Facial Landmark Activity Features for Depression Screening

Sumali, Brian; Mitsukura, Yasue; Tazawa, Yuuki; Kishimoto, Toshifumi

doi:10.23919/sice.2019.8859798

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…SVM gave the accuracy of 80% and F1 score of 0.80. Sumali et al (2019) [144] T his paper used the features of landmarks, movement of eye pupils, and feature near to mouth area. T hey used the data obtaining from the clinical interview.…”

Section: Multi-modalmentioning

confidence: 99%

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

Chahar¹,

Dubey²,

Narang³

2021

IJATEE

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Section: Multi-modalmentioning

confidence: 99%

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

Chahar¹,

Dubey²,

Narang³

2021

IJATEE

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“…Sumali et al 185 Significant differences were observed in facial landmark features (e.g., average right nose (speed), median left ear top (speed), and left pupil-right pupil positions) between healthy and depressive volunteers.…”

Section: Author Main Findingsmentioning

confidence: 98%

A systematic review on automated clinical depression diagnosis

Mao,

Wu,

Chen

2023

npj Mental Health Res

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Assessing mental health disorders and determining treatment can be difficult for a number of reasons, including access to healthcare providers. Assessments and treatments may not be continuous and can be limited by the unpredictable nature of psychiatric symptoms. Machine-learning models using data collected in a clinical setting can improve diagnosis and treatment. Studies have used speech, text, and facial expression analysis to identify depression. Still, more research is needed to address challenges such as the need for multimodality machine-learning models for clinical use. We conducted a review of studies from the past decade that utilized speech, text, and facial expression analysis to detect depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline. We provide information on the number of participants, techniques used to assess clinical outcomes, speech-eliciting tasks, machine-learning algorithms, metrics, and other important discoveries for each study. A total of 544 studies were examined, 264 of which satisfied the inclusion criteria. A database has been created containing the query results and a summary of how different features are used to detect depression. While machine learning shows its potential to enhance mental health disorder evaluations, some obstacles must be overcome, especially the requirement for more transparent machine-learning models for clinical purposes. Considering the variety of datasets, feature extraction techniques, and metrics used in this field, guidelines have been provided to collect data and train machine-learning models to guarantee reproducibility and generalizability across different contexts.

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“…Sumali et al [16] used the recording of interviews between depressed and healthy volunteers with the clinician. These recordings were provided as input to detect 40 landmark coordinates.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing Depression Prediction Accuracy Using Filter and Wrapper-Based Visual Feature Extraction

Mamidisetti,

Reddy

2023

JAIT

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The pressing need for Artificial Intelligence (AI) applications in healthcare is evident, particularly in the context of depression prediction. Literature underscores the significance of visual cues as crucial indicators of depression. Primary objective of this work is to design a complete machine learning pipeline for more accurate depression prediction, which includes several stages like: data collection stage, feature extraction stage, feature selection stage, classification stage, and performance evaluation stage. Data collection involved video recording of participants (n = 219) while conducting emotion elicitation (triggering emotions by showing photos/videos) to depressed and nondepressed subjects. Then, numerous visual features like geometrical features and facial action unit features were extracted. Filter and Wrapper Feature Selection (FS)methods were used to extract the optimal feature set from high-dimensional visual features. In the Filter method, experiments are conducted using three strategies: quasiconstant strategy, mutual information gain, and linear discriminant analysis. In the wrapper method, experiments are conducted using three strategies: forward selection, backward elimination, and recursive feature elimination. Accuracy for the classification of non-depressed or depressed subjects was used as the performance metric. Obtained results with an accuracy of 85.6% show that the backward elimination approach (even though only ten features were selected) outperformed other experiments conducted in current work and also with the state-of-the-art methods. In addition to this, our method is also applied to publicly available benchmarking dataset to show its effectiveness on diverse dataset. These findings demonstrate the applicability of visual features using filter and wrapper feature selection method is reliable in depression prediction.Hence implications extend to a potential application in mental health assessment.

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Facial Landmark Activity Features for Depression Screening

Cited by 6 publications

References 14 publications

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

A review and meta-analysis of machine intelligence approaches for mental health issues and depression detection

A systematic review on automated clinical depression diagnosis

Enhancing Depression Prediction Accuracy Using Filter and Wrapper-Based Visual Feature Extraction

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