Human emotion classification based on respiration signal

Hameed, Rabab Alaa; Sabir, Mohannad K.; Fadhel, Mohammed A.; Al-Shamma, Omran; Alzubaidi, Laith

doi:10.1145/3321289.3321315

Cited by 18 publications

(15 citation statements)

References 17 publications

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“…The intrusiveness of sensors makes participants feel uneasy during the emotion elicitation process. The research classifies six basic emotions and one neutral emotion [20]. To calculate the air flow rate and respiration rate, a Biopac sensor with a mouthpiece and an air flow sensor is used.…”

Section: Discussion and Analysismentioning

confidence: 99%

“…In the study [20] human emotions were classified using respiration signals. The study [20] has two objectives in which the first objective was to analyze the effect of the created emotions on breathing signals and the second objective was to calculate the emotions in two different ways that were Fast Fourier Transform (FFT) and neural networks (NN). In this study, investigators created their own dataset in al-Khwarizmi College of Engineering University of Baghdad's experimental lab.…”

Section: Literature Reviewmentioning

confidence: 99%

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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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Section: Discussion and Analysismentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A Review on Physiological Signal Based Emotion Detection

Shahzad¹,

Saleem²,

Ahmed³

et al. 2021

AETiC

View full text Add to dashboard Cite

show abstract

“…Various emotions e.g., anger, excitement, anxiety, happiness, etc. can be determined using such sensors [ 69 ].…”

Section: Sensors For Human Emotion Recognitionmentioning

confidence: 99%

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Pal

Mukhopadhyay

Suryadevara

2021

Sensors

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With the advancement of human-computer interaction, robotics, and especially humanoid robots, there is an increasing trend for human-to-human communications over online platforms (e.g., zoom). This has become more significant in recent years due to the Covid-19 pandemic situation. The increased use of online platforms for communication signifies the need to build efficient and more interactive human emotion recognition systems. In a human emotion recognition system, the physiological signals of human beings are collected, analyzed, and processed with the help of dedicated learning techniques and algorithms. With the proliferation of emerging technologies, e.g., the Internet of Things (IoT), future Internet, and artificial intelligence, there is a high demand for building scalable, robust, efficient, and trustworthy human recognition systems. In this paper, we present the development and progress in sensors and technologies to detect human emotions. We review the state-of-the-art sensors used for human emotion recognition and different types of activity monitoring. We present the design challenges and provide practical references of such human emotion recognition systems in the real world. Finally, we discuss the current trends in applications and explore the future research directions to address issues, e.g., scalability, security, trust, privacy, transparency, and decentralization.

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“…Recent research shows how a subject’s respiration signal can be used for drowsiness detection while driving a car [ 10 , 11 ]. Also emotion classification from respiration and other physiological features has been a focus in some studies [ 12 , 13 ]. In [ 13 ], an accuracy of emotion classification of 75 to 90% from breathing alone is reported, depending on the chosen feature within the respiration signal.…”

Section: Related Workmentioning

confidence: 99%

Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition

Kempfle

Laerhoven

2020

Sensors

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Depth imaging has, through recent technological advances, become ubiquitous as products become smaller, more affordable, and more precise. Depth cameras have also emerged as a promising modality for activity recognition as they allow detection of users’ body joints and postures. Increased resolutions have now enabled a novel use of depth cameras that facilitate more fine-grained activity descriptors: The remote detection of a person’s breathing by picking up the small distance changes from the user’s chest over time. We propose in this work a novel method to model chest elevation to robustly monitor a user’s respiration, whenever users are sitting or standing, and facing the camera. The method is robust to users occasionally blocking their torso region and is able to provide meaningful breathing features to allow classification in activity recognition tasks. We illustrate that with this method, with specific activities such as paced-breathing meditating, performing breathing exercises, or post-exercise recovery, our model delivers a breathing accuracy that matches that of a commercial respiration chest monitor belt. Results show that the breathing rate can be detected with our method at an accuracy of 92 to 97% from a distance of two metres, outperforming state-of-the-art depth imagining methods especially for non-sedentary persons, and allowing separation of activities in respiration-derived features space.

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Human emotion classification based on respiration signal

Cited by 18 publications

References 17 publications

A Review on Physiological Signal Based Emotion Detection

A Review on Physiological Signal Based Emotion Detection

Development and Progress in Sensors and Technologies for Human Emotion Recognition

Towards Breathing as a Sensing Modality in Depth-Based Activity Recognition

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