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#GamesUR Conference: From Body Signals to Brainy Player Insights


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Games User Researchers are often sceptical when it comes to using brain and body sensors, but as the cost of sensor technologies continues to drop, it is time to consider the potential insights that we might gain from using these signals in our work. In this talk, I will briefly introduce the most common physiological measures that are used in Games User Research, and discuss the challenges in obtaining a clean signal and usable data from different low-cost devices. Additionally, I will make recommendations for signal cleaning procedures and briefly talk about the analysis made possible with different physiological sensors. I will also demonstrate the conclusions that may be inferred from some of these data when compared to other Games User Research methods, such as behavioural observation. Lastly, I will introduce some of my own visualization methods for quickly comprehending the meaning of physiological sensor data.

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#GamesUR Conference: From Body Signals to Brainy Player Insights

  1. 1. From Body Signals to Brainy Player Insights Lennart Nacke, Ph.D. Associate Professor CC by Lennart Nacke/, @acagamic, #gamesUR,
  2. 2. About me • Associate Professor • Director of the HCI Games Group at the University of Waterloo
  3. 3. CC by Liz Henry/Flickr Body Signals
  4. 4. Electrodermal Activity • EDA • Primary index of arousal • Increased sweat gland activity • Sympathetic nervous system
  5. 5. Facial Electromyography • EMG • Muscle contraction result in electrical activity • Primary index of hedonic valence Brow muscle (negative valence) Periocular muscle (high arousal, positive valence) Cheek muscle (positive valence)
  6. 6. C3 CMS DRLCz C4 F3 F4 FC5 FC1 FC2 FC6 Fz Pz Oz P4 AF4 Fp2 PO4 O2O1 CP2 F8 T8T7 P8 CP6 PO3 P3 P7 CP1 CP5 AF3 F7 Fp1 Chatrian, G. E., Lettich, E., & Nelson, P. L. (1988). Modified nomenclature for the "10%" electrode system. Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society, 5(2), 183-186. Frontal (F) Parietal (P) Occipital (O) Temporal (T) Central (C) Odd Numbers Even Numbers Electroencephalography
  7. 7. Psychophysiological Inference • Body and Mind Relations • One-to-one • One-to-many • Many-to-one • Many-to-many • Null relations • Measure operation and activity of • Muscles • Nerve cells • Glands
  8. 8. Raw Physiological Signal (e.g., EMG) 0 1 2 3 4 5 6 7 x 10 4 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 11
  9. 9. Signal Processing 1. Rectify signal (RMS) 2. Filter (bandpass) 3. Smooth (average window) 4. If EEG → Fast-Fourier Transformation
  10. 10. Better EMG Signals
  11. 11. Normalized Skin Conductance  
  12. 12. CC by Nefci/Flickr
  13. 13. Biometric Storyboards • A single User Experience (UX) graph • Intended UX • Actual UX • Comparison study (N=24) of different player testing approaches Pejman Mirza-Babaei, Lennart E. Nacke, John Gregory, Nick Collins, and Geraldine Fitzpatrick. 2013. How does it play better?: exploring user testing and biometric storyboards in games user research. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '13).ACM, NewYork, NY, USA, 1499-1508. DOI=10.1145/2470654.2466200
  14. 14. Repidly • Online tool • Rapid evaluation • Annotate videos and physiological signals Bachelor’sThesis project from Mike Schaekermann
  15. 15. Repidly • Easy to associate game events with physiological events • Rapid annotation Bachelor’sThesis project from Mike Schaekermann
  16. 16. Repidly • Level of detail changes • Aggregation done smoothly Bachelor’sThesis project from Mike Schaekermann
  17. 17. Player Data Visualization • Mixed visualization of qualitative and quantitative player data overlaid on top of a level • The overall hue of the paths conveys areas with high (B and C) and low (A) player arousal Pejman Mirza-Babaei, Günter Wallner, Graham McAllister, and Lennart E. Nacke. 2014. Unified visualization of quantitative and qualitative playtesting data. In CHI '14 Extended Abstracts on Human Factors in Computing Systems (CHI EA '14).ACM, NewYork, NY, USA, 1363-1368. DOI=10.1145/2559206.2581224
  18. 18. Takeaways • Physiological data is complex • Explore visual options for rapid insights • Online tools allow collaboration of GURs CC by David D/Flickr
  19. 19. Thank you Contact me @acagamic (Twitter)