Evidence map›Paper›PMID 27219342›Full record

ArticleTranslational psychiatry2016

Interaction and behaviour imaging: a novel method to measure mother-infant interaction using video 3D reconstruction.

C Leclère, M Avril, S Viaux-Savelon, N Bodeau, C Achard, S Missonnier, M Keren, R Feldman, M Chetouani, D Cohen

Abstract read
In one paragraph

Article in Translational psychiatry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Article
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  4. Reconstructing 3D Face of Infants in Social Interactions Using Morphable Models of Non-Infants.Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval · 2022
    Article
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  6. Article
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  11. Engagement in Human-Agent Interaction: An Overview.Frontiers in robotics and AI · 2020
    Review
  12. Article
  13. Article
  14. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

C LeclèreInstitut des Systèmes Intelligents et de Robotiques, CNRS, UMR 7222, Sorbonne Universités, Université Pierre et Marie Curie, Paris, France.
M AvrilInstitut des Systèmes Intelligents et de Robotiques, CNRS, UMR 7222, Sorbonne Universités, Université Pierre et Marie Curie, Paris, France.
S Viaux-SavelonInstitut des Systèmes Intelligents et de Robotiques, CNRS, UMR 7222, Sorbonne Universités, Université Pierre et Marie Curie, Paris, France.
N BodeauDepartment of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, AP-HP, Paris, France.
C AchardInstitut des Systèmes Intelligents et de Robotiques, CNRS, UMR 7222, Sorbonne Universités, Université Pierre et Marie Curie, Paris, France.
S MissonnierLaboratoire de Psychologie Clinique et Psychopathologie, Psychanalyse, EA4056, Paris V René Descartes University, Boulogne, France.
M KerenDepartment of Psychiatry, Infant Mental Health Unit, Geha Hospital, Tel Aviv University, Tel Aviv, Israel.
R FeldmanDepartment of Psychology and the Gonda Brain Sciences Center, Bar-Ilan University, Ramat-Gan, Israel.
M ChetouaniInstitut des Systèmes Intelligents et de Robotiques, CNRS, UMR 7222, Sorbonne Universités, Université Pierre et Marie Curie, Paris, France.
D CohenInstitut des Systèmes Intelligents et de Robotiques, CNRS, UMR 7222, Sorbonne Universités, Université Pierre et Marie Curie, Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Studying early interaction is essential for understanding development and psychopathology. Automatic computational methods offer the possibility to analyse social signals and behaviours of several partners simultaneously and dynamically. Here, 20 dyads of mothers and their 13-36-month-old infants were videotaped during mother-infant interaction including 10 extremely high-risk and 10 low-risk dyads using two-dimensional (2D) and three-dimensional (3D) sensors. From 2D+3D data and 3D space reconstruction, we extracted individual parameters (quantity of movement and motion activity ratio for each partner) and dyadic parameters related to the dynamics of partners heads distance (contribution to heads distance), to the focus of mutual engagement (percentage of time spent face to face or oriented to the task) and to the dynamics of motion activity (synchrony ratio, overlap ratio, pause ratio). Features are compared with blind global rating of the interaction using the coding interactive behavior (CIB). We found that individual and dyadic parameters of 2D+3D motion features perfectly correlates with rated CIB maternal and dyadic composite scores. Support Vector Machine classification using all 2D-3D motion features classified 100% of the dyads in their group meaning that motion behaviours are sufficient to distinguish high-risk from low-risk dyads. The proposed method may present a promising, low-cost methodology that can uniquely use artificial technology to detect meaningful features of human interactions and may have several implications for studying dyadic behaviours in psychiatry. Combining both global rating scales and computerized methods may enable a continuum of time scale from a summary of entire interactions to second-by-second dynamics.

Indexed as

Mother-Child RelationsVideo RecordingAdultChild, PreschoolFemaleHumansImaging, Three-DimensionalInfantMale

Identifiers

PMID27219342
PMCPMC5070050

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.