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Klein DN. Assessment of Depression in Adults and Youth. Assessment 2024; 31:110-125. [PMID: 37081793 DOI: 10.1177/10731911231167446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
This article selectively reviews the key issues and measures for the assessment of depressive disorders and symptoms in youth and adults. The first portion of the article addresses the nature and conceptualization of depression and some key issues that must be considered in its assessment. Next, the diagnostic interview and clinician- and self-administered rating scales that are most widely used to diagnose, screen for, and assess the severity of depression in adults and youth are selectively reviewed. In addition, the assessment of three transdiagnostic clinical features (anhedonia, irritability, and suicidality) that are frequently associated with both depression and other forms of psychopathology is discussed. The article concludes with some broad recommendations for assessing depression in research and clinical practice and suggestions for future research.
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2
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Conelea C, Liang H, DuBois M, Raab B, Kellman M, Wellen B, Jacob S, Wang S, Sun J, Lim K. Automated Quantification of Eye Tics Using Computer Vision and Deep Learning Techniques. Mov Disord 2024; 39:183-191. [PMID: 38146055 PMCID: PMC10895867 DOI: 10.1002/mds.29593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 12/27/2023] Open
Abstract
BACKGROUND Tourette syndrome (TS) tics are typically quantified using "paper and pencil" rating scales that are susceptible to factors that adversely impact validity. Video-based methods to more objectively quantify tics have been developed but are challenged by reliance on human raters and procedures that are resource intensive. Computer vision approaches that automate detection of atypical movements may be useful to apply to tic quantification. OBJECTIVE The current proof-of-concept study applied a computer vision approach to train a supervised deep learning algorithm to detect eye tics in video, the most common tic type in patients with TS. METHODS Videos (N = 54) of 11 adolescent patients with TS were rigorously coded by trained human raters to identify 1.5-second clips depicting "eye tic events" (N = 1775) and "non-tic events" (N = 3680). Clips were encoded into three-dimensional facial landmarks. Supervised deep learning was applied to processed data using random split and disjoint split regimens to simulate model validity under different conditions. RESULTS Area under receiver operating characteristic curve was 0.89 for the random split regimen, indicating high accuracy in the algorithm's ability to properly classify eye tic vs. non-eye tic movements. Area under receiver operating characteristic curve was 0.74 for the disjoint split regimen, suggesting that algorithm generalizability is more limited when trained on a small patient sample. CONCLUSIONS The algorithm was successful in detecting eye tics in unseen validation sets. Automated tic detection from video is a promising approach for tic quantification that may have future utility in TS screening, diagnostics, and treatment outcome measurement. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Christine Conelea
- University of Minnesota, Department of Psychiatry & Behavioral Sciences
| | - Hengyue Liang
- University of Minnesota, Department of Electrical & Computer Engineering
| | - Megan DuBois
- University of Minnesota, Department of Psychiatry & Behavioral Sciences
| | - Brittany Raab
- University of Minnesota, Department of Psychiatry & Behavioral Sciences
| | - Mia Kellman
- University of Minnesota, Department of Psychiatry & Behavioral Sciences
| | - Brianna Wellen
- University of Minnesota, Department of Psychiatry & Behavioral Sciences
| | - Suma Jacob
- University of Minnesota, Department of Psychiatry & Behavioral Sciences
| | - Sonya Wang
- University of Minnesota, Department of Neurology
| | - Ju Sun
- University of Minnesota, Department of Computer Science & Engineering
| | - Kelvin Lim
- University of Minnesota, Department of Psychiatry & Behavioral Sciences
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3
