Identification of movement synchrony: Validation of windowed cross-lagged correlation and -regression with peak-picking algorithm

Autor: Jane Paulick, Désirée Schoenherr, Uwe Altmann, Ulrich Stangier, Brian S. Schwartz, Anne-Katharina Deisenhofer, Wolfgang Lutz, Julian A. Rubel, Bernhard Strauss
Přispěvatelé: Tay, Dennis
Jazyk: angličtina
Rok vydání: 2019
Předmět:
050103 clinical psychology
Normal Distribution
Video Recording
Social Sciences
Correlation
Database and Informatics Methods
ddc:150
Medicine and Health Sciences
Image Processing
Computer-Assisted
Psychology
Musculoskeletal System
Mathematics
Multidisciplinary
Geography
Applied Mathematics
Simulation and Modeling
05 social sciences
Research Assessment
Regression
Identification (information)
Data point
Physical Sciences
Medicine
Engineering and Technology
Anatomy
Algorithm
Sequence Analysis
Algorithms
Research Article
Bioinformatics
Science
Concordance
Movement
Human Geography
Research and Analysis Methods
050105 experimental psychology
Normal distribution
Humans
0501 psychology and cognitive sciences
Interpersonal Relations
Time series
Nonverbal Communication
Research Errors
Behavior
Biology and Life Sciences
Signal Bandwidth
Probability Theory
Probability Distribution
Psychotherapy
Logistic Models
Signal Processing
Earth Sciences
Human Mobility
Kappa
Zdroj: PLoS ONE
PLoS ONE, Vol 14, Iss 2, p e0211494 (2019)
ISSN: 1932-6203
Popis: In psychotherapy, movement synchrony seems to be associated with higher patient satisfaction and treatment outcome. However, it remains unclear whether movement synchrony rated by humans and movement synchrony identified by automated methods reflect the same construct. To address this issue, video sequences showing movement synchrony of patients and therapists (N = 10) or not (N = 10), were analyzed using motion energy analysis. Three different synchrony conditions with varying levels of complexity (naturally embedded, naturally isolated, and artificial) were generated for time series analysis with windowed cross-lagged correlation/ -regression (WCLC, WCLR). The concordance of ratings (human rating vs. automatic assessment) was computed for 600 different parameter configurations of the WCLC/WCLR to identify the parameter settings that measure movement synchrony best. A parameter configuration was rated as having a good identification rate if it yields high concordance with human-rated intervals (Cohen's kappa) and a low amount of over-identified data points. Results indicate that 76 configurations had a good identification rate (IR) in the least complex condition (artificial). Two had an acceptable IR with regard to the naturally isolated condition. Concordance was low with regard to the most complex (naturally embedded) condition. A valid identification of movement synchrony strongly depends on parameter configuration and goes beyond the identification of synchrony by human raters. Differences between human-rated synchrony and nonverbal synchrony measured by algorithms are discussed.
Databáze: OpenAIRE
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