Identifying Psychological Symptoms Based on Facial Movements.

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

doi:10.3389/fpsyt.2020.607890

Abstract

Background: Many methods have been proposed to automatically identify the presence of mental illness, but these have mostly focused on one specific mental illness. In some non-professional scenarios, it would be more helpful to understand an individual's mental health status from all perspectives.Methods: We recruited 100 participants. Their multi-dimensional psychological symptoms of mental health were evaluated using the Symptom Checklist 90 (SCL-90) and their facial movements under neutral stimulation were recorded using Microsoft Kinect. We extracted the time-series characteristics of the key points as the input, and the subscale scores of the SCL-90 as the output to build facial prediction models. Finally, the convergent validity, discriminant validity, criterion validity, and the split-half reliability were respectively assessed using a multitrait-multimethod matrix and correlation coefficients.Results: The correlation coefficients between the predicted values and actual scores were 0.26 and 0.42 (P < 0.01), which indicated good criterion validity. All models except depression had high convergent validity but low discriminant validity. Results also indicated good levels of split-half reliability for each model [from 0.516 (hostility) to 0.817 (interpersonal sensitivity)] (P < 0.001).Conclusion: The validity and reliability of facial prediction models were confirmed for the measurement of mental health based on the SCL-90. Our research demonstrated that fine-grained aspects of mental health can be identified from the face, and provided a feasible evaluation method for multi-dimensional prediction models.

Highlights

Mental illnesses have a significant impact on an individual’s physical health [1], achievements [2, 3], and life satisfaction [4]
Since the Symptom Checklist 90 (SCL-90) assesses a wide range of psychiatric features and can measure multiple physical and psychological symptoms, it has been widely used in the mental health assessment of various groups [23]
Our study suggests the possibility that different psychological symptoms of mental illnesses may have different facial movements that can correspond to the SCL-90 scores, which are detailed and granular

Summary

Introduction

Mental illnesses have a significant impact on an individual’s physical health [1], achievements [2, 3], and life satisfaction [4]. Identifying an individual’s mental health status from a range of perspectives may be more helpful in non-professional scenarios such as self-monitoring or large-scale monitoring. Many studies have found that the physiological and behavioral indicators of individuals with mental illnesses differ, including brain activity [8, 9], galvanic skin response [10], eye contact [11, 12], voice [13, 14], and facial movements [15]. Neural activity in response to different emotional faces can help distinguish bipolar depression from unipolar depression. Such differences make it possible for machine learning models to diagnose the multi-dimensional psychological symptoms of mental illnesses. In some non-professional scenarios, it would be more helpful to understand an individual’s mental health status from all perspectives

Methods

Results

Discussion

Conclusion