Analysis of Depression Based on Facial Cues on A Captured Motion Picture

Dadiz, Bryan G.; Marcos, Nelson

doi:10.1109/siprocess.2018.8600523

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Nevertheless, high accuracy and interrater agreement were obtained from the models in both machine learning phases. For comparison, studies [ 24 , 25 , 28 , 29 ], and [ 23 ] have 87.2%, 81%, 81.23%, 89.71% and 73% as accuracy for predicting depression, respectively. [ 31 ] reports 73.6% accuracy for predicting dementia and [ 30 ] reports 99.9% TNR and 78.8% TPR.…”

Section: Discussionmentioning

confidence: 99%

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Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

Sumali

Mitsukura

Liang

et al. 2020

Sensors

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Loss of cognitive ability is commonly associated with dementia, a broad category of progressive brain diseases. However, major depressive disorder may also cause temporary deterioration of one’s cognition known as pseudodementia. Differentiating a true dementia and pseudodementia is still difficult even for an experienced clinician and extensive and careful examinations must be performed. Although mental disorders such as depression and dementia have been studied, there is still no solution for shorter and undemanding pseudodementia screening. This study inspects the distribution and statistical characteristics from both dementia patient and depression patient, and compared them. It is found that some acoustic features were shared in both dementia and depression, albeit their correlation was reversed. Statistical significance was also found when comparing the features. Additionally, the possibility of utilizing machine learning for automatic pseudodementia screening was explored. The machine learning part includes feature selection using LASSO algorithm and support vector machine (SVM) with linear kernel as the predictive model with age-matched symptomatic depression patient and dementia patient as the database. High accuracy, sensitivity, and specificity was obtained in both training session and testing session. The resulting model was also tested against other datasets that were not included and still performs considerably well. These results imply that dementia and depression might be both detected and differentiated based on acoustic features alone. Automated screening is also possible based on the high accuracy of machine learning results.

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Section: Discussionmentioning

confidence: 99%

“…Features commonly employed for automated mental health screening include facial features (gaze, blink, emotion detection, etc.) [ 24 , 25 , 26 ], biosignals (electroencephalogram, heart rate, respiration, etc.) [ 27 , 28 , 29 , 30 ], and auditory features (intensity, tone, speed of speech, etc.)…”

Section: Introductionmentioning

confidence: 99%

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

Sumali

Mitsukura

Liang

et al. 2020

Sensors

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“…Dadiz et al (2019) presented the depression detection classification model based on local‐binary‐pattern (LBP) texture characteristics 59,60 . A video recording from the SEMAINE database 61 was included in the analysis.…”

Section: Classification Of Depression Detection Techniquesmentioning

confidence: 99%

“…The system obtained an accuracy of 94.8% for identifying individuals with high scores on a depressive symptomatology self-report scale. presented the depression detection classification model based on local-binary-pattern (LBP) texture characteristics 59,60. A video recording from the SEMAINE database61 was included in the analysis.…”

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confidence: 99%

Review of automated depression detection: Social posts, audio and video, open challenges and future direction

Yadav

Sharma

Patil

2022

Concurrency and Computation

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Depression is the primary cause of illness and injury in the country, with over 280 million people suffering from it as per the 2021 survey. Depression is one of the most common psychiatric illnesses in the world, affecting millions of people. Early detection of major depression symptoms and treatment with timely intervention can help to prevent the emergence of major depression. This has generated the need for some novel techniques to be utilized for depression detection to help doctors in diagnosing and treating depression effectively. Depression can be investigated through online posts, audio files, facial expressions, as well as through video files. In this regard, the research presents a detailed survey of the existing machine learning methods in depression detection, along with the different datasets available. The research explores automated depression detection strategies and the various ways to detect depression from text, audio, and video, and even examines the various systems and procedures for detecting depression using various criteria. More than 140 related research articles were considered for this review, with 80 of them being processed for comparative analysis based on various key performance indicators. The classifications of various methods used to detect stress, anxiety, and depression are highlighted followed by open research challenges and issues while analyzing depression techniques.

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“…Beyond manually assessing footage, automatic video analysis from the field of computer vision and machine learning has been used. Many studies in this category attempt to identify depression from facial cues [15][16][17][18]. For example, in one study [17] participants were shown a sad video, a neutral video, and a text to read and were interviewed while their facial expressions were analyzed using a Microsoft Kinect camera.…”

Section: Understanding Suicide Risk Factorsmentioning

confidence: 99%

The Use of Closed-Circuit Television and Video in Suicide Prevention: Narrative Review and Future Directions

Onie¹,

Li²,

Liang³

et al. 2021

JMIR Ment Health

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Background Suicide is a recognized public health issue, with approximately 800,000 people dying by suicide each year. Among the different technologies used in suicide research, closed-circuit television (CCTV) and video have been used for a wide array of applications, including assessing crisis behaviors at metro stations, and using computer vision to identify a suicide attempt in progress. However, there has been no review of suicide research and interventions using CCTV and video. Objective The objective of this study was to review the literature to understand how CCTV and video data have been used in understanding and preventing suicide. Furthermore, to more fully capture progress in the field, we report on an ongoing study to respond to an identified gap in the narrative review, by using a computer vision–based system to identify behaviors prior to a suicide attempt. Methods We conducted a search using the keywords “suicide,” “cctv,” and “video” on PubMed, Inspec, and Web of Science. We included any studies which used CCTV or video footage to understand or prevent suicide. If a study fell into our area of interest, we included it regardless of the quality as our goal was to understand the scope of how CCTV and video had been used rather than quantify any specific effect size, but we noted the shortcomings in their design and analyses when discussing the studies. Results The review found that CCTV and video have primarily been used in 3 ways: (1) to identify risk factors for suicide (eg, inferring depression from facial expressions), (2) understanding suicide after an attempt (eg, forensic applications), and (3) as part of an intervention (eg, using computer vision and automated systems to identify if a suicide attempt is in progress). Furthermore, work in progress demonstrates how we can identify behaviors prior to an attempt at a hotspot, an important gap identified by papers in the literature. Conclusions Thus far, CCTV and video have been used in a wide array of applications, most notably in designing automated detection systems, with the field heading toward an automated detection system for early intervention. Despite many challenges, we show promising progress in developing an automated detection system for preattempt behaviors, which may allow for early intervention.

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Analysis of Depression Based on Facial Cues on A Captured Motion Picture

Cited by 6 publications

References 23 publications

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

Speech Quality Feature Analysis for Classification of Depression and Dementia Patients

Review of automated depression detection: Social posts, audio and video, open challenges and future direction

The Use of Closed-Circuit Television and Video in Suicide Prevention: Narrative Review and Future Directions

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