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Adaptation in serious games for upper-limb rehabilitation: an approach to improve training outcomes

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Abstract

In this paper, we propose a game adaptation technique that seeks to improve the training outcomes of stroke patients during a therapeutic session. This technique involves the generation of customized game levels, which difficulty is dynamically adjusted to the patients’ abilities and performance. Our goal was to evaluate the effect of this adaptation strategy on the training outcomes of post-stroke patients during a therapeutic session. We hypothesized that a dynamic difficulty adaptation strategy would have a more positive effect on the training outcomes of patients than two control strategies, incremental difficulty adaptation and random difficulty adaptation. To test these strategies, we developed three versions of PRehab, a serious game for upper-limb rehabilitation. Seven stroke patients and three therapists participated in the experiment, and played all three versions of the game on a graphics tablet. The results of the experiment show that our dynamic adaptation technique increases movement amplitude during a therapeutic session. This finding may serve as a basis to improve patient recovery.

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References

  • Annett, M., Anderson, F., Goertzen, D., Halton, J., Ranson, Q., Bischof, W., Boulanger, P.: Using a multi-touch tabletop for upper extremity motor rehabilitation. In: Proceedings of the 21st Annual Conference of the Australian Computer–Human Interaction Special Interest Group, pp. 261–264. Melbourne (2009)

  • Avila-Sansores, S., Orihuela-Espina, F., Enrique-Sucar, L.: Patient Tailored Virtual Rehabilitation, Converging Clinical and Engineering Research on Neurorehabilitation. Springer, Berlin (2013)

    Google Scholar 

  • Beynon, M., Beynon, W.: Mediating intelligence through observation, dependency and agency in making construals of malaria. In: Intelligent Tutoring Systems Conference, pp. 664–665. Chania, Crete (2012)

  • Burke, J., McNeill, M., Charles, D., Morrow, P., Crosbie, J., McDonough, S.: Serious games for upper limb rehabilitation following stroke. In: Conference in Games and Virtual Worlds for Serious Applications, pp. 103–110. Coventry (2009)

  • Burke, J., McNeill, M., Charles, D., Morrow, P., Crosbie, J., McDonough, S.: Optimising engagement for stroke rehabilitation using serious games. J. Vis. Comput. 25, 1085–1099 (2009)

    Article  Google Scholar 

  • Brehm, J., Self, E.: The intensity of motivation. Ann. Rev. Psychol. 40, 109–131 (1989)

    Article  Google Scholar 

  • Cameirão, M.S., Badia, S.B., Oller, E.D.: Neurorehabilitation using the virtual reality based Rehabilitation Gaming System: methodology, design, psychometrics, usability and validation. J. Neuroeng. Rehabil. 7, 48 (2010)

    Article  Google Scholar 

  • Chittaro, L., Ranon, R., Carchietti, E., Zampa, A., Biasutti, E., De Marco, L.: A knowledge-based system to support emergency medical services for disabled patients. J. Artif. Intell. Med. 2, 176–180 (2009). (Elsevier)

    Article  Google Scholar 

  • Cirstea, M.C., Levin, M.F.: Improvement of arm movement patterns and endpoint control depends on type of feedback during practice in stroke survivors. J. Neurorehabil. Neural Repair 21, 398–411 (2007)

    Article  Google Scholar 

  • Cohen, J.: Statistical Power Analysis for the Behavioral Sciences. Routledge Academic, New York (1988)

    MATH  Google Scholar 

  • Conati, C., Maclaren, H.: Modeling user affect from causes and effects. User modeling and user-adapted interaction. J. Personal. Res. 5535, 4–15 (2009)

    Google Scholar 

  • Conati, C., Manske, M.: Evaluating adaptive feedback in an educational computer game. In: 9th International Conference on Intelligent Virtual Agents, pp. 146–158. Amsterdam (2009)

  • Czikszentmihalyi, M.: Flow: The Psychology of Optimal Experience. Lidové Noviny, Praha (1991)

    Google Scholar 

  • Dobkin, B.H.: Rehabilitation after stroke. N. Engl. J. Med. 352, 1677–1684 (2005)

    Article  Google Scholar 

  • Gouaïch, A., Hocine, N., Van Dokkum, L., Mottet, D.: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, pp. 5–12. Miami (2012)

  • Goude, D., Björk, S., Rydmark, M.: Game design in virtual reality systems for stroke rehabilitation. J. Stud. Health Technol. Inform. 125, 146–148 (2007)

    Google Scholar 

  • Hocine, N., Gouaich, A.: Therapeutic games difficulty adaptation: an approach based on player ability and motivation. In: Proceddings of 16th International Conference CGAME 2011, pp. 257–261 (2011)

