Automated Measurement of Head Movement Synchrony during Dyadic Depression Severity Interviews

Shalini BHATIA, Roland GOECKE, Zakia Hammal, Jeffrey F Cohn

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

7 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
Place of PublicationDanvers, United States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages169-176
Number of pages8
ISBN (Electronic)9781728100890
ISBN (Print)9781728100890
DOIs
Publication statusPublished - 1 May 2019
Event14th IEEE International Conference on Automatic Face and Gesture Recognition - Lille, France
Duration: 14 May 201918 May 2019
http://fg2019.org/

Publication series

NameProceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019

Conference

Conference14th IEEE International Conference on Automatic Face and Gesture Recognition
Abbreviated titleFG 2019
Country/TerritoryFrance
CityLille
Period14/05/1918/05/19
Internet address

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