Identifying Psychological Symptoms Based on Facial Movements

Wang, Xiaoyang; Wang, Yilin; Zhou, Mingjie; Li, Baobin; Liu, Xiaoqian; Zhu, Tingshao

doi:10.3389/fpsyt.2020.607890

Cited by 15 publications

(17 citation statements)

References 54 publications

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“…Furthermore, the mechanisms associated with complex neural networks that regulate and control the gestural expression of complex emotions have not been fully investigated ( 37 ). In future research, it may be possible to extract the exact site of visualized individual emotional expression by decomposing features of the facial coding system ( 3 ) or body skeleton.…”

Section: Discussionmentioning

confidence: 99%

“…In the field of psychology, an individual's internal emotional experience can be inferred based on the individual's external facial expressions, gesture expressions, and intonation expressions ( 2 ). With the concept of affective computing proposed in 1997, the characteristic physiological and behavioral signals caused by human emotions are obtained through various sensors to establish an “emotional model” ( 3 ), so as to accurately identify human emotions ( 4 ) and eliminate uncertainties and ambiguities. Therefore, the computation of emotions and feelings is no longer limited to traditional methods such as autonomic nervous system measurements, startle response measurements, and brain measurements.…”

Section: Introductionmentioning

confidence: 99%

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Mental states and personality based on real-time physical activity and facial expression recognition

Huang

Zhai

Song³

et al. 2023

Front. Psychiatry

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IntroductionTo explore a quick and non-invasive way to measure individual psychological states, this study developed interview-based scales, and multi-modal information was collected from 172 participants.MethodsWe developed the Interview Psychological Symptom Inventory (IPSI) which eventually retained 53 items with nine main factors. All of them performed well in terms of reliability and validity. We used optimized convolutional neural networks and original detection algorithms for the recognition of individual facial expressions and physical activity based on Russell's circumplex model and the five factor model.ResultsWe found that there was a significant correlation between the developed scale and the participants' scores on each factor in the Symptom Checklist-90 (SCL-90) and Big Five Inventory (BFI-2) [r = (−0.257, 0.632), p < 0.01]. Among the multi-modal data, the arousal of facial expressions was significantly correlated with the interval of validity (p < 0.01), valence was significantly correlated with IPSI and SCL-90, and physical activity was significantly correlated with gender, age, and factors of the scales.DiscussionOur research demonstrates that mental health can be monitored and assessed remotely by collecting and analyzing multimodal data from individuals captured by digital tools.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mental states and personality based on real-time physical activity and facial expression recognition

Huang

Zhai

Song³

et al. 2023

Front. Psychiatry

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“…As a new method to measure NFA, the performance of the prediction models should be tested. Except for existing model evaluation methods, such as R-square/R 2 (the proportion of the variability in the response variable), the reliability and validity of the predictive models were also evaluated by an improved psychometric method which is suitable for machine-learning models ( 54 ).…”

Section: Methodsmentioning

confidence: 99%

Identifying Chinese social media users' need for affect from their online behaviors

Hong

Zhao²,

Wang³

2023

Front. Public Health

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The need for affect (NFA), which refers to the motivation to approach or avoid emotion-inducing situations, is a valuable indicator of mental health monitoring and intervention, as well as many other applications. Traditionally, NFA has been measured using self-reports, which is not applicable in today's online scenarios due to its shortcomings in fast, large-scale assessments. This study proposed an automatic and non-invasive method for recognizing NFA based on social media behavioral data. The NFA questionnaire scores of 934 participants and their social media data were acquired. Then we run machine learning algorithms to train predictive models, which can be used to automatically identify NFA degrees of online users. The results showed that Extreme Gradient Boosting (XGB) performed best among several algorithms. The Pearson correlation coefficients between predicted scores and NFA questionnaire scores achieved 0.25 (NFA avoidance), 0.31 (NFA approach) and 0.34 (NFA total), and the split-half reliabilities were 0.66–0.70. Our research demonstrated that adolescents' NFA can be identified based on their social media behaviors, and opened a novel way of non-intrusively perceiving users' NFA which can be used for mental health monitoring and other situations that require large-scale NFA measurements.

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“…One previous study used a linear regression algorithm to establish a model to distinguish multi-dimensional psychological symptoms. Split-half reliability was one of the methods to evaluate the model and achieved a moderate or above correlation (Wang et al, 2020).…”

Section: Prediction Effects Of the Four Algorithm Modelsmentioning

confidence: 99%

Automatic Decision-Making Style Recognition Method Using Kinect Technology

et al. 2022

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In recent years, somatosensory interaction technology, represented by Microsoft’s Kinect hardware platform, has been widely used in various fields, such as entertainment, education, and medicine. Kinect technology can easily capture and record behavioral data, which provides new opportunities for behavioral and psychological correlation analysis research. In this paper, an automatic decision-style recognition method is proposed. Experiments involving 240 subjects were conducted to obtain face data and individual decision-making style score. The face data was obtained using the Kinect camera, and the decision-style score were obtained via a questionnaire. To realize automatic recognition of an individual decision-making style, machine learning was employed to establish the mapping relationship between the face data and a scaled evaluation of the decision-making style score. This study adopts a variety of classical machine learning algorithms, including Linear regression, Support vector machine regression, Ridge regression, and Bayesian ridge regression. The experimental results show that the linear regression model returns the best results. The correlation coefficient between the linear regression model evaluation results and the scale evaluation results was 0.6, which represents a medium and higher correlation. The results verify the feasibility of automatic decision-making style recognition method based on facial analysis.

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Identifying Psychological Symptoms Based on Facial Movements

Cited by 15 publications

References 54 publications

Mental states and personality based on real-time physical activity and facial expression recognition

Mental states and personality based on real-time physical activity and facial expression recognition

Identifying Chinese social media users' need for affect from their online behaviors

Automatic Decision-Making Style Recognition Method Using Kinect Technology

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