Abstract

Rehabilitation is defined as a set of measures that assist individuals who experience, or are likely to experience, disability to achieve and maintain optimal functioning in interaction with their environments. Ambient Intelligence (AmI) refers to a digital environment that proactively, but sensibly, supports people in their daily lives. AmI is useful in order to rehabilitate patients. In this paper, a survey of AmI for rehabilitation is presented.

References

• World Health Organization, Chapter 4- Rehabilitation, https://www.who.int/disabilities/world_report/2011/chapter4.pdf?ua=1.
• Cancer.Net, https://www.cancer.net/survivorship/follow-care-after-cancer-treatment/rehabilitation.
• Fariba Sadri, “Ambient Intelligence: A Survey”, ACM Computing Surveys (CSUR), 2011.
• A Gaggioli, “Optimal Experince in Ambient Intelligence”, Ambient Intelligence 35 G. Riva, F. Vatalaro, F. Davide, M. Alcañiz (Eds.), pp. 35-43, 2005.
• G. Acampora, D.J. Cook, P.Rashidi, A.V. Vasilakos, “A Survey on Ambient Intelligence in Health Care”, Proc. IEEE Inst Electr Electron Eng, Vol. 101, No.12, pp. 2470-2494, 2013
• G. Riva, “Ambient Intelligence in Health Care”, CyberPsychology and Behavior, Vol.6, No.3, pp. 295-300, 2003.
• Ducatel, K., Bogdanowicz, M., Scapolo, F., et al. “Scenarios for ambient intelligence in 2010” (ISTAG).
• T. Kleinberger, M. Becker, E. Ras, A. Holzinger, P. Müller, “Ambient Intelligence in Assisted Living: Enable Elderly People to Handle Future Interfaces”, Springer-Verlag Berlin Heidelberg , pp. 103-112, 2007.
• P. L. Emiliani, C. Stephanidis, “Universal access to ambient intelligence environments: Opportunities and challenges for people with disabilities”, IBM Systems Journals, Vol.44, No.3, pp. 605-619, 2005.
• J.Nehmer, A. Karshmer, M. Becker, R. Lamm, “Living Assistance Systems - An Ambient Intelligence Approach –“, ICSE, Shanghai, May 24-26, 2006.
• S. Patel, H. Park, P. Bonato, L. Chan, and M. Rodgers, ‘‘A review of wearable sensors and systems with application in rehabilitation,’’ J. NeuroEng. Rehabil., vol. 9, no. 1, 2012.
• B. P. Jarochowski, S. Shin, D. Ryu, and H. Kim, ‘‘Ubiquitous rehabilitation center: An implementation of a wireless sensor network based rehabilitation management system,’’ in Proc. Int. Conf. Technol., Washington, DC, USA, 2007, Conv. Inf. pp. 2349–2358.
• E. Piotrowicz, A. Jasionowska, M. Banaszak-Bednarczyk, J. Gwilkowska and R. Piotrowicz, ‘‘ECG telemonitoring during home-based cardiac rehabilitation in heart failure patients,’’ J. Telemed. Telecare, vol. 18, no. 4, pp. 193–197, 2012.
• A. Helmer, B. Song, W. Ludwig, M. Schulze, M. Eichelberg, A. Hein, U. Tegtbur, R. Kayser, R. Haux, and M. Marschollek, ‘‘A sensor-enhanced health information system to support automatically controlled exercise training of COPD patients,’’ in Proc. 4th Int. Conf. Perv. Comput. Technol. Healthcare, Mar. 2010.
• J. Winters and Y. Wang, ‘‘Wearable sensors and telerehabilitation,’’ IEEE Eng. Med. Biol. Mag., vol. 22, no. 3, pp. 56–65, May–Jun. 2003.
• C. Poon, Y.-T. Zhang, and S.-D. Bao, ‘‘A novel biometrics method to secure wireless body area sensor networks for telemedicine and m-health,’’ IEEE ommun. Mag., vol. 44, no. 4, pp. 73–81, Apr. 2006.
• C.Roda, A. Rodriguez, V. Lopez-Jaquero, E. Navarro, P.Gonzalez, “A Multi-agent System for Acquired Brain Injuri rehabilitation in Ambient Intelligence Environment”, Nurocomputing, Vol. 231, pp.11-18, 2017.
• C.Roda, A. Rodriguez, V. Lopez-Jaquero, E. Navarro, P.Gonzalez, “A Multi-agent System in Ambient intelligence for the Physical Rehabilitation of Older People”, Trends in Practical Applications of Agents, Multi-Agent Systems and Sustainability. Advances in Intelligent Systems and Computing, vol 372. Springer, pp. 113-123, 2015.
• M. Korman, P. L. Weiss & R. Kizony (2016) Living Labs: overview of ecological approaches for health promotion and rehabilitation, Disability and Rehabilitation, Vol. 38 No.7, pp. 613-619, 2016.
• M. Oliver, P. González, F. Montero , J.P. Molina, A. Fernández-Caballero, “Smart Computer-Assisted Cognitive Rehabilitation for the Ageing Population.”, In: Lindgren H. et al. (eds) Ambient Intelligence- Software and Applications – 7th International Symposium on Ambient Intelligence (ISAmI 2016). ISAmI 2016. Advances in Intelligent Systems and Computing, vol 476. Springer, pp. 197-205, 2016.
• D. Monekosso, F. Florez-Revuelta and P. Remagnino, “Ambient Assisted Living”, IEEE, pp.1541-1672, 2015
• C. Ramírez-Fernández et al., “GoodVybesConnect: A Real-Time Haptic Enhanced Tele-Rehabilitation System for Massage Therapy”. In: García C., Caballero-Gil P., Burmester M., Quesada-Arencibia A. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2016. Lecture Notes in Computer Science, vol 10069. Springer, pp. 487-496, 2016.
• P. Saini, R. Willmann, R. Huurneman, G. Lanfermann, J. te Vrugt, S. Winter, and J. Buurke, ‘‘Philips stroke rehabilitation exerciser: A usability test,’’ in Proc. IASTED Int. Conf. Telehealth/Assistive Technol., Anaheim, CA, USA, 2008, pp. 116–122.