Identifying an Emotional State from Body Movements Using Genetic-Based Algorithms

  • Yann Maret
  • Daniel Oberson
  • Marina GavrilovaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)


Emotions may not only be perceived by humans, but could also be identified and recognized by a machine. Emotion recognition through pattern analysis can be used in expert systems, lie detectors, medical emergencies, as well as during rescue operations to quickly identify people in distress. This paper describes a system capable of recognizing emotions based on the arm movement. Features extracted from 3D skeleton using Kinect sensor are classified by five commonly used machine learning techniques: K nearest neighbors, SVM, Decision tree, Neural Network and Naive Bayes. A genetic algorithm is then invoked to find the best system parameters to achieve the higher recognition rate. The system achieved 98.96% average accuracy on the experimental dataset.


Kinect sensor Genetic algorithm Machine learning Biometric Activity recognition Image processing Risk assessment 



Authors would like to acknowledge NSERC, MITACS funding and Switzerland international exchange program. We also grateful to all members of the Biometric Technologies Laboratory, Department of Computer Science, University of Calgary.


  1. 1.
    Kozlowski, L.T., Cutting, J.E.: Recognizing the sex of a walker from a dynamic point-light display. Atten. Percept. Psychophy. 21(6), 575–580 (1977)CrossRefGoogle Scholar
  2. 2.
    Cutting, J.E.: Generation of synthetic male and female walkers through manipulation of a biomechanical invariant. Perception 7(4), 393–405 (1978)CrossRefGoogle Scholar
  3. 3.
    Hill, H., Pollick, F.E.: Exaggerating temporal differences enhances recognition of individuals from point light displays. Psychol. Sci. 11(3), 223–228 (2000)CrossRefGoogle Scholar
  4. 4.
    Munir, S., Arora, R., Hesling, C., Li, J., Francis, J., Shelton, C., Martin, C., Rowe, A., Berges, M.: Real-time fine grained occupancy estimation using depth sensors on ARM embedded platforms. In: 23rd IEEE RTETA Symposium (2017)Google Scholar
  5. 5.
    Gavrilova, M.L., Monwar, M.: Multimodal Biometrics and Intelligent Image Processing for Security Systems. IGI Global, Hershey (2013)CrossRefGoogle Scholar
  6. 6.
    Ahmed, F., Paul, P.P., Gavrilova, M.L.: DTW-based kernel and rank-level fusion for 3D gait recognition using Kinect. Vis. Comput. 31(6–8), 915–924 (2015)CrossRefGoogle Scholar
  7. 7.
    Pollick, F.E., Paterson, H.M., Bruderlin, A., Sanford, A.J.: Perceiving affect from arm movement. Cognition 82(2), B51–B61 (2001)CrossRefGoogle Scholar
  8. 8.
    Xia, L., Chen, C., Aggarwal, J.: View invariant human action recognition using histograms of 3D joints. In: IEEE Conference CVPR, pp. 20–27 (2012)Google Scholar
  9. 9.
    Yang, X., Tian, Y.: Effective 3D action recognition using eigenjoints. J. Vis. Commun. Image Represent. 25(1), 2–11 (2014)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Theodorakopoulos, I., Kastaniotis, D., Economou, G., Fotopoulos, S.: Pose-based human action recognition via sparse representation in dissimilarity space. J. Vis. Commun. Image Represent. 25(1), 12–23 (2014)CrossRefGoogle Scholar
  11. 11.
    Roether, C.L., Omlor, L., Christensen, A., Giese, M.A.: Critical features for the perception of emotion from gait. J. Vis. 9(6), 1–15 (2009)CrossRefGoogle Scholar
  12. 12.
    Saha, S., Datta, S., Konar, A., Janarthanan, R.: A study on emotion recognition from body gestures using Kinect sensor. In: Proceedings of Communications and Signal Processing (ICCSP), pp. 056–060 (2014)Google Scholar
  13. 13.
    Senecal, S., Cuel, L., Aristidou, A., Magnenat-Thalmann, N.: Continuous body emotion recognition system during theater performances. Comput. Animat. Virtual Worlds 27(3–4), 311–320 (2016)CrossRefGoogle Scholar
  14. 14.
    Wang, Y., Howard, N., Karcprzyk, J., Frieder, P., Sheu, P., Fiorini, R., Gavrilova, M.L., Patel, S., Peng, J., Widrow, B.: Cognitive informatics: towards cognitive machine learning and autonomous knowledge manipulation. Int. J. Cogn. Inf. Nat. Intell. (IJCINI) 12(1), 1–18 (2017)Google Scholar
  15. 15.
    Gavrilova, M., Wang, Y., Ahmed, F., Paul, P.P.: KINECT sensor gesture and activity recognition for consumer cognitive systems. IEEE Consum. Electron. Mag. Spec. Issue. Consum. Electron. 4, 88–96 (2017)Google Scholar
  16. 16.
    Melin, P., Castillo, O., Kacprzyk, J. (eds.): Nature-Inspired Design of Hybrid Intelligent Systems. SCI, vol. 667. Springer, Cham (2017). Scholar
  17. 17.
    Ball, A., Rye, D., Ramos, F., Velonaki, M.: Unsupervised clustering of people from ‘skeleton’ data. In: 2012 Conference on Human Robot Interaction, pp. 225–226 (2012)Google Scholar
  18. 18.
    Preis, J., Kessel, M., Linnhoff-Popien, C., Werner, M.: Gait recognition with Kinect. In: Workshop on Kinect in Pervasive Computing (2012)Google Scholar
  19. 19.
    Gabel, M., Gilad-Bachrach, R., Renshaw, E., Schuster, A.: Full body gait analysis with kinect. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1964–1967 (2012)Google Scholar
  20. 20.
    Sas, D., Saeed, K.: Comprehensive performance evaluation of various feature extraction methods for OCR purposes. In: Saeed, K., Homenda, W. (eds.) CISIM 2015. LNCS, vol. 9339, pp. 411–422. Springer, Cham (2015). Scholar
  21. 21.
    Markowska-Kaczmar, U., Kwasnicka, H., Szczepkowski, M.: Genetic algorithm as a tool for stock market modelling. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 450–459. Springer, Heidelberg (2008). Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of CalgaryCalgaryCanada
  2. 2.The School of Engineering and Architecture of FribourgFribourgSwitzerland

Personalised recommendations