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Rodriguez SN, Gullapalli AR, Maurer JM, Tirrell PS, Egala U, Anderson NE, Harenski CL, Kiehl KA. Quantitative Head Dynamics Associated with Interpersonal (Grandiose-Manipulative) Psychopathic Traits in Incarcerated Youth. JOURNAL OF PSYCHOPATHOLOGY AND BEHAVIORAL ASSESSMENT 2022; 44:1054-1063. [PMID: 37008299 PMCID: PMC10065468 DOI: 10.1007/s10862-022-09988-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2022] [Indexed: 10/16/2022]
Abstract
Clinicians have long noted that individuals with elevated psychopathic traits can be characterized by unique interpersonal styles, including prolonged eye contact, invasion of interpersonal space, and frequent use of hand gestures. Such forms of nonverbal communication can be measured via hand, body, and head position and dynamics. Previous studies have developed an automated algorithm designed to capture head position and dynamics from digital recordings of clinical interviews in a sample of incarcerated adult men. We observed that higher psychopathy scores were associated with stationary head dwell time. Here, we applied a similar automated algorithm to assess head position and dynamics on videotaped clinical interviews assessing psychopathic traits from n = 242 youth housed at a maximum-security juvenile correctional facility. We observed that higher psychopathy scores (assessed via the Hare Psychopathy Checklist: Youth Version [PCL:YV]) were associated with unique patterns of head dynamics. Specifically, PCL:YV Total, Factor 1 (measuring grandiose-manipulative and callous-unemotional traits), and Facet 1 (measuring grandiose-manipulative traits) scores were associated with a higher proportion of time spent in a head dynamics pattern consisting of moderate movement away from the average head position. This study lays the groundwork for future investigations to apply quantitative methods to better understand patterns of nonverbal communication styles in clinical populations characterized by severe antisocial behavior.
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Affiliation(s)
- Samantha N. Rodriguez
- University of New Mexico, Department of Psychology, Albuquerque, NM, USA
- The Mind Research Network, Albuquerque, NM, USA
| | | | | | - Palmer S. Tirrell
- University of New Mexico, Department of Psychology, Albuquerque, NM, USA
| | - Ugesh Egala
- University of New Mexico, Department of Psychology, Albuquerque, NM, USA
| | | | | | - Kent A. Kiehl
- University of New Mexico, Department of Psychology, Albuquerque, NM, USA
- The Mind Research Network, Albuquerque, NM, USA
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4
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Lin RF, Leung TK, Liu YP, Hu KR. Disclosing Critical Voice Features for Discriminating between Depression and Insomnia—A Preliminary Study for Developing a Quantitative Method. Healthcare (Basel) 2022; 10:healthcare10050935. [PMID: 35628071 PMCID: PMC9142030 DOI: 10.3390/healthcare10050935] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/09/2022] [Accepted: 05/16/2022] [Indexed: 02/06/2023] Open
Abstract
Background: Depression and insomnia are highly related—insomnia is a common symptom among depression patients, and insomnia can result in depression. Although depression patients and insomnia patients should be treated with different approaches, the lack of practical biological markers makes it difficult to discriminate between depression and insomnia effectively. Purpose: This study aimed to disclose critical vocal features for discriminating between depression and insomnia. Methods: Four groups of patients, comprising six severe-depression patients, four moderate-depression patients, ten insomnia patients, and four patients with chronic pain disorder (CPD) participated in this preliminary study, which aimed to record their speaking voices. An open-source software, openSMILE, was applied to extract 384 voice features. Analysis of variance was used to analyze the effects of the four patient statuses on these voice features. Results: statistical analyses showed significant relationships between patient status and voice features. Patients with severe depression, moderate depression, insomnia, and CPD reacted differently to certain voice features. Critical voice features were reported based on these statistical relationships. Conclusions: This preliminary study shows the potential in developing discriminating models of depression and insomnia using voice features. Future studies should recruit an adequate number of patients to confirm these voice features and increase the number of data for developing a quantitative method.
