Evaluating the severity of depressive symptoms using upper body motion captured by RGB-depth sensors and machine learning in a clinical interview setting: A preliminary study

Horigome, Toshiro; Sumali, Brian; Kitazawa, Momoko; Yoshimura, Michitaka; Liang, Kuo Ching; Tazawa, Yuki; Fujita, Takanori; Mimura, Masaru; Kishimoto, Toshifumi

doi:10.1016/j.comppsych.2020.152169

Cited by 11 publications

(9 citation statements)

References 37 publications

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“…Motor difficulties were another central symptom in this depression network model, which confirms the notion that psychomotor problems are among the most important individual symptoms in depression (68)(69)(70)(71)(72). This could be partly attributed to disrupted structural and functional coupling between different brain networks (73,74).…”

Section: Discussionsupporting

confidence: 82%

Network Analysis of Depressive Symptoms Among Residents of Wuhan in the Later Stage of the COVID-19 Pandemic

Zhao

Zhang

et al. 2021

Front. Psychiatry

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Background: Depression has been a common mental health problem during the COVID-19 epidemic. From a network perspective, depression can be conceptualized as the result of mutual interactions among individual symptoms, an approach that may elucidate the structure and mechanisms underlying this disorder. This study aimed to examine the structure of depression among residents in Wuhan, the epicenter of the COVID-19 outbreak in China, in the later stage of the COVID-19 pandemic.Methods: A total of 2,515 participants were recruited from the community via snowball sampling. The Patient Health Questionnaire was used to assess self-reported depressive symptoms with the QuestionnaireStar program. The network structure and relevant centrality indices of depression were examined in this sample.Results: Network analysis revealed Fatigue, Sad mood, Guilt and Motor disturbances as the most central symptoms, while Suicide and Sleep problems had the lowest centrality. No significant differences were found between women and men regarding network structure (maximum difference = 0.11, p = 0.44) and global strength (global strength difference = 0.04; female vs. male: 3.78 vs. 3.83, p = 0.51), a finding that suggests there are no gender differences in the structure or centrality of depressive symptoms.Limitations: Due to the cross-sectional study design, causal relationships between these depressive symptoms or dynamic changes in networks over time could not be established.Conclusions: Fatigue, Sad mood, Guilt, and Motor disturbances should be prioritized as targets in interventions and prevention efforts to reduce depression among residents in Wuhan, in the later stage of the COVID-19 pandemic.

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Section: Discussionsupporting

confidence: 82%

Network Analysis of Depressive Symptoms Among Residents of Wuhan in the Later Stage of the COVID-19 Pandemic

Zhao

Zhang

et al. 2021

Front. Psychiatry

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“…Along similar lines, many studies that analyze audio and visual data of patients with SCZ have demonstrated abnormalities in language [14][15][16] , speech [17][18][19][20][21] , facial expressions [21][22][23] , and motor [24][25][26] behaviors. Similar studies of patients with MDD have identified abnormalities in verbal [27][28][29][30] and nonverbal behaviors [31][32][33][34][35][36] , facial expressions 34,37,38 , and body movement [39][40][41] associated with MDD. This stream of the literature suggests that digital phenotyping is a promising avenue toward objective behavioral measures for characterizing mental disorders.…”

Section: Introductionmentioning

confidence: 64%

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach

Yang

Chakraborty

et al. 2022

Schizophr

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Schizophrenia (SCZ) and depression (MDD) are two chronic mental disorders that seriously affect the quality of life of millions of people worldwide. We aim to develop machine-learning methods with objective linguistic, speech, facial, and motor behavioral cues to reliably predict the severity of psychopathology or cognitive function, and distinguish diagnosis groups. We collected and analyzed the speech, facial expressions, and body movement recordings of 228 participants (103 SCZ, 50 MDD, and 75 healthy controls) from two separate studies. We created an ensemble machine-learning pipeline and achieved a balanced accuracy of 75.3% for classifying the total score of negative symptoms, 75.6% for the composite score of cognitive deficits, and 73.6% for the total score of general psychiatric symptoms in the mixed sample containing all three diagnostic groups. The proposed system is also able to differentiate between MDD and SCZ with a balanced accuracy of 84.7% and differentiate patients with SCZ or MDD from healthy controls with a balanced accuracy of 82.3%. These results suggest that machine-learning models leveraging audio-visual characteristics can help diagnose, assess, and monitor patients with schizophrenia and depression.

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“…Speech rate, pause time, and response time showed significant associations with the total score of Hamilton Depression Rating Scale ( 15 , 16 ). We have also reported that body movement captured by infrared sensor can be reflective of depression severity ( 15 , 17 ).…”

Section: Introductionmentioning

confidence: 93%

Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design

et al. 2021

Self Cite

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Introduction: Mental disorders are a leading cause of disability worldwide. Depression has a significant impact in the field of occupational health because it is particularly prevalent during working age. On the other hand, there are a growing number of studies on the relationship between “well-being” and employee productivity. To promote healthy and productive workplaces, this study aims to develop a technique to quantify stress and well-being in a way that does not disturb the workplace.Methods and analysis: This is a single-arm prospective observational study. The target population is adult (>20 years old) workers at companies that often engage in desk work; specifically, a person who sits in front of a computer for at least half their work hours. The following data will be collected: (a) participants' background characteristics; (b) participants' biological data during the 4-week observation period using sensing devices such as a camera built into the computer (pulse wave data extracted from the facial video images), a microphone built into their work computer (voice data), and a wristband-type wearable device (electrodermal activity data, body motion data, and body temperature); (c) stress, well-being, and depression rating scale assessment data. The analysis workflow is as follows: (1) primary analysis, comprised of using software to digitalize participants' vital information; (2) secondary analysis, comprised of examining the relationship between the quantified vital data from (1), stress, well-being, and depression; (3) tertiary analysis, comprised of generating machine learning algorithms to estimate stress, well-being, and degree of depression in relation to each set of vital data as well as multimodal vital data.Discussion: This study will evaluate digital phenotype regarding stress and well-being of white-collar workers over a 4-week period using persistently obtainable biomarkers such as heart rate, acoustic characteristics, body motion, and electrodermal activity. Eventually, this study will lead to the development of a machine learning algorithm to determine people's optimal levels of stress and well-being.Ethics and dissemination: Collected data and study results will be disseminated widely through conference presentations, journal publications, and/or mass media. The summarized results of our overall analysis will be supplied to participants.Registration: UMIN000036814

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Evaluating the severity of depressive symptoms using upper body motion captured by RGB-depth sensors and machine learning in a clinical interview setting: A preliminary study

Cited by 11 publications

References 37 publications

Network Analysis of Depressive Symptoms Among Residents of Wuhan in the Later Stage of the COVID-19 Pandemic

Network Analysis of Depressive Symptoms Among Residents of Wuhan in the Later Stage of the COVID-19 Pandemic

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach

Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design

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