Clustering of Emotional States under Different Task Difficulty Levels for the Robot-assisted Rehabilitation system-RehabRoby

Yigit Can Aypar, Yunus Palaska, Ramazan Gokay, Engin Masazade, Duygun Erol Barkana, Nilanjan Sarkar

Abstract

In this paper, we study an unsupervised learning problem where the aim is to cluster the emotional state (excitedness, boredom, or stress) using the biofeedback sensor data while subjects perform tasks under different difficulty levels on the robot assisted rehabilitation system-RehabRoby. The dimension of the training vectors has been reduced by using the Principal Component Analysis (PCA) algorithm after collecting the biofeedback sensor measurements from different subjects under different task difficulty levels to better visualize the sensor data. The reduced dimension vectors are fed into a K-means clustering algorithm. Numerical results have been given to demonstrate that for each training vector, the emotional state decided by the clustering algorithm is consistent with the subjects declaration of his/her emotional state obtained via surveys after performing the task.

References

  1. Balasubramanian, S., Wei, R., Perez, M., Shepard, B., Koeneman, E., Koeneman, J., and He, J. (2008). Rupert: an exoskeleton robot for assisting rehabilitation of arm functions. In Virtual Rehabilitation, 2008, pages 163-167. IEEE.
  2. Bishop, C. M. (2006). Pattern Recognition and Machine Learning, volume 1. Springer New York.
  3. Bradley, M. M. and Lang, P. J. (1994). Measuring emotion: the self-assessment manikin and the semantic differential. Journal of behavior therapy and experimental psychiatry, 25(1):49-59.
  4. Caldwell, D. G., Tsagarakis, N. G., Kousidou, S., Costa, N., and Sarakoglou, I. (2007). ” soft” exoskeletons for upper and lower body rehabilitationdesign, control and testing. International Journal of Humanoid Robotics, 4(03):549-573.
  5. Filipovic, S. R. and Andreassi, J. L. (2001). Psychophysiology: Human behavior and physiological response. Journal of Psychophysiology, 15(3):210-212.
  6. Guadagnoli, M. A. and Lee, T. D. (2004). Challenge point: a framework for conceptualizing the effects of various practice conditions in motor learning. Journal of motor behavior, 36(2):212-224.
  7. Gunes, H., Schuller, B., Pantic, M., and Cowie, R. (2011). Emotion representation, analysis and synthesis in continuous space: A survey. In IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011),, pages 827-834. IEEE.
  8. Kandemir, M. (2013). Learning Mental States from Biosignals. PhD thesis, Aalto University School of Science.
  9. Kiguchi, K., Rahman, M. H., Sasaki, M., and Teramoto, K. (2008). Development of a 3dof mobile exoskeleton robot for human upper-limb motion assist. Robotics and Autonomous Systems, 56(8):678-691.
  10. Koenig, A., Novak, D., Omlin, X., Pulfer, M., Perreault, E., Zimmerli, L., Mihelj, M., and Riener, R. (2011a). Real-time closed-loop control of cognitive load in neurological patients during robot-assisted gait training. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(4):453-464.
  11. Koenig, A., Omlin, X., Zimmerli, L., Sapa, M., Krewer, C., Bolliger, M., Müller, F., and Riener, R. (2011b). Psychological state estimation from physiological recordings during robot-assisted gait rehabilitation. Journal of Rehabilitation Research & Development, 48(4).
  12. Krebs, H. I., Ferraro, M., Buerger, S. P., Newbery, M. J., Makiyama, A., Sandmann, M., Lynch, D., Volpe, B. T., and Hogan, N. (2004). Rehabilitation robotics: pilot trial of a spatial extension for mit-manus. Journal of NeuroEngineering and Rehabilitation, 1(1):5.
  13. Loureiro, R., Amirabdollahian, F., Topping, M., Driessen, B., and Harwin, W. (2003). Upper limb robot mediated stroke therapygentle/s approach. Autonomous Robots, 15(1):35-51.
  14. Lum, P. S., Burgar, C. G., Van der Loos, M., Shor, P. C., Majmundar, M., and Yap, R. (2006). Mime robotic device for upper-limb neurorehabilitation in subacute stroke subjects: A follow-up study. Journal of rehabilitation research & development, 43(5).
