Abstract
Any effort to improve the efficiency of the physical rehabilitation processes is fundamental to ensure the sustainability of healthcare services. This efficiency depends greatly on the patient’s adherence to the rehabilitation treatments. Information and communication technologies can help in these issues offering solutions that aim to monitor the patients’ rehabilitation exercises performance allowing the existence of domiciliary rehabilitation scenarios. We have developed a solution of this kind, which aims to be as simple and low-cost as possible in the way of how recognizes and evaluates patient’s movements. In this work we show a comparison between the use of a multilayer perceptron and a distance between patterns measuring algorithm for patients’ motion recognition.
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References
World Health Organization: World report on disability. Technical report, World Health Organization, Geneva (2011)
Sabaté, E.: Adherence to long-term therapies: evidence for action. Technical report, World Health Organization, Geneva (2003)
Pisters, M.F., Veenhof, C., van Meeteren, N.L.U., Ostelo, R.W., de Bakker, D.H., Schellevis, F.G., Dekker, J.: Long-term effectiveness of exercise therapy in patients with osteoarthritis of the hip or knee: a systematic review. Arthritis and Rheumatism 57(7), 1245–1253 (2007)
Belza, B., Topolski, T., Kinne, S., Patrick, D.L., Ramsey, S.D.: Does adherence make a difference? Results from a community-based aquatic exercise program. Nursing Research 51(5), 285–291 (2002)
Kolt, G.S., Brewer, B.W., Pizzari, T., Schoo, A.M., Garrett, N.: The Sport Injury Rehabilitation Adherence Scale: a reliable scale for use in clinical physiotherapy. Physiotherapy 93(1), 17–22 (2007)
Jack, K., McLean, S.M., Moffett, J.K., Gardiner, E.: Barriers to treatment adherence in physiotherapy outpatient clinics: a systematic review. Manual Therapy 15(3), 220–228 (2010)
Milne, M.I., Hall, C.R., Forwell, L.: Self-Efficacy, imagery use, and adherence to rehabilitation by injured athletes. Journal os Sport Rehabilitation 14(2), 150–167 (2005)
Sniehotta, F.F., Scholz, U., Schwarzer, R.: Bridging the intention-behaviour gap: Planning, self-efficacy, and action control in the adoption and maintenance of physical exercise. Psychology & Health 20(2), 143–160 (2005)
da Silva Cameirão, M., Bermúdez i Badia, S., Duarte, E., Verschure, P.F.M.J.: Virtual reality based rehabilitation speeds up functional recovery of the upper extremities after stroke: a randomized controlled pilot study in the acute phase of stroke using the rehabilitation gaming system. Restorative Neurology and Neuroscience 29(5), 287–298 (2011)
Henderson, A., Korner-Bitensky, N., Levin, M.: Virtual reality in stroke rehabilitation: a systematic review of its effectiveness for upper limb motor recovery. Topics in Stroke Rehabilitation 14(2), 52–61 (2007)
Guberek, R., Schneiberg, S., McKinley, P., Cosentino, F., Levin, M.F., Sveistrup, H.: Virtual reality as adjunctive therapy for upper limb rehabilitation in cerebral palsy. In: 2009 Virtual Rehabilitation International Conference, pp. 219–219. IEEE (June 2009)
Zhou, H., Hu, H.: Human motion tracking for rehabilitation-A survey. Biomedical Signal Processing and Control 3(1), 1–18 (2008)
Tao, Y., Hu, H., Member, S.: A Novel Sensing and Data Fusion System for 3-D Arm Motion Tracking in Telerehabilitation. IEEE Transactions on Instrumentation and Measurement 57(5), 1029–1040 (2008)
Zhang, S., Hu, H., Zhou, H.: An interactive Internet-based system for tracking upper limb motion in home-based rehabilitation. Medical & Biological Engineering & Computing 46(3), 241–249 (2008)
Attygalle, S., Duff, M., Rikakis, T.: Low-cost, at-home assessment system with Wii Remote based motion capture. In: 2008 Virtual Rehabilitation, pp. 168–174. IEEE (August 2008)
Mannini, A., Sabatini, A.M.: Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers. Sensors 10(2), 1154–1175 (2010)
Khan, A.M., Lee, Y.K., Lee, S., Kim, T.S.: Accelerometer’s position independent physical activity recognition system for long-term activity monitoring in the elderly. Medical & Biological Engineering & Computing 48(12), 1271–1279 (2010)
Schönauer, C., Pintaric, T., Kaufmann, H.: Full body interaction for serious games in motor rehabilitation. In: Proceedings of the 2nd Augmented Human International Conference on - AH 2011, pp. 1–8. ACM Press, New York (2011)
Lee, J.C.: Hacking the Nintendo Wii Remote. IEEE Pervasive Computing 7(3), 39–45 (2008)
Schlömer, T., Poppinga, B., Henze, N., Boll, S.: Gesture recognition with a Wii controller. In: Proceedings of the 2nd International Conference on Tangible and Embedded Interaction - TEI 2008, p. 11. ACM Press, New York (2008)
Leong, T.S., Lai, J., Panza, J., Pong, P., Hong, J.: Wii Want to Write: An Accelerometer Based Gesture Recognition System. In: International Conference on Recent and Emerging Advanced Technologies in Engineering, pp. 4–7 (2009)
Kratz, S., Rohs, M., Laboratories, D.T.: A $3 gesture recognizer: simple gesture recognition for devices equipped with 3D acceleration sensors. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, Hong Kong, pp. 341–344 (February 2010)
Loureiro, R., Valentine, D., Lamperd, B., Collin, C., Harwin, W.: Gaming and Social Interactions in the Rehabilitation of Brain Injuries: A Pilot Study with the Nintendo Wii Console. In: Langdon, P.M., Clarkson, P.J., Robinson, P. (eds.) Designing Inclusive Interactions, pp. 219–228. Springer, London (2010)
Fung, V., Ho, A., Shaffer, J., Chung, E., Gomez, M.: Use of Nintendo Wii FitTM in the rehabilitation of outpatients following total knee replacement: a preliminary randomised controlled trial. Physiotherapy 98(3), 183–188 (2012)
Raso, I.: M-Physio: Personalized accelerometer-based physical rehabilitation platform. In: UBICOMM 2010: The Fourth International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, pp. 416–421 (2010)
Hoffman, M., Varcholik, P., LaViola, J.J.: Breaking the status quo: Improving 3D gesture recognition with spatially convenient input devices. In: 2010 IEEE Virtual Reality Conference (VR), pp. 59–66. IEEE (March 2010)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing 26(1), 43–49 (1978)
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Marin-Alonso, O., Ruiz-Fernández, D., Soriano, A., Garcia-Perez, J.D. (2013). Use of Multilayer Perceptron vs. Distance Measurement Methods for Classification of Exercises in Telerehabilitation. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds) Natural and Artificial Models in Computation and Biology. IWINAC 2013. Lecture Notes in Computer Science, vol 7930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38637-4_18
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DOI: https://doi.org/10.1007/978-3-642-38637-4_18
Publisher Name: Springer, Berlin, Heidelberg
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