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Mathematical Models Used in Intelligent Assistive Technologies: Response Surface Methodology in Software Tools Optimization for Medical Rehabilitation

  • Oana GemanEmail author
  • Octavian Postolache
  • Iuliana Chiuchisan
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 170)

Abstract

Assistive Technology (AT) refers to any commercially, modified, or customized item, equipment, or system that is used to enhance, maintain or improve the functional capabilities of people with disabilities. Assistive technologies should be viewed as a series of products and services through which people with different types of disabilities achieve their own goals by facilitating an independent life. The scope of support technologies extends to those products and services for three categories of people: people with disabilities, the elderly and people with chronic illnesses. Ambient Assistance Living (AAL) is a subcategory of environmental intelligence, which refers to the use of intelligent environmental techniques, processes and technologies to enable the elderly to live independently for as long as possible without intrusive behaviors. The Exergaming Platform presented in this chapter is a recovery application, designed for upper limb rehabilitation, that helps patients with locomotor disabilities and not only, transforming the unpleasant physical therapy into a fun game. The platform transforms the traditional games into video game-based exercises and drives patients to exercise correctly, while monitoring them. A RSM tool for Medical Rehabilitation based on an Exergaming Platform that uses a Microsoft Kinect Sensors, is presented in this chapter. The development of this application was made in cooperation between a research team from Institute de Telecommunication from Lisbon, Portugal and a research team from the University of Suceava, Romania. In order to find the score for a subject with a locomotor system leisure or neurodegenerative disorders, we used RSM Methodology and we optimized the exergame using statistical and mathematical. During the model developing, the data analysis has shown that the RSM can be a good candidate for optimization the application. The application has demonstrated that the Response Surface Methodology (RSM) is a useful instrument in the prediction of the patients variable scores.

Keywords

Assistive technologies Mathematical models Response surface method Exergaming Rehabilitation 

