Show Me Your Moves: Analyzing Body Signals to Predict Creativity of Knowledge Workers

  • Marius Stein
  • Peter A. Gloor
  • Daniel Oster
Part of the Studies on Entrepreneurship, Structural Change and Industrial Dynamics book series (ESID)


We propose a novel approach to measuring the collaboration of knowledge workers, using body sensing smartwatches to capture psychometric data about individuals in a team. In a proof of concept study, we collected 2653 samples of body signals by equipping 15 people with our body sensing smartwatch over the course of 3 days during a design workshop. Additionally, we polled the users about their self-perceived team creativity at the end of each day. By employing multiple linear regression models, we found that body signals tracked by the smartwatch correlate significantly with the perceived team creativity reported by the individuals. Comparing those correlations with known predictors of creativity such as mood states and personality traits, we found that movement-related body signals predict creativity on the same accuracy level as mood states and personality traits do.


  1. Abdel-Khalek, A. M. (2006). Measuring happiness with a single-item scale. Social Behavior and Personality: An International Journal, 34, 139–150.CrossRefGoogle Scholar
  2. Adaman, J. E., & Blaney, P. H. (1995). The effects of musical mood induction on creativity. Journal of Creative Behaviour, 29, 95–108.CrossRefGoogle Scholar
  3. Amabile, T. M. (1988). A model of creativity and innovation in organizations. Research in Organizational Behavior, 10, 123–167.Google Scholar
  4. Aral, S., Brynjolfsson, E., & Van Alstyne, M. (2012). Information, technology, and information worker productivity. Information Systems Research, 23, 849–867.CrossRefGoogle Scholar
  5. Ashby, F. G., Isen, A. M., et al. (1999). A neuropsychological theory of positive affect and its influence on cognition. Psychological Review, 106, 529.CrossRefGoogle Scholar
  6. Baas, M., De Dreu, C. K., & Nijstad, B. A. (2008). A meta-analysis of 25 years of mood-creativity research: Hedonic tone, activation, or regulatory focus? Psychological Bulletin, 134, 779.CrossRefGoogle Scholar
  7. Banfield, R., Lombardo, C. T., & Wax, T. (2015). Design sprint: A practical guidebook for building great digital products. Newton, MA: O’Reilly Media.Google Scholar
  8. Barrick, M. R., & Mount, M. K. (1991). The big five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44, 1–26.CrossRefGoogle Scholar
  9. Bell, R., & Lumsden, J. (1980). Test length and validity. Applied Psychological Measurement, 4, 165–170.CrossRefGoogle Scholar
  10. Bond, M. H., Nakazato, H., & Shiraishi, D. (1975). Universality and distinctiveness in dimensions of Japanese person perception. Journal of Cross-Cultural Psychology, 6, 346–357.CrossRefGoogle Scholar
  11. Bowers, M. T., Green, B. C., Hemme, F., & Chalip, L. (2014). Assessing the relationship between youth sport participation settings and creativity in adulthood. Creativity Research Journal, 26, 314–327.CrossRefGoogle Scholar
  12. Budner, P., Eirich, J., & Gloor, P. A. (2017). “Making you happy makes me happy” - measuring individual mood with smartwatches. ArXiv171106134 Cs.Google Scholar
  13. Clapham, M. M. (2001). The effects of affect manipulation and information exposure on divergent thinking. Creativity Research Journal, 13, 335–350.CrossRefGoogle Scholar
  14. Conley, J. J. (1985). Longitudinal stability of personality traits: A multitrait–multimethod–multioccasion analysis. Journal of Personality and Social Psychology, 49, 1266.CrossRefGoogle Scholar
  15. Costa, P. T., & McCrae, R. R. (1989). NEO five-factor inventory (NEO-FFI). Odessa FL Psychol. Assess. Resour.Google Scholar
  16. Daggett, M., O’Brien, K., Hurley, M., & Hannon, D. (2017). Predicting team performance through human behavioral sensing and quantitative workflow instrumentation. In Advances in human factors and system interactions (pp. 245–258). Heidelberg: Springer.CrossRefGoogle Scholar
  17. Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual Review of Psychology, 41, 417–440.CrossRefGoogle Scholar
  18. Digman, J. M., & Inouye, J. (1986). Further specification of the five robust factors of personality. Journal of Personality and Social Psychology, 50, 116.CrossRefGoogle Scholar
  19. Gaskin, J., Jenkins, J., Meservy, T., Steffen, J., & Payne, K. (2017). Using wearable devices for non-invasive, inexpensive physiological data collection. In Hawaii international conference on system sciences 2017 HICSS-50.Google Scholar
  20. Glover, J. A., Ronning, R. R., & Reynolds, C. (2013). Handbook of creativity. Cham: Springer.Google Scholar
  21. Göritz, A. S., & Moser, K. (2003). Mood and flexibility in categorization: A conceptual replication. Perceptual and Motor Skills, 97, 107–119.CrossRefGoogle Scholar