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Affiliation(s)
- Ray F. Lin
- Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 32003, Taiwan;
- Correspondence:
| | - Ting-Kai Leung
- Department of Radiology, Taoyuan General Hospital, Ministry of Health and Welfare, No. 1492, Zhongshan Rd., Taoyuan City 33004, Taiwan;
- Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 11031, Taiwan
| | - Yung-Ping Liu
- Department of Industrial Engineering and Management, Chaoyang University of Technology, Taichung 413310, Taiwan;
| | - Kai-Rong Hu
- Department of Industrial Engineering and Management, Yuan Ze University, Taoyuan 32003, Taiwan;
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5
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Birnbaum ML, Abrami A, Heisig S, Ali A, Arenare E, Agurto C, Lu N, Kane JM, Cecchi G. Acoustic and Facial Features From Clinical Interviews for Machine Learning-Based Psychiatric Diagnosis: Algorithm Development. JMIR Ment Health 2022; 9:e24699. [PMID: 35072648 PMCID: PMC8822433 DOI: 10.2196/24699] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Revised: 04/29/2021] [Accepted: 12/01/2021] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND In contrast to all other areas of medicine, psychiatry is still nearly entirely reliant on subjective assessments such as patient self-report and clinical observation. The lack of objective information on which to base clinical decisions can contribute to reduced quality of care. Behavioral health clinicians need objective and reliable patient data to support effective targeted interventions. OBJECTIVE We aimed to investigate whether reliable inferences-psychiatric signs, symptoms, and diagnoses-can be extracted from audiovisual patterns in recorded evaluation interviews of participants with schizophrenia spectrum disorders and bipolar disorder. METHODS We obtained audiovisual data from 89 participants (mean age 25.3 years; male: 48/89, 53.9%; female: 41/89, 46.1%): individuals with schizophrenia spectrum disorders (n=41), individuals with bipolar disorder (n=21), and healthy volunteers (n=27). We developed machine learning models based on acoustic and facial movement features extracted from participant interviews to predict diagnoses and detect clinician-coded neuropsychiatric symptoms, and we assessed model performance using area under the receiver operating characteristic curve (AUROC) in 5-fold cross-validation. RESULTS The model successfully differentiated between schizophrenia spectrum disorders and bipolar disorder (AUROC 0.73) when aggregating face and voice features. Facial action units including cheek-raising muscle (AUROC 0.64) and chin-raising muscle (AUROC 0.74) provided the strongest signal for men. Vocal features, such as energy in the frequency band 1 to 4 kHz (AUROC 0.80) and spectral harmonicity (AUROC 0.78), provided the strongest signal for women. Lip corner-pulling muscle signal discriminated between diagnoses for both men (AUROC 0.61) and women (AUROC 0.62). Several psychiatric signs and symptoms were successfully inferred: blunted affect (AUROC 0.81), avolition (AUROC 0.72), lack of vocal inflection (AUROC 0.71), asociality (AUROC 0.63), and worthlessness (AUROC 0.61). CONCLUSIONS This study represents advancement in efforts to capitalize on digital data to improve diagnostic assessment and supports the development of a new generation of innovative clinical tools by employing acoustic and facial data analysis.
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Affiliation(s)
- Michael L Birnbaum
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States.,The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States.,The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Avner Abrami
- Computational Biology Center, IBM Research, Yorktown Heights, NY, United States
| | - Stephen Heisig
- Icahn School of Medicine at Mount Sinai, New York City, NY, United States
| | - Asra Ali
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States.,The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Elizabeth Arenare
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States.,The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States
| | - Carla Agurto
- Computational Biology Center, IBM Research, Yorktown Heights, NY, United States
| | - Nathaniel Lu
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States.,The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States
| | - John M Kane
- Department of Psychiatry, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY, United States.,The Feinstein Institute for Medical Research, Northwell Health, Manhasset, NY, United States.,The Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, United States
| | - Guillermo Cecchi
- Computational Biology Center, IBM Research, Yorktown Heights, NY, United States
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6
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Chen X, Pan Z. A Convenient and Low-Cost Model of Depression Screening and Early Warning Based on Voice Data Using for Public Mental Health. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:6441. [PMID: 34198659 PMCID: PMC8296267 DOI: 10.3390/ijerph18126441] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/10/2021] [Accepted: 06/10/2021] [Indexed: 12/12/2022]
Abstract
Depression is a common mental health disease, which has great harm to public health. At present, the diagnosis of depression mainly depends on the interviews between doctors and patients, which is subjective, slow and expensive. Voice data are a kind of data that are easy to obtain and have the advantage of low cost. It has been proved that it can be used in the diagnosis of depression. The voice data used for modeling in this study adopted the authoritative public data set, which had passed the ethical review. The features of voice data were extracted by Python programming, and the voice features were stored in the format of CSV files. Through data processing, a big database, containing 1479 voice feature samples, was generated for modeling. Then, the decision tree screening model of depression was established by 10-fold cross validation and algorithm selection. The experiment achieved 83.4% prediction accuracy on voice data set. According to the prediction results of the model, the patients can be given early warning and intervention in time, so as to realize the health management of personal depression.