References

  1. 1.
    Akbari, S., Mahmood, S.M., Tan, I.M., Hematpour, H.: Comparison of neuro-fuzzy network and response surface methodology pertaining to the viscosity of polymer solutions. J. Pet. Explor. Prod. Technol., 8(3), 887–900 (2018), ISSN: 2190-0558Google Scholar
  2. 2.
    Active and Assisted Living Programme: ICT for ageing well. Retrieved May 10, 2018, from http://www.aal-europe.eu
  3. 3.
    Chiuchisan, I., Geman, O.: Trends in embedded systems for e-Health and biomedical applications. In: Proceedings of 2016 International Conference and Exposition on Electrical and Power Engineering (EPE), pp. 304–308 (2016). ISBN: 978-1-5090-6129-7Google Scholar
  4. 4.
    Chiuchisan, I., Geman, O., Prelipceanu, M., Costin, H.N.: Health Care System for Monitoring Older Adults in a “Green” Environment using Organic Photovoltaic Devices. Environmental Engineering & Management Journal (EEMJ), 15 (12), pp. 2595–2604 (2016).  https://doi.org/10.30638/eemj.2016.286CrossRefGoogle Scholar
  5. 5.
    Chiuchisan, I., Geman, O.: An approach of a decision support and home monitoring system for patients with neurological disorders using internet of things concepts. WSEAS Trans. Syst., Issue: Multi-Model. Complex Technol. Syst., 13, 460–469 (2014a). E-ISSN: 2224-2678Google Scholar
  6. 6.
    Chiuchisan, I., Costin, H.N., Geman, O.: Adopting the Internet of Things Technologies in Health Care Systems. Proceedings of 2014 International Conference and Exposition on Electrical and Power Engineering (EPE), Workshop on Electromagnetic Compatibility and Engineering in Medicine and Biology, pp. 532–535 (2014b). E-ISBN: 978-1-4799-5849-8Google Scholar
  7. 7.
    Chiuchisan, I., Geman, O., Chiuchisan, Iulian, Iuresi, A.C., Graur, A.: NeuroParkinScreen—a Health Care System for Neurological Disorders Screening and Rehabilitation. In: Proceedings of 2014 International Conference and Exposition on Electrical and Power Engineering (EPE), Workshop on Electromagnetic Compatibility and Engineering in Medicine and Biology, pp. 536–540 (2014c). E-ISBN: 978-1-4799-5849-8Google Scholar
  8. 8.
    Da Gama, A., Fallavollita, P., Teichrieb, V., Navab, N.: Motor rehabilitation using Kinect: a systematic review. Games Health J. 4(2), 123–135 (2015)CrossRefGoogle Scholar
  9. 9.
    Dittmar, A., Meffre, R., De Oliveira, F., Gehin, C., Delhomme, G.: Wearable Medical Devices Using Textile and Flexible Technologies for Ambulatory Monitoring, pp. 7161–7164. Engineering in Medicine and Biology Society, IEEE-EMBS (2005)Google Scholar
  10. 10.
    European Commission: Active Ageing Report 2017. Retrieved February 15, 2018, from http://ec.europa.eu/social
  11. 11.
    Geman, O., Postolache, A.P., Chiuchisan, I., Prelipceanu, M.: Jude Hemanth: an intelligent assistive tool using exergaming and response surface methodology for patients with brain disorders, IEEE Access, 7, 21502–21513 (2019)CrossRefGoogle Scholar
  12. 12.
    Geman, O., Toderean, R., Lungu, M.M., Chiuchisan, I., Covasa, M.: Challenges in nutrition education using smart sensors and personalized tools for prevention and control of type 2 diabetes. In: Proceedings of 2017 IEEE 23rd International Symposium for Design and Technology in Electronic Packaging (SIITME), pp. 460–469 (2017). E-ISBN: 978-1-5386-1626-0Google Scholar
  13. 13.
    Geman, O., Hagan, M., Chiuchisan, I.: A novel device for peripheral neuropathy assessment and rehabilitation. In: Proceedings of 2016 International Conference and Exposition on Electrical and Power Engineering (EPE), pp. 309–312 (2016). ISBN: 978-1-5090-6129-7Google Scholar
  14. 14.
    Geman, O., Sanei, S., Costin, H.N., Eftaxias, K., Vysata, O., Prochazka, A., Lhotska, L.: Challenges and trends in Ambient Assisted Living and intelligent tools for disabled and elderly people. In: Proceedings of 2015 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), pp. 1–5 (2015). ISBN: 978-1-4673-8457-5Google Scholar
  15. 15.
    Geman, O., Chiuchisan, I., Iuresi, A.C., Chiuchisan, Iulian, Dimian, M. et al.: Intelligent system for a personalized diet of obese patients with cancer. In: Proceedings of 2014 International Conference and Exposition on Electrical and Power Engineering (EPE), Workshop on Electromagnetic Compatibility and Engineering in Medicine and Biology, pp. 528–531 (2014a). E-ISBN: 978-1-4799-5849-8Google Scholar