  15. Maclean, N. and Pound, P. (2000). A critical review of the concept of patient motivation in the literature on physical rehabilitation. Soc Sci Med, 50(4):495-506.
  16. Mandryk, R. L. and Atkins, M. S. (2007). A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. International Journal of Human-Computer Studies, 65(4):329-347.
  17. Nef, T., Guidali, M., and Riener, R. (2009). Armin III-arm therapy exoskeleton with an ergonomic shoulder actuation. Applied Bionics and Biomechanics, 6(2):127- 142.
  18. Ng, A. (2014). Machine Learning Lecture Notes. http://coursera.org/ml.
  19. Novak, D., Mihelj, M., and Munih, M. (2012). A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing. Interacting with Computers, 24(3):154- 172.
  20. Ozkul, F. and Barkana, D. E. (2013). Upper-extremity rehabilitation robot rehabroby: Methodology, design, usability and validation. International Journal of Advanced Robotic Systems.
  21. Ozkul, F., Barkana, D. E., Demirbas, S. B., and Inal, S. (2012). Evaluation of elbow joint proprioception with rehabroby: a pilot study. Acta Orthop Traumatol Turc, 46(5):332-338.
  22. Perry, J. C., Rosen, J., and Burns, S. (2007). Upper-limb powered exoskeleton design. IEEE/ASME Transactions on Mechatronics, 12(4):408-417.
  23. Rahman, M. H., Saad, M., Kenne, J.-P., and Archambault, P. S. (2009). Modeling and control of a 7dof exoskeleton robot for arm movements. In IEEE International Conference on Robotics and Biomimetics (ROBIO), pages 245-250. IEEE.
  24. Rahman, T., Sample, W., Jayakumar, S., King, M. M., Wee, J. Y., Seliktar, R., Alexander, M., Scavina, M., and Clark, A. (2006). Passive exoskeletons for assisting limb movement. Journal of rehabilitation research and development, 43(5):583.
  25. Rani, P., Sarkar, N., and Adams, J. (2007). Anxietybased affective communication for implicit humanmachine interaction. Advanced Engineering Informatics, 21(3):323-334.
  26. Rani, P., Sarkar, N., and Smith, C. A. (2003a). Affectsensitive human-robot cooperation-theory and experiments. In Robotics and Automation, 2003. Proceedings. ICRA'03. IEEE International Conference on, volume 2, pages 2382-2387. IEEE.
  27. Rani, P., Sarkar, N., Smith, C. A., and Adams, J. A. (2003b). Affective communication for implicit human-machine interaction. In Systems, Man and Cybernetics, 2003. IEEE International Conference on, volume 5, pages 4896-4903. IEEE.
  28. Ren, Y., Park, H.-S., and Zhang, L.-Q. (2009). Developing a whole-arm exoskeleton robot with hand opening and closing mechanism for upper limb stroke rehabilitation. In IEEE International Conference on Rehabilitation Robotics, 2009. ICORR 2009., pages 761-765. IEEE.
  29. Russell, J. A. (1989). Measures of emotion. In R. Plutchik and H. Kellerman (Eds.), Emotion: Theory, research, and experience, 4:83-111.
  30. Vertechy, R., Frisoli, A., Dettori, A., Solazzi, M., and Bergamasco, M. (2009). Development of a new exoskeleton for upper limb rehabilitation. In IEEE International Conference on Rehabilitation Robotics, ICORR 2009., pages 188-193. IEEE.
Download


Paper Citation


in Harvard Style

Aypar Y., Palaska Y., Gokay R., Masazade E., Erol Barkana D. and Sarkar N. (2014). Clustering of Emotional States under Different Task Difficulty Levels for the Robot-assisted Rehabilitation system-RehabRoby . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-039-0, pages 34-41. DOI: 10.5220/0005052600340041


in Bibtex Style

@conference{icinco14,
author={Yigit Can Aypar and Yunus Palaska and Ramazan Gokay and Engin Masazade and Duygun Erol Barkana and Nilanjan Sarkar},
title={Clustering of Emotional States under Different Task Difficulty Levels for the Robot-assisted Rehabilitation system-RehabRoby},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2014},
pages={34-41},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005052600340041},
isbn={978-989-758-039-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Clustering of Emotional States under Different Task Difficulty Levels for the Robot-assisted Rehabilitation system-RehabRoby
SN - 978-989-758-039-0
AU - Aypar Y.
AU - Palaska Y.
AU - Gokay R.
AU - Masazade E.
AU - Erol Barkana D.
AU - Sarkar N.
PY - 2014
SP - 34
EP - 41
DO - 10.5220/0005052600340041