Advances in Emotion Recognition: Link to Depressive Disorder

Cheng, Xiaotong; Wang, Xiaoxia; Ouyang, Tan-Te; Feng, Zhichun

doi:10.5772/intechopen.92019

Cited by 10 publications

(4 citation statements)

References 60 publications

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“…To determine the of deep emotions related to deep emotions, a web camera will be used to record footage of students with frontal faces as part of the programming process. Based on the level of depressive features that can be detected in these faces, machine learning will be used to categorize the pupil as depressed or non-depressed [28]. Our study topic by analyzing stress patterns in working employees by providing them with appropriate stress management treatments and surveys on a regular basis to determine their regularly using machine learning algorithms such as KNN classifiers to classify stress levels, we are able to identify stress patterns that significantly influence stress levels [54].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Emotions Recognition from Facial Expressions using Deep Learning

Shahzadi,

Fuzail,

Aslam

2023

VFAST trans. softw. eng.

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Deep emotion recognition has a wide range of applications, including human-robot communication, business, movies, services hotels, and even politics. Despite the use of various supervised and unsupervised methods in many different fields, there is still a lack of accurate analysis. Therefore, we have taken on this challenge as our research problem. We have proposed a mechanism for efficient and fine-grained classification of human deep emotions that can be applied to many other problems in daily life. This study aims to explore the best-suited algorithm along with optimal parameters to provide a solution for an efficient emotion detection machine learning system. In this study, we aimed to recognize emotions from facial expressions using deep learning techniques and the JAFFE dataset. The performance of three different models, a CNN (Convolutional Neural Network), an ANN (Artificial Neural Network), and an SVM (Support Vector Machine) were evaluated using precision, recall, F1-score, and accuracy as the evaluation metrics. The results of the experiments show that all three models performed well in recognizing emotions from facial expressions. The CNN model achieved a precision of 0.653, recall of 0.561, F1-score of 0.567, and accuracy of 0.62. The ANN model achieved a precision of 0.623, recall of 0.542, F1-score of 0.542, and accuracy of 0.59. The SVM model achieved a precision of 0.643, recall of 0.559, F1-score of 0.545, and accuracy of 0.6. Overall, the results of the study indicate that deep learning techniques can be effectively used for recognizing emotions from facial expressions using the JAFFE dataset.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Emotions Recognition from Facial Expressions using Deep Learning

Shahzadi,

Fuzail,

Aslam

2023

VFAST trans. softw. eng.

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“…Computer vision techniques use facial expression recognition and facial landmark recognition to mark the region of interest and compute the mental health status whereas, human labeling involves crowdsourcing techniques (like PyBossa) for evaluating the images on the grounds of facial expression, context, etc. [13,14].…”

Section: Facial Logging For Mental Health Analysis -Mentalmentioning

confidence: 99%

Mental Health Evaluation and Assistance for Visually Impaired People

Pandey

Pandey²

2023

EAI Endorsed Scal Inf Syst

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Earlier, three-person out of every 10 persons had been suffering from mental health issues but nowadays this situation became worsened due to the pandemic. The number of individuals suffering from mental health has been increasing day by day. One of the WHO reports states that 3.8% of the population has been affected due to depression. Although such an important issue in human health, still it is one of the neglected health sectors around the world. This situation is more worsened among special need people as they feel more anxiety disorders and loneliness in comparison to their sighted peers. Technology is helping us to solve several such problems which are difficult to discuss with peers and worsen over time if not taken preventively on time. In this article, a technological solution using facial expression and voice-based assistance has been used. This would especially help in monitoring the mood of visually challenged people. Their mental health can be monitored and assistance would be provided as per the severity of the situation. Mental health is analyzed based on emotion recognition from facial expressions and psychometric evaluations. A health score would be calculated which is used to judge the severity of mental health. An assessment report would also be generated and sent to the guardian of the user. The whole application is designed with a voice assistance platform using Dart (Flutter) and firebase as storage for all interactions with the visually challenged person.

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“…Menial, monotonous and dangerous jobs are easily assisted by robots. In present [8] scenario of covid 19, robots has served as a shield between the patients and healthcare staffs. Surgical robots carry out operations more precisely.…”

Section: Roboticmentioning

confidence: 99%

Artificial Intelligence and Its Impact on Healthcare

Singh¹

2021

OAJCAM

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Artificial Intelligence (AI) is globally emerging as a logistic tool in digital technology and depicts its wide range of relevance in preventive medicine and its management. Smart Hospitals or Hospital 4.0 is a basic need for today's scenario seeking to COVID 19.[1] Coronavirus has become epidemic and claim for imperative medical supplies, medicines along with the synthesis of neutralizing antibodies which can block the virus particles. Industrial Internet of things (IIoT) known as the fourth industrial revolution served to be at rest during COVID-19 crunch. IIoT has assassinated the demands of individualized face conceal, mitten and enabled to conduct all relevant research and assisted to provide the data immediately for the treatment of COVID-19 patients. Effective upturn off wearables and sensors (like Apple Watch, MI Band) has escort a new generation of telemedicine, where patients could be cared virtually by hospital staff. The employment of these technologies would help people to get [2] enlighten regarding their wellbeing. These smart manufacturing technologies could provide a lot of resourceful brainstorm band-aid to scuffle geographical and global medical emergencies.The aim of chapter is to through light on the smart system of Industry 4.0 during this pandemic of COVID 19 by providing better digital techniques without imposing the risks to [3] healthcare. This paper overall deals with how AI and Industry 4.0 co-jointly can change the whole scenario of our medical procedures as well as healthcare systems and in addition to that many technologies which are currently helping in the medical field are also being discussed.

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Advances in Emotion Recognition: Link to Depressive Disorder

Cited by 10 publications

References 60 publications

Deep Emotions Recognition from Facial Expressions using Deep Learning

Deep Emotions Recognition from Facial Expressions using Deep Learning

Mental Health Evaluation and Assistance for Visually Impaired People

Artificial Intelligence and Its Impact on Healthcare

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