  22. Hills, P., & Argyle, M. (2002). The Oxford happiness questionnaire: A compact scale for the measurement of psychological well-being. Personality and Individual Differences, 33, 1073–1082.CrossRefGoogle Scholar
  23. Isen, A. M., & Baron, R. A. (1991). Positive affect as a factor in organizational-behavior. Research in Organizational Behavior, 13, 1–53.Google Scholar
  24. Jausovec, N., & Bakracevic, K. (1995). What can heart rate tell us about the creative process? Creativity Research Journal, 8, 11–24.CrossRefGoogle Scholar
  25. Knez, I. (1995). Effects of indoor lighting on mood and cognition. Journal of Environmental Psychology, 15, 39–51.CrossRefGoogle Scholar
  26. Léger, P.-M., Davis, F. D., Cronan, T. P., & Perret, J. (2014). Neurophysiological correlates of cognitive absorption in an enactive training context. Computers in Human Behavior, 34, 273–283.CrossRefGoogle Scholar
  27. Lyubomirsky, S., King, L., & Diener, E. (2005). The benefits of frequent positive affect: Does happiness lead to success? Worcester, MA: American Psychological Association.Google Scholar
  28. McCrae, R. R., & Costa, P. T. (2004). A contemplated revision of the NEO five-factor inventory. Personality and Individual Differences, 36, 587–596.CrossRefGoogle Scholar
  29. Mikulincer, M., Kedem, P., & Paz, D. (1990). Anxiety and categorization—1. The structure and boundaries of mental categories. Personality and Individual Differences, 11, 805–814.CrossRefGoogle Scholar
  30. Minnery, B. S., & Fine, M. S. (2009). FEATURE neuroscience and the future of human-computer interaction. Interactions, 16, 70–75.CrossRefGoogle Scholar
  31. Mumford, M. D. (2003). Where have we been, where are we going? Taking stock in creativity research. Creativity Research Journal, 15, 107–120.CrossRefGoogle Scholar
  32. Norman, W. T. (1963). Toward an adequate taxonomy of personality attributes: Replicated factor structure in peer nomination personality ratings. Journal of Abnormal and Social Psychology, 66, 574.CrossRefGoogle Scholar
  33. Pentland, A. (2010). Honest signals: How they shape our world. Cambridge, MA: MIT press.Google Scholar
  34. Posner, J., Russell, J. A., & Peterson, B. S. (2005). The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Development and Psychopathology, 17, 715–734. CrossRefGoogle Scholar
  35. Robins, R. W., Fraley, R. C., Roberts, B. W., & Trzesniewski, K. H. (2001). A longitudinal study of personality change in young adulthood. Journal of Personality, 69, 617–640.CrossRefGoogle Scholar
  36. Russell, J. A., & Pratt, G. (1980). A description of the affective quality attributed to environments. Journal of Personality and Social Psychology, 38, 311.CrossRefGoogle Scholar
  37. Somech, A. (2006). The effects of leadership style and team process on performance and innovation in functionally heterogeneous teams. Journal of Management, 32, 132–157.CrossRefGoogle Scholar
  38. Sternberg, R. J. (1999). Handbook of creativity. Cambridge: Cambridge University Press.Google Scholar
  39. Stockham, N. (2016). Opening the black box: Publishing Pebble’s activity-tracking algorithms. Medium.Google Scholar
  40. Sung, S. Y., & Choi, J. N. (2009). Do big five personality factors affect individual creativity? The moderating role of extrinsic motivation. Social Behavior and Personality: An International Journal, 37, 941–956.CrossRefGoogle Scholar
  41. SuzanneK. Vosburg, G. K. (1997). ‘Paradoxical’ mood effects on creative problem-solving. Cognition & Emotion, 11, 151–170.CrossRefGoogle Scholar
  42. Thayer, J. F., Ahs, F., Fredrikson, M., Sollers, J. J., & Wager, T. D. (2012). A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience and Biobehavioral Reviews, 36, 747–756.CrossRefGoogle Scholar
  43. Verhaeghen, P., Joorman, J., & Khan, R. (2005). Why we sing the blues: The relation between self-reflective rumination, mood, and creativity. Emotion, 5, 226.CrossRefGoogle Scholar
  44. Vosburg, S. K. (1998). The effects of positive and negative mood on divergent-thinking performance. Creativity Research Journal, 11, 165–172.CrossRefGoogle Scholar
  45. Wagner, J., Kim, J., & André, E. (2005). From physiological signals to emotions: Implementing and comparing selected methods for feature extraction and classification. In IEEE international conference on Multimedia and expo, 2005, ICME 2005 (pp. 940–943). IEEE.Google Scholar
  46. Yamamoto, K. (1963). Relationships between creative thinking abilities of teachers and achievement and adjustment of pupils. The Journal of Experimental Education, 32, 3–25.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marius Stein
    • 1
  • Peter A. Gloor
    • 2
  • Daniel Oster
    • 1
  1. 1.University of CologneCologneGermany
  2. 2.MIT Center for Collective IntelligenceCambridgeUSA

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