  • Howcroft, J., Klejman, S., Fehlings, D., Wright, V., Zabjek, K., Andrysek, J., Biddiss, E.: Active video game play in children with cerebral palsy: potential for physical activity promotion and rehabilitation therapies. Arch. Phys. Med. Rehabil. 93, 1448–1456 (2012)

    Article  Google Scholar 

  • Hunicke, R., Chapman, V.: AI for dynamic difficulty adjustment in games. In: Challenges in Game Artificial Intelligence AAAI Workshop, pp. 91–96 (2004)

  • IJsselsteijn, W.A., De Kort, Y., Poels, K.: The Game Experience Questionnaire: Development of a self-report measure to assess the psychological impact of digital games. Manuscript (2013)

  • Khademi, M., Mousavi, H., Lopes, C.V., Dodakian, L., Cramer, S.C.: Haptic augmented reality to monitor human arm’s stiffness in rehabilitation. In: IEEE EMBS Conference on Biomedical Engineering and Sciences, pp. 892–895 (2012)

  • Kivikangas, J., Ekman, I., Chanel, G., Järvelä, S., Salminen, M., Cowley, B., Henttonen, P., Ravaja, N.: Review on psychophysiological methods in game research. J. Gaming Virtual Worlds 3, 181–199 (2010)

    Article  Google Scholar 

  • Kocsis, L., Szepesvari, C.: Bandit based Monte-Carlo planning. Mach. Learn. 2006, 282–293 (2006)

    MathSciNet  Google Scholar 

  • Kukla, A.: Foundations of an attributional theory of performance. Psychol. Rev. 9, 454–470 (1972)

    Article  Google Scholar 

  • Kwakkel, G., Wagenaar, R.C., Koelman, T.W., Lankhorst, G.J., Koetsier, J.C.: Effects of intensity of rehabilitation after stroke a research synthesis. J. Stroke 28, 722–728 (1997)

    Article  Google Scholar 

  • Left 4 Dead video game. http://www.l4d.com (2008)

  • Levin, F., Sveistrup, H., Subramanian, S.: Feedback and virtual environments for motor learning and rehabilitation. Schedae 1, 19–36 (2010)

    Google Scholar 

  • Magerko, B., Stensrud, B., Holt, L.: Bringing the schoolhouse inside the box-A tool for engaging, individualized training, pp. 23–29 (2006)

  • Markow, T.: Mobile music touch: using haptic stimulation for passive rehabilitation and learning. Doctoral dissertation, Georgia Institute of Technology (2012)

  • Max Payne video game. http://www.maxpayne.com (2001)

  • McCuaig, J., Gauthier, R.: Interoperability for ITS: An Ontology of Learning Style Models. Intelligent Tutoring Systems, Crete (2012)

    Google Scholar 

  • Mihelj, M., Novak, D., Milavec, M., Ziherl, J., Olenšek, A., Munih, M.: Virtual rehabilitation environment using principles of intrinsic motivation and game design. Presence 21, 1–15 (2012)

    Article  Google Scholar 

  • Murray, T., Arroyo, I.: Toward Measuring and Maintaining the Zone of Proximal Development in Adaptive Instructional Systems. Intelligent Tutoring Systems, Le Mans (2002)

    Google Scholar 

  • Natkin, S., Yan, C., Jumpertz, S., Market, B.: Difficulty scaling of game aI. In: International Conference on Digital Games Research Association, pp. 33–37. Tokyo (2007)

  • Nef, T., Riener, R.: Three-Dimensional Multi-Degree-of-Freedom Arm Therapy Robot (ARMin), Neurorehabilitation Technology. Springer, New York (2012)

    Google Scholar 

  • PCG, Wiki page for procedural content generation site. http://www.pcg.wikidot.com (2014)

  • Peirce, N., Conlan, O., Wade, V.: Adaptive educational games: providing non-invasive personalised learning experiences. In: Second IEEE International Conference on Digital Games and Intelligent Toys Based Education, pp. 28–35 (2008)

  • Pirovano, M., Mainetti, R., Baud-Bovy, G., Lanzi, P.L., Borghese, N.: Self-adaptive games for rehabilitation at home. In: IEEE Conference on Computational Intelligence and Games (CIG), pp. 179–186. Granada (2012)

  • PRehab, serious game for stroke rehabilitation, university of Montpellier, video of the game. http://www.youtube.com/watch?v=cfTv9Dtc5Ww

