Providing Appropriate Social Support to Prevention of Depression for Highly Anxious Sufferers

Hao, Fei; Pang, Guangyao; Wu, Yulei; Pi, Zhongling; Xia, Lirong; Min, Geyong

doi:10.1109/tcss.2019.2894144

Cited by 24 publications

(7 citation statements)

References 26 publications

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“…It means LST M can predict the next instants of depression better than others. Hao et al (2019) [150] T his paper used the data of 1425 studies of questionnaires from different places. T hey applied different scales on the collected data.…”

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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“…This enables depression detection at early stages. In [21], authors adopt clustering techniques to quantify the anxiety and depression indices on questionnaire textual data. Further, the correlation amongst anxiety, depression and social data is investigated.For most of social media data, the text analysis is done for short text and these classifiers dont work well in a conversation setting which happens during the counselling/screening sessions.…”

Section: Depression Quotient Detection In Textmentioning

confidence: 99%

Multi-level Attention Network using Text, Audio and Video for Depression Prediction

Ray

Kumar

Reddy

et al. 2019

Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop

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Depression has been the leading cause of mental-health illness worldwide. Major depressive disorder (MDD), is a common mental health disorder that affects both psychologically as well as physically which could lead to loss of lives. Due to the lack of diagnostic tests and subjectivity involved in detecting depression, there is a growing interest in using behavioural cues to automate depression diagnosis and stage prediction. The absence of labelled behavioural datasets for such problems and the huge amount of variations possible in behaviour makes the problem more challenging. This paper presents a novel multi-level attention based network for multimodal depression prediction that fuses features from audio, video and text modalities while learning the intra and inter modality relevance. The multi-level attention reinforces overall learning by selecting the most influential features within each modality for the decision making. We perform exhaustive experimentation to create different regression models for audio, video and text modalities. Several fusions models with different configurations are constructed to understand the impact of each feature and modality. We outperform the current baseline by 17.52% in terms of root mean squared error.

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“…To avoid distortion, other studies employed fixed-size histogram or other statistics to summarize the distribution of representations. Specifically, they generate video-level descriptors by computing statistics of features [31], [32], [33], [34], using Gaussian Mixture Model (GMM) [35], [36], [37], [38], [39], [40] or fisher vector [38], [41], etc. Although these methods summarize undistorted information, temporal relations between segments/frames, such as the order of events, are lost after creating the statistics.…”

Section: 21)mentioning

confidence: 99%

Spectral Representation of Behaviour Primitives for Depression Analysis

Song

Jaiswal

Shen

et al. 2022

IEEE Trans. Affective Comput.

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Depression is a serious mental disorder affecting millions of people all over the world. Traditional clinical diagnosis methods are subjective, complicated and require extensive participation of clinicians. Recent advances in automatic depression analysis systems promise a future where these shortcomings are addressed by objective, repeatable, and readily available diagnostic tools to aid health professionals in their work. Yet there remain a number of barriers to the development of such tools. One barrier is that existing automatic depression analysis algorithms base their predictions on very brief sequential segments, sometimes as little as one frame. Another barrier is that existing methods do not take into account what the context of the measured behaviour is. In this paper, we extract multi-scale video-level features for video-based automatic depression analysis. We propose to use automatically detected human behaviour primitives as the low-dimensional descriptor for each frame. We also propose two novel spectral representations, i.e. spectral heatmaps and spectral vectors, to represent video-level multi-scale temporal dynamics of expressive behaviour. Constructed spectral representations are fed to Convolution Neural Networks (CNNs) and Artificial Neural Networks (ANNs) for depression analysis. We conducted experiments on the AVEC 2013 and AVEC 2014 benchmark datasets to investigate the influence of interview tasks on depression analysis. In addition to achieving state of the art accuracy in severity of depression estimation, we show that the task conducted by the user matters, that fusion of a combination of tasks reaches highest accuracy, and that longer tasks are more informative than shorter tasks, up to a point.

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Providing Appropriate Social Support to Prevention of Depression for Highly Anxious Sufferers

Cited by 24 publications

References 26 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

Multi-level Attention Network using Text, Audio and Video for Depression Prediction

Spectral Representation of Behaviour Primitives for Depression Analysis

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