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Affiliation(s)
- Xin Chen
- School of Medicine, Hangzhou Normal University, Hangzhou 311121, China;
- Engineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou Normal University, Hangzhou 311121, China
- Institute of VR and Intelligent System, Hangzhou Normal University, Hangzhou 311121, China
| | - Zhigeng Pan
- School of Medicine, Hangzhou Normal University, Hangzhou 311121, China;
- Engineering Research Center of Mobile Health Management System, Ministry of Education, Hangzhou Normal University, Hangzhou 311121, China
- Institute of VR and Intelligent System, Hangzhou Normal University, Hangzhou 311121, China
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7
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Muszynski M, Zelazny J, Girard JM, Morency LP. Depression Severity Assessment for Adolescents at High Risk of Mental Disorders. PROCEEDINGS OF THE ... ACM INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. ICMI (CONFERENCE) 2020; 2020:70-78. [PMID: 33782675 PMCID: PMC8005296 DOI: 10.1145/3382507.3418859] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
Recent progress in artificial intelligence has led to the development of automatic behavioral marker recognition, such as facial and vocal expressions. Those automatic tools have enormous potential to support mental health assessment, clinical decision making, and treatment planning. In this paper, we investigate nonverbal behavioral markers of depression severity assessed during semi-structured medical interviews of adolescent patients. The main goal of our research is two-fold: studying a unique population of adolescents at high risk of mental disorders and differentiating mild depression from moderate or severe depression. We aim to explore computationally inferred facial and vocal behavioral responses elicited by three segments of the semi-structured medical interviews: Distress Assessment Questions, Ubiquitous Questions, and Concept Questions. Our experimental methodology reflects best practise used for analyzing small sample size and unbalanced datasets of unique patients. Our results show a very interesting trend with strongly discriminative behavioral markers from both acoustic and visual modalities. These promising results are likely due to the unique classification task (mild depression vs. moderate and severe depression) and three types of probing questions.
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8
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Bhatia S, Goecke R, Hammal Z, Cohn JF. Automated Measurement of Head Movement Synchrony during Dyadic Depression Severity Interviews. PROCEEDINGS OF THE ... INTERNATIONAL CONFERENCE ON AUTOMATIC FACE AND GESTURE RECOGNITION. IEEE INTERNATIONAL CONFERENCE ON AUTOMATIC FACE & GESTURE RECOGNITION 2019; 2019. [PMID: 31745390 DOI: 10.1109/fg.2019.8756509] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
With few exceptions, most research in automated assessment of depression has considered only the patient's behavior to the exclusion of the therapist's behavior. We investigated the interpersonal coordination (synchrony) of head movement during patient-therapist clinical interviews. Our sample consisted of patients diagnosed with major depressive disorder. They were recorded in clinical interviews (Hamilton Rating Scale for Depression, HRSD) at 7-week intervals over a period of 21 weeks. For each session, patient and therapist 3D head movement was tracked from 2D videos. Head angles in the horizontal (pitch) and vertical (yaw) axes were used to measure head movement. Interpersonal coordination of head movement between patients and therapists was measured using windowed cross-correlation. Patterns of coordination in head movement were investigated using the peak picking algorithm. Changes in head movement coordination over the course of treatment were measured using a hierarchical linear model (HLM). The results indicated a strong effect for patient-therapist head movement synchrony. Within-dyad variability in head movement coordination was found to be higher than between-dyad variability, meaning that differences over time in a dyad were higher as compared to the differences between dyads. Head movement synchrony did not change over the course of treatment with change in depression severity. To the best of our knowledge, this study is the first attempt to analyze the mutual influence of patient-therapist head movement in relation to depression severity.
