Intelligent depression detection and support system: Statistical analysis, psychological review and design implication

Tasnim, Mashrura; Shahriyar, Rifat; Nahar, Nowshin; Mahmud, Hossain

doi:10.1109/healthcom.2016.7749494

Cited by 16 publications

(3 citation statements)

References 7 publications

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“…Expecting the students' sentiments are wanted to the activities done in homeroom, their excited state should be visible in the event that they are deterred or not, and considering this the teacher can help the student by zeroing in more earnestly on that particular student. [11] In the event that different appearances in a comparable scene show a comparable positive or negative assessment, it would help with figuring out the whole situation of the scene, whether or not subjects in the scene are lively or whether something wrong is happening in the scene [12].…”

Section: Literature Surveymentioning

confidence: 99%

AI Based Mental Health Prediction System

Desai¹,

Nagapurkar²,

Gajbhiye³

et al. 2023

IJRASET

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The most common mood disorder in the world, depression has a considerable negative influence on health and functionality as well as profound personal, familial, and society implications. The correct and timely identification of depressionrelated symptoms may have numerous advantages for both doctors and those who are affected. The current work aimed to develop and clinically test a system capable of identifying visual signs of melancholy and supporting physician decisions. Programmable suffering assessment based on visible signals is a rapidly expanding research area. Picture handling and AI computations are the focus of the current thorough evaluation of existing approaches as described in more than sixty distributions during the last 10 years. The current datasets, various information-gathering methods, and visual cues of misery are compiled. The survey depicts estimates for visual element extraction, dimensionality reduction, layout and relapse choosing options, as well as numerous combination techniques. Incorporating a quantitative meta-analysis of announced results based on execution metrics risk-tolerant, it identifies general trends and significant irksome issues to be taken into account in ongoing investigations of programmed sadness appraisal using visible signs either alone or in combination with obvious signals. Additionally, the proposed work used deep learning to predict the level of the downturn as shown by the contribution of current face photographs.

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Section: Literature Surveymentioning

confidence: 99%

AI Based Mental Health Prediction System

Desai¹,

Nagapurkar²,

Gajbhiye³

et al. 2023

IJRASET

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show abstract

“…The suggested model can increase detection review over 6-9 percent in F1-score, according to experimental data. By delving deeper into the data on social interactions, the number of social network system with poor connections is over 14 percent greater among stressed users than among non-stressed users, showing that stressed peoples friends are less linked and difficult than non-stressed people's friends [11].…”

Section: B System Frameworkmentioning

confidence: 99%

Depression Scale Recognition over Fusion of Visual and Vocal Expression using Artificial Intellectual Method

Shinde¹,

Kulkarni²

2021

IJRTE

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Now a day’s supported visual and audio cues for automatic depression assessment may be a fast emerging research subject. This comprehensive evaluation of existing methodologies focuses on machine learning (ML) algorithm and image processing (IP) algorithm, as documented in over sixty articles over the last ten years. There is a visual indicator of depression, several data collection procedures are used, and finally examined the previous year or existing datasets. In this article describes techniques and algorithms as well as methods for dimensionality reduction, visual feature extraction, regression approaches, and classification decision procedures, and also various fusion tactics. A significant meta-analysis of published data is given, based on performance indicators that are robust to chance, to identify general trends and important pressing concerns for further research using visual and verbal cues alone or in combination with signals for automated depression evaluation The suggested work also used deep learning and natural language processing to estimate depression levels based on current video data.

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“…In [6] patients were asked to wear devices to observe their heart-rate, sleep pattern, their reduction in social interaction, their GPS location to check if they are skipping work etc. for depression analysis.…”

Section: Introductionmentioning

confidence: 99%

A computer vision based image processing system for depression detection among students for counseling

Namboodiri

Venkataraman

2019

IJEECS

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Psychological problems in college students like depression, pessimism, eccentricity, anxiety etc. are caused principally due to the neglect of continuous monitoring of students’ psychological well-being. Identification of depression at college level is desirable so that it can be controlled by giving better counseling at the starting stage itself. The disturbed mental state of a student suffering from depression would be clearly evident in the student’s facial expressions.Identification of depression in large group of college students becomes a tedious task for an individual. But advances in the Image-Processing field have led to the development of effective systems, which prove capable of detecting emotions from facial images, in a much simpler way. Thus, we need an automated system that captures facial images of students and analyze them, for effective detection of depression. In the proposed system, an attempt is being made to make use of the Image processing techniques, to study the frontal face features of college students and predict depression. This automated system will be trained with facial features of positive and negative facial emotions. To predict depression, a video of the student is captured, from which the face of the student is extracted. Then using Gabor filters, the facial features are extracted. Classification of these facial features is done using SVM classifier. The level of depression is identified by calculating the amount of negative emotions present in the entire video. Based on the level of depression, notification is send to the class advisor, department counselor or university counselor, indicating the student’s disturbed mental state. The present system works with an accuracy of 64.38%. The paper concludes with the description of an extended architecture for depression detection as future work.

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Intelligent depression detection and support system: Statistical analysis, psychological review and design implication

Cited by 16 publications

References 7 publications

AI Based Mental Health Prediction System

AI Based Mental Health Prediction System

Depression Scale Recognition over Fusion of Visual and Vocal Expression using Artificial Intellectual Method

A computer vision based image processing system for depression detection among students for counseling

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