techniques to help researchers develop a global solution for emotional detection system. Various researchers use benchmark datasets and few of them created their own dataset for emotion recognition. It is observed that many investigators used invasive sensors to acquire respiration signals that makes subject uncomfortable and conscious that affects the results. The numbers of subjects involved in the studies reviewed are of the same age and race which is the reason why the results obtained in those studies cannot be applied to diverse population. There is no single global solution exist.

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The emotional state classification using physiological signal interpretation framework

Cited by 3 publications

References 23 publications

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

Respiration Based Non-Invasive Approach for Emotion Recognition Using Impulse Radio Ultra Wide Band Radar and Machine Learning

A user-centered approach for detecting emotions with low-cost sensors

A Review on Physiological Signal Based Emotion Detection

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