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Behavior Research Methods

, Volume 46, Issue 3, pp 625–633 | Cite as

AMAB: Automated measurement and analysis of body motion

  • Ronald PoppeEmail author
  • Sophie Van Der Zee
  • Dirk K. J. Heylen
  • Paul J. Taylor
Article
First Online:
  • 618 Downloads

Abstract

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.

Keywords

Motion capture Human motion analysis Measurement of body motion Body motion analysis 
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Notes

Acknowledgment

The authors acknowledge financial support from the Dutch programme COMMIT and from the EU FP7 network of excellence SSPNet.

Supplementary material

13428_2013_398_MOESM1_ESM.zip (33 kb)
ESM 1 (ZIP 33.2 KB)

References

  1. Altorfer, A., Jossen, S., Wurmle, O., Kasermann, M.-L., Foppa, K., & Zimmermann, H. (2000). Measurement and meaning of head movements in everyday face-to-face communicative interaction. Behavior Research Methods, Instruments, & Computers, 32, 17–32. doi: 10.3758/BF03200785 CrossRefGoogle Scholar
  2. Bailenson, J. N., Blascovich, J., Beall, A. C., & Loomis, J. M. (2003). Interpersonal distance in immersive virtual environments. Personality and Social Psychology Bulletin, 29, 819–833. doi: 10.1177/0146167203029007002 PubMedCrossRefGoogle Scholar
  3. Bente, G. (1989). Facilities for the graphical computer simulation of head and body movements. Behavior Research Methods, Instruments, & Computers, 21, 455–462.CrossRefGoogle Scholar
  4. Bente, G., Petersen, A., Krämer, N. C., & De Ruiter, J. P. (2001). Transcript-based computer animation of movement: Evaluating a new tool for nonverbal behavior research. Behavior Research Methods, Instruments, & Computers, 33, 303–310. doi: 10.3758/BF03195383 CrossRefGoogle Scholar
  5. Bente, G., Senokozlieva, M., Pennig, S., Al-Issa, A., & Fischer, O. (2008). Deciphering the secret code: A new methodology for the cross-cultural analysis of nonverbal behavior. Behavior Research Methods, 40, 269–277. doi: 10.3758/BRM.40.1.269 PubMedCrossRefGoogle Scholar
  6. Bezodis, N. E., Salo, A. I. T., & Trewartha, G. (2010). Choice of sprint start performance measure affects the performance-based ranking within a group of sprinters: Which is the most appropriate measure? Sports Biomechanics, 9, 258–269. doi: 10.1080/14763141.2010.538713 PubMedCrossRefGoogle Scholar
  7. Chartrand, T. L., & Bargh, J. A. (1999). The chameleon effect: The perception-behavior link and social interaction. Journal of Personality and Social Psychology, 76, 893–910. doi: 10.1037/0022-3514.76.6.893 PubMedCrossRefGoogle Scholar
  8. Chartrand, T. L., & Lakin, J. L. (2013). The antecedents and consequences of human behavioral mimicry. Annual Review of Psychology, 64, 285–308. doi: 10.1146/annurev-psych-113011-143754 PubMedCrossRefGoogle Scholar
  9. Dael, N., Mortillaro, M., & Scherer, K. R. (2012). The body action and posture coding system (BAP): Development and reliability. Journal of Nonverbal Behavior, 36, 97–121. doi: 10.1007/s10919-012-0130-0 CrossRefGoogle Scholar
  10. Dakin, C. J., Luu, B. L., van den Doel, K., Inglis, J. T., & Blouin, J. (2010). Frequency-specific modulation of vestibular-evoked sway responses in humans. Journal of Neurophysiology, 103, 1048–1056. doi: 10.1152/jn.00881.2009 PubMedCrossRefGoogle Scholar
  11. Doron, C., Beattie, G., & Shovelton, S. (2010). Nonverbal indicators of deception: How iconic gestures reveal thoughts that cannot be suppressed. Semiotica, 133–174. doi: 10.1515/semi.2010.055
  12. Dotsch, R., & Wigboldus, D. H. J. (2008). Virtual prejudice. Journal of Experimental Social Psychology, 44, 1194–1198. doi: 10.1016/j.jesp.2008.03.003 CrossRefGoogle Scholar
  13. Ekman, P. (1965). Communication through nonverbal behavior: A source of information about an interpersonal relationship. In S. S. Tomkins & C. Izard (Eds.), Affect, cognition, and personality (pp. 390–442). New York: Springer.Google Scholar
  14. Feese, S., Arnrich, B., Tröster, G., Meyer, B., & Jonas, K. (2012). Quantifying behavioral mimicry by automatic detection of nonverbal cues from body motion. In A. Nijholt, A. Vinciarelli, B. Schüller, & M. Smith (Eds.), International conference on social computing (pp. 520–525). Piscataway, NJ: IEEE. doi: 10.1109/SocialCom-PASSAT.2012.48 Google Scholar
  15. Frey, S., & Von Cranach, M. (1973). A method for the assessment of body movement variability. In M. von Cranach & I. Vine (Eds.), Social communication and movement (pp. 389–418). New York, NY: Academic.Google Scholar
