Paper: AMAB – Automated Measurement and Analysis of Body Motion

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Psychologists are interested in measuring, describing, and predicting human behavior. There are several ways to analyse such behavior, from questionnaires and self-reports to observing such behaviors, either in real time or on video recordings. Currently, these videos are usually manually annotated and a predefined set of movements is coded. Although these annotated videos have provided very interesting insights in human nature, new technologies provide even better ways to gain such insights.

Together with co-authors Ronald Poppe, Dirk Heylen, and Paul Taylor, I developed a method to automatically measure human behavior using motion capture equipment. With this equipment, you can create a 3D image of a person and record with many frames per second exactly how this person is moving and behaving. We discuss the benefits and disadvantages of the different motion capture systems that can be used, and provide a step-by-step guide on how to screen and analyze this type of data. We also propose some interesting application areas and research questions that can be explored using the AMAB method. Algorithms and equations are supplied.

Full reference:

Poppe, R. W., Van Der Zee, S., Taylor, P. J., & Heylen, D. K. J., (2014). AMAB: Automated Measurement and Analysis of Body Motion. Behavior Research Methods, 46, 625-633.


Technologies that measure human nonverbal behavior have existed for some time, and their use in the analysis of social behavior has become more popular following the development of sensor technologies that record full-body
movement. However, a standardized methodology to efficiently represent and analyze full-body motion is absent. In
this article, we present automated measurement and analysis of body motion (AMAB), a methodology for examining individual and interpersonal nonverbal behavior from the output of full-body motion tracking systems. We address the recording, screening, and normalization of the data, providing methods for standardizing the data across recording condition and across subject body sizes. We then propose a series of dependent measures to operationalize common research questions in psychological research.We present practical examples from several application areas to demonstrate the efficacy of our proposed method for full-body measurements and comparisons across time, space, body parts, and subjects.



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