  • Pugnetti, L., Mendozzi, L., Attree, E.A., Barbieri, E., Brooks, B., Cazzullo, C., Motta, A., Rose, F.D., Psychol, C.: Probing memory and executive functions with virtual reality: past and present studies. Cyber Psychol. Behav. 1, 151–161 (1998)

    Article  Google Scholar 

  • Rabin, B., Burdea, G., Hundal, J., Roll, D., Damiani, F.: Integrative motor, emotive and cognitive therapy for elderly patients chronic post-stroke A feasibility study of the BrightArm rehabilitation system. In: International Conference on Virtual Rehabilitation, pp. 1–8 (2011)

  • Rani, P., Sarkar, N., Nilanjan, L., Liu, C.: Maintaining optimal challenge in computer games through real-time physiological feedback. In: Proceedings of the 11th International Conference on Human Computer Interaction, pp. 184–192. Las Vegas (2005)

  • Rodrigo, R., Fernández-Gajardo, R., Gutiérrez, R., Matamala, J.M., Carrasco, R., Miranda-Merchak, A., Feuerhake, W.: Oxidative stress and pathophysiology of ischemic stroke: novel therapeutic opportunities. J. Oxid. Stress Pathophysiol. Ischemic Stroke 28, 23–29 (2013)

    Google Scholar 

  • Rojas, D., Kapralos, B., Cristancho, S., Collins, K., Hogue, A., Conati, C., Dubrowski, A.: Developing effective serious games: the effect of background sound on visual fidelity perception with varying texture resolution. J. Stud. Health Technol. Inform. 173, 386–392 (2012)

    Google Scholar 

  • Sanders, T., Cairns, P., Paul : Time perception, immersion and music in videogames. In: Proceedings of the 24th BCS Interaction Specialist Group Conference, pp. 160–167. Swinton (2010)

  • Sherlock, K.: Plays, Acts and Scenes in Structure, Drama Writing. Grossmont College, El Cajon (2005)

    Google Scholar 

  • Spronck, P., Ponsen, M., Sprinkhuizen-Kuyper, I., Postma, E.: Adaptive game AI with dynamic scripting. J. Mach. Learn. 63, 23–29 (2006)

    Article  Google Scholar 

  • Spronck, P., Sprinkhuizen-Kuyper, I., Postma, E.: Difficulty scaling of game AI. In: Proceedings of the 5th International Conference on Intelligent Games and Simulation, pp. 33–37 (2004)

  • Tavener, S., Perez, D., Samothrakis, S., Colton, S.: A survey of Monte Carlo tree search methods. IEEE Trans. Comput. Intell. AI Games 4, 1–43 (2012)

    Article  Google Scholar 

  • Tijs, T., Brokken, D., IJsselsteijn, W.: Dynamic Game Balancing by Recognizing Affect, Fun and Games. Springer, Berlin (2008)

    Google Scholar 

  • Togelius, J., Yannakakis, G., Stanley, K., Browne, C.: Search-based procedural content generation applications of evolutionary computation. IEEE Trans. Comput. Intell. AI Games 3, 141–150 (2010)

    Google Scholar 

  • Varkuti, B., Guan, C., Pan, Y., Phua, K., Ang, K., Kuah, C., Chua, K., Ang, B., Birbaumer, N., Sitaram, R.: Resting state changes in functional connectivity correlate with movement recovery for BCI and robot-assisted upper-extremity training after stroke. J. Neurorehabil. Neural Repair 27, 53–62 (2013)

    Article  Google Scholar 

  • Vincent, O. R., Folorunso, O.: A descriptive algorithm for sobel image edge detection. In: Proceedings of Informing Science and IT Education Conference (InSITE), pp. 97–107 (2009)

  • Wertsch, J.: The zone of proximal development: some conceptual issues. New Dir. Child. Adolesc. Dev. 1984, 7–18 (1984)

    Article  Google Scholar 

  • World Health Organization (WHO): The Top Ten Causes of Death. World Health Organization, Geneva (2013)

    Google Scholar 

  • Yannakakis, G.N., Hallam, J.: Real-time adaptation of augmented-reality games for optimizing player satisfaction. In: IEEE Symposium of Computational Intelligence and Games, pp. 103–110 (2008)

  • Zook, A., Riedl, M.: A temporal data-driven player model for dynamic difficulty adjustment. In: The Eighth Annual AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, pp. 23–29 (2012)

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Hocine, N., Gouaïch, A., Cerri, S.A. et al. Adaptation in serious games for upper-limb rehabilitation: an approach to improve training outcomes. User Model User-Adap Inter 25, 65–98 (2015). https://doi.org/10.1007/s11257-015-9154-6

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