Real Time Stress Detection Using Deep Learning with Facial Expressions and Vocal Signals

Embodiment is very essential in the expression and management of emotions at work, especially since the physical expressions of stress, such as facial expressions and voice changes, are crucial indicators of the emotional status of workers.The understanding of these embodied expressions enhances the emotional intelligence of teams because of better communication and effective conflict resolution.This paper examines how machine learning and deep learning have approached the problem of stress detection through facial expression and vocal attributes with their dependence on convolutional neural networks for efficient identification of minor indicators, which might include furrowed eyebrows and the presence of elements in a voice like pitch and tone.Datasets like FER2013 and RAVDESS make data augmentation techniques integrate model robustness, and a more precise time analysis through neural networks improves the real-time capability of the model.Also, a multimodal fusion of facial and vocal features further helps to enhance Embodiment is very essential in the expression and management of emotions at work, especially since the physical expressions of stress, such as facial expressions and voice changes, are crucial indicators of the emotional status of workers.The understanding of these embodied expressions enhances the emotional intelligence of teams because of better communication and effective conflict resolution.This paper examines how machine learning and deep learning have approached the problem of stress detection through facial expression and vocal attributes with their dependence on convolutional neural networks for efficient identification of minor indicators, which might include furrowed eyebrows and the presence of elements in a voice like pitch and tone.Datasets like FER2013 and RAVDESS make data augmentation techniques integrate model robustness, and a more precise time analysis through neural networks Also, a multimodal fusion of facial and vocal features further helps to enhance overall accuracy in relation to the level of perception about stress levels.It would help organizations be proactive about addressing emotional health and therefore lead to a healthier workplace environment and the well-being of employees.Overall accuracy in relation to the level of perception about stress levels.It would help organizations be proactive about addressing emotional health and therefore lead to a healthier workplace environment and the well-being of employees.

Authors

Publication Details

Journal
International Research Journal of Education and Technology (IRJET).
Published
2024-11-29
DOI
https://doi.org/10.70127/irjedt.vol.8.issue04.1188
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
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article

Real Time Stress Detection Using Deep Learning with Facial Expressions and Vocal Signals

Subha Indu S
International Research Journal of Education and Technology (IRJET).
Emotion and Mood Recognition
article

Real Time Stress Detection Using Deep Learning with Facial Expressions and Vocal Signals

Subha Indu S
article en

Abstract

Embodiment is very essential in the expression and management of emotions at work, especially since the physical expressions of stress, such as facial expressions and voice changes, are crucial indicators of the emotional status of workers.The understanding of these embodied expressions enhances the emotional intelligence of teams because of better communication and effective conflict resolution.This paper examines how machine learning and deep learning have approached the problem of stress detection through facial expression and vocal attributes with their dependence on convolutional neural networks for efficient identification of minor indicators, which might include furrowed eyebrows and the presence of elements in a voice like pitch and tone.Datasets like FER2013 and RAVDESS make data augmentation techniques integrate model robustness, and a more precise time analysis through neural networks improves the real-time capability of the model.Also, a multimodal fusion of facial and vocal features further helps to enhance Embodiment is very essential in the expression and management of emotions at work, especially since the physical expressions of stress, such as facial expressions and voice changes, are crucial indicators of the emotional status of workers.The understanding of these embodied expressions enhances the emotional intelligence of teams because of better communication and effective conflict resolution.This paper examines how machine learning and deep learning have approached the problem of stress detection through facial expression and vocal attributes with their dependence on convolutional neural networks for efficient identification of minor indicators, which might include furrowed eyebrows and the presence of elements in a voice like pitch and tone.Datasets like FER2013 and RAVDESS make data augmentation techniques integrate model robustness, and a more precise time analysis through neural networks Also, a multimodal fusion of facial and vocal features further helps to enhance overall accuracy in relation to the level of perception about stress levels.It would help organizations be proactive about addressing emotional health and therefore lead to a healthier workplace environment and the well-being of employees.Overall accuracy in relation to the level of perception about stress levels.It would help organizations be proactive about addressing emotional health and therefore lead to a healthier workplace environment and the well-being of employees.

International Research Journal of Education and Technology (IRJET).Vol. 6(11)
Openalex Percentile: Top 66%
Emotion and Mood Recognition
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