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Affiliation(s)
- Shalini Bhatia
- Human-Centred Technology Research Centre, University of Canberra, Canberra, Australia
| | - Roland Goecke
- Human-Centred Technology Research Centre, University of Canberra, Canberra, Australia
| | - Zakia Hammal
- Robotics Institute, Carnegie Mellon University, Pittsburgh, USA
| | - Jeffrey F Cohn
- Department of Psychology, University of Pittsburgh, Pittsburgh, USA
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9
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Su KP. Are we all the same? The critical role of translational brain, behavior, and immunity research in East Asia. Brain Behav Immun 2019; 82:1-2. [PMID: 31302174 DOI: 10.1016/j.bbi.2019.07.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Accepted: 07/06/2019] [Indexed: 10/26/2022] Open
Abstract
Traditional Chinese medicine (TCM) is founded on the idea that the "Mind and Body" are all interconnected. When you have a difficult time or disturbing lifestyle and experience a series of somatic and psychological symptoms mimicking inflammation-induced sickness behaviors, the TCM practitioners would be very likely to give you a diagnosis of "On Fire" and prescribe specific food intervention and herbal medicine, which might be considered anti-inflammatory to "cool you down." Psychoneuroimmunology has been long stemmed in ancient medicine.
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Affiliation(s)
- Kuan-Pin Su
- Department of Psychiatry & Mind-Body Interface Laboratory (MBI-Lab), China Medical University Hospital, Taichung, Taiwan; College of Medicine, China Medical University, Taichung, Taiwan.
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10
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Haines N, Bell Z, Crowell S, Hahn H, Kamara D, McDonough-Caplan H, Shader T, Beauchaine TP. Using automated computer vision and machine learning to code facial expressions of affect and arousal: Implications for emotion dysregulation research. Dev Psychopathol 2019; 31:871-886. [PMID: 30919792 PMCID: PMC7319037 DOI: 10.1017/s0954579419000312] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
As early as infancy, caregivers' facial expressions shape children's behaviors, help them regulate their emotions, and encourage or dissuade their interpersonal agency. In childhood and adolescence, proficiencies in producing and decoding facial expressions promote social competence, whereas deficiencies characterize several forms of psychopathology. To date, however, studying facial expressions has been hampered by the labor-intensive, time-consuming nature of human coding. We describe a partial solution: automated facial expression coding (AFEC), which combines computer vision and machine learning to code facial expressions in real time. Although AFEC cannot capture the full complexity of human emotion, it codes positive affect, negative affect, and arousal-core Research Domain Criteria constructs-as accurately as humans, and it characterizes emotion dysregulation with greater specificity than other objective measures such as autonomic responding. We provide an example in which we use AFEC to evaluate emotion dynamics in mother-daughter dyads engaged in conflict. Among other findings, AFEC (a) shows convergent validity with a validated human coding scheme, (b) distinguishes among risk groups, and (c) detects developmental increases in positive dyadic affect correspondence as teen daughters age. Although more research is needed to realize the full potential of AFEC, findings demonstrate its current utility in research on emotion dysregulation.
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Affiliation(s)
- Nathaniel Haines
- Department of Psychology, Ohio State University, Columbus, OH, USA
| | - Ziv Bell
- Department of Psychology, Ohio State University, Columbus, OH, USA
| | - Sheila Crowell
- Department of Psychology, University of Utah, Salt Lake City, UT, USA
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Hunter Hahn
- Department of Psychology, Ohio State University, Columbus, OH, USA
| | - Dana Kamara
- Department of Psychology, Ohio State University, Columbus, OH, USA
| | | | - Tiffany Shader
- Department of Psychology, Ohio State University, Columbus, OH, USA
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Pampouchidou A, Simantiraki O, Vazakopoulou CM, Chatzaki C, Pediaditis M, Maridaki A, Marias K, Simos P, Yang F, Meriaudeau F, Tsiknakis M. Facial geometry and speech analysis for depression detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2017:1433-1436. [PMID: 29060147 DOI: 10.1109/embc.2017.8037103] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Depression is one of the most prevalent mental disorders, burdening many people world-wide. A system with the potential of serving as a decision support system is proposed, based on novel features extracted from facial expression geometry and speech, by interpreting non-verbal manifestations of depression. The proposed system has been tested both in gender independent and gender based modes, and with different fusion methods. The algorithms were evaluated for several combinations of parameters and classification schemes, on the dataset provided by the Audio/Visual Emotion Challenge of 2013 and 2014. The proposed framework achieved a precision of 94.8% for detecting persons achieving high scores on a self-report scale of depressive symptomatology. Optimal system performance was obtained using a nearest neighbour classifier on the decision fusion of geometrical features in the gender independent mode, and audio based features in the gender based mode; single visual and audio decisions were combined with the OR binary operation.