  16. Grammer, K., Kruck, K. B., & Magnusson, M. S. (1998). The courtship dance: Patterns of nonverbal synchronization in opposite-sex encounters. Journal of Nonverbal Behavior, 22(1), 3–29. doi: 10.1023/A:1022986608835 CrossRefGoogle Scholar
  17. Hall, E. T. (1966). The hidden dimension. New York, NY: Doubleday.Google Scholar
  18. Hayduk, L. A. (1983). Personal space: Where we now stand. Psychological Bulletin, 94, 293–335. doi: 10.1037/0033-2909.94.2.293 CrossRefGoogle Scholar
  19. Hirsbrunner, H. P., Frey, S., & Crawford, R. (1987). Movement in human interaction: Description, parameter formation, and analysis. In A. Siegman & S. Feldstein (Eds.), Nonverbal behavior and communication (pp. 99–140). Hillside, NJ: Lawrence Erlbaum Assocates.Google Scholar
  20. Kleinsmith, A., & Bianchi-Berthouze, N. (2013). Affective body expression perception and recognition: A survey. IEEE Transactions on Affective Computing, 4, 15–33. doi: 10.1109/T-AFFC.2012.16 CrossRefGoogle Scholar
  21. Kleinsmith, A., Bianchi-Berthouze, N., & Steed, A. (2011). Automatic recognition of non-acted affective postures. IEEE Transactions on Systems, Man, and Cybernetics - Part B, 41, 1027–1038. doi: 10.1109/TSMCB.2010.2103557 CrossRefGoogle Scholar
  22. Krishnan, N. C., Juillard, C., Colbry, D., & Panchanathan, S. (2009). Recognition of hand movements using wearable accelerometers. Journal of Ambient Intelligence and Smart Environments, 1, 143–155. doi: 10.3233/AIS-2009-0019 Google Scholar
  23. Lausberg, H., & Sloetjes, H. (2009). Coding gestural behavior with the NEUROGES-ELAN system. Behavior Research Methods, 41, 841–849. doi: 10.3758/BRM.41.3.841 PubMedCrossRefGoogle Scholar
  24. McNeill, D. (1985). So you think gestures are nonverbal? Psychological Review, 92, 350–371. doi: 10.1037/0033-295X.92.3.350 CrossRefGoogle Scholar
  25. McNeill, D. (1992). Hand and mind: What gestures reveal about thought. Chicago, IL: University of Chicago Press.Google Scholar
  26. Mead, R., Atrash, A., & Mataric, M. J. (2013). Automated proxemic feature extraction and behavior recognition: Applications in human-robot interaction. International Journal of Social Robotics, 5, 367–378. doi: 10.1007/s12369-013-0189-8 CrossRefGoogle Scholar
  27. Oullier, O., de Guzman, G. C., Jantzen, K. J., Lagarde, J., & Kelso, J. A. S. (2007). Social coordination dynamics: Measuring human bonding. Social Neuroscience, 3, 178–192. doi: 10.1080/17470910701563392 CrossRefGoogle Scholar
  28. Paxton, A., & Dale, R. (2013). Frame-differencing methods for measuring bodily synchrony in conversation. Behavior Research Methods, 45, 329–343. doi: 10.3758/s13428-012-0249-2 PubMedCrossRefGoogle Scholar
  29. Poppe, R. (2007). Vision-based human motion analysis: An overview. Computer Vision and Image Understanding, 108, 4–18. doi: 10.1016/j.cviu.2006.10.016 CrossRefGoogle Scholar
  30. Scherer, K. R., & Ekman, P. (1982). Methodological issues in studying nonverbal behavior. In K. R. Scherer & P. Ekman (Eds.), Handbook of methods in nonverbal behavior research (pp. 45–135). New York: Cambridge University Press.Google Scholar
  31. Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A. W., Finocchio, M., Blake, A., ... Moore, R. (2013). Real-time human pose recognition in parts from single depth images. Communications of the ACM, 56, 116–124. doi: 10.1145/2398356.2398381 CrossRefGoogle Scholar
  32. Slawinski, J., Dumas, R., Cheze, L., Ontanon, G., Miller, C., & Mazure–Bonnefoy, A. (2013). Effect of postural changes on 3D joint angular velocity during starting block phase. Journal of Sports Sciences, 31, 256–263. doi: 10.1080/02640414.2012.729076 PubMedCrossRefGoogle Scholar
  33. Stel, M., van Dijk, E., & Olivier, E. (2009). You want to know the truth? Then don’t mimic! Psychological Science, 20, 693–699. doi: 10.1111/j.1467-9280.2009.02350.x PubMedCrossRefGoogle Scholar
  34. Vick, S. J., Waller, B. M., Parr, L. A., Smith Pasqualini, M. C., & Bard, K. A. (2006). A cross-species comparison of facial morphology and movement in humans and chimpanzees using the Facial Action Coding System (FACS)". Journal of Nonverbal Behavior, 31, 1–20. doi: 10.1007/s10919-006-0017-z CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2013

Authors and Affiliations

  • Ronald Poppe
    • 1
    Email author
  • Sophie Van Der Zee
    • 2
  • Dirk K. J. Heylen
    • 1
  • Paul J. Taylor
    • 1
    • 2
  1. 1.Department of Human Media InteractionUniversity of TwenteEnschedeThe Netherlands
  2. 2.Lancaster UniversityLancasterUK

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