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12
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Abstract
Videotaping of experimental sessions is a common practice across many disciplines of psychology, ranging from clinical therapy, to developmental science, to animal research. Audio-visual data are a rich source of information that can be easily recorded; however, analysis of the recordings presents a major obstacle to project completion. Coding behavior is time-consuming and often requires ad-hoc training of a student coder. In addition, existing software is either prohibitively expensive or cumbersome, which leaves researchers with inadequate tools to quickly process video data. We offer the Simple Video Coder-free, open-source software for behavior coding that is flexible in accommodating different experimental designs, is intuitive for students to use, and produces outcome measures of event timing, frequency, and duration. Finally, the software also offers extraction tools to splice video into coded segments suitable for training future human coders or for use as input for pattern classification algorithms.
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13
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Werner P, Al-Hamadi A, Limbrecht-Ecklundt K, Walter S, Traue HC. Head movements and postures as pain behavior. PLoS One 2018; 13:e0192767. [PMID: 29444153 PMCID: PMC5812618 DOI: 10.1371/journal.pone.0192767] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Accepted: 01/30/2018] [Indexed: 11/19/2022] Open
Abstract
Pain assessment can benefit from observation of pain behaviors, such as guarding or facial expression, and observational pain scales are widely used in clinical practice with nonverbal patients. However, little is known about head movements and postures in the context of pain. In this regard, we analyze videos of three publically available datasets. The BioVid dataset was recorded with healthy participants subjected to painful heat stimuli. In the BP4D dataset, healthy participants performed a cold-pressor test and several other tasks (meant to elicit emotion). The UNBC dataset videos show shoulder pain patients during range-of-motion tests to their affected and unaffected limbs. In all videos, participants were sitting in an upright position. We studied head movements and postures that occurred during the painful and control trials by measuring head orientation from video over time, followed by analyzing posture and movement summary statistics and occurrence frequencies of typical postures and movements. We found significant differences between pain and control trials with analyses of variance and binomial tests. In BioVid and BP4D, pain was accompanied by head movements and postures that tend to be oriented downwards or towards the pain site. We also found differences in movement range and speed in all three datasets. The results suggest that head movements and postures should be considered for pain assessment and research. As additional pain indicators, they possibly might improve pain management whenever behavior is assessed, especially in nonverbal individuals such as infants or patients with dementia. However, in advance more research is needed to identify specific head movements and postures in pain patients.
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Affiliation(s)
- Philipp Werner
- Neuro-Information Technology group, Institute for Information Technology and Communications, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | - Ayoub Al-Hamadi
- Neuro-Information Technology group, Institute for Information Technology and Communications, Otto-von-Guericke University Magdeburg, Magdeburg, Germany
| | | | - Steffen Walter
- Medical Psychology, University Clinic for Psychosomatic Medicine and Psychotherapy, Ulm, Germany
| | - Harald C. Traue
- Medical Psychology, University Clinic for Psychosomatic Medicine and Psychotherapy, Ulm, Germany
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14
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Beevers CG. Editorial overview: The assessment, etiology, and treatment of unipolar depression. Curr Opin Psychol 2015; 4:v-viii. [PMID: 26273694 DOI: 10.1016/j.copsyc.2015.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Christopher G Beevers
- Department of Psychology and Institute for Mental Health Research, University of Texas at Austin, 305 E. 23rd St., Stop E9000, Austin, TX, 78712, USA,
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