  16. 16.
    Geman, O., Sanei, S., Chiuchisan, I., Prochazka, A., Vysata, O.: Towards an inclusive parkinson’s screening system. In: Proceedings of 18th International Conference on System Theory, Control and Computing, pp. 470–475 (2014b). ISBN: 978-1-4799-4601-3Google Scholar
  17. 17.
    Geman, O., Costin, H.N.: Parkinson’s Disease Prediction based on Multistate Markov Models. Int. J. Comput. Commun. & Control. 8(4), 525–537 (2013a)Google Scholar
  18. 18.
    Geman, O., Costin, H.N.: Tremor and gait screening and rehabilitation system for patients with neurodegenerative disorders. “Buletinul Institului Politehnic din Iasi” J. Autom. Control. Comput. Sci. Sect., LIX (LXIII) 2, 43–56 (2013b)Google Scholar
  19. 19.
    Guzsvinecz, T., Szucs, V., Sik Lanyi, C.: Developing movement recognition application with the use of Shimmer sensor and Microsoft Kinect sensor. Stud. Health Technol. Inform. 217, 767–772 (2015)Google Scholar
  20. 20.
    Kannan, S., Baskar, N.: Modeling and optimization of face milling operation based on response surface methodology and genetic algorithm. Int. J. Eng. Technol. 5, 959–971 (2013)Google Scholar
  21. 21.
    Kinect: Haas, D., Somphong, P., Jing, Y., et al.: Kinect Based physiotherapy system for home use. Current directions. Biomed. Eng. 1(1), 180–183 (2015)Google Scholar
  22. 22.
    Kinect: Webster, D., Celik, O.: Systematic review of Kinect applications in elderly care and stroke rehabilitation. J. NeuroEngineering Rehabil. 11:108–112 (2014)Google Scholar
  23. 23.
    Kinect: Hondori, H., Khademi, M.: A review on technical and clinical impact of microsoft kinect on physical therapy and rehabilitation. J. Med. Eng. 16 (2014)Google Scholar
  24. 24.
    Kinect: Ekstam, L., Johnson, U., Guidetti, S., Eriksson, G.: The combined perceptions of people with stroke and their carers regarding rehabilitation 1 year after stroke: a mixed methods study. BMJ Open 5(2) (2015)Google Scholar
  25. 25.
    Kinect: Dobkin, B.H.: Rehabilitation after stroke. N. Engl. J. Med., 352(16):1677–1684 (2005)Google Scholar
  26. 26.
    Kinect: McBean D., Wijck F Van.: Perceptuo-motor control. Applied neurosciences for the Allied Health Professions, pp. 65–109. Churchill Livingstone Elsevier, Britain (2013)Google Scholar
  27. 27.
    Kinect: Archambault, P., Norouzi-Gheidari, N., Kairy, D., Solomon, J.M., Levin, M.F.: Towards establishing clinical guidelines for an arm rehabilitation virtual reality system. Replace, Repair, Restore, Relieve—Bridging Clinical and Engineering Solutions in Neurorehabilitation Biosystems Biorobotics. Switzerland, pp. 263–270. Springer International (2014)Google Scholar
  28. 28.
    Kinect: Tao, G., Archambault, P. S., Levin, M.: Evaluation of a virtual reality rehabilitation system for upper limb hemiparesis. In: International Conference Virtual Rehabilitation (ICVR), pp. 163–165 (2013)Google Scholar
  29. 29.
    Kinect: Lange, B., Chang, C.Y., Suma, E., Newman, B., Rizzo, A.S., Bolas, M.: Development and evaluation of low cost game-based balance rehabilitation tool using Microsoft Kinect sensor. Engineering in Medicine and Biology Society, EMBC, pp. 1831–1834 (2011)Google Scholar
  30. 30.
    Mintal, F.A., Szucs, V., Sik-Lanyi, C.: Developing movement therapy application with Microsoft Kinect control for supporting stroke rehabilitation. Stud. Health Technol. Inform. 217, 773–781 (2015)Google Scholar
  31. 31.
    Myers, R.H., Montgomery, D.C., et al: Response surface methodology: a retrospective and literature survey. J. Qual. Technol. 36(1):53–77 (2018)CrossRefGoogle Scholar
  32. 32.
    Parry, I., Carbullido, C., Kawada, J., Bagley, A., et al.: Keeping up with video game technology: objective analysis of Xbox Kinect™ and PlayStation 3 Move™ for use in burn rehabilitation. Burns, 40(5), 852–859 (2014)CrossRefGoogle Scholar
  33. 33.
    Palumbo, F.: Ambient intelligence in assisted living environments. Ph.D. Thesis, Universita degli Studi di Pisa, Dipartimento di Informatica, Dottorato di Ricerca in Informatica (2016)Google Scholar
  34. 34.
    Postolache, O.: Project: smart sensors and tailored environments for physiotherapy. Retrieved May 5, 2018 from: https://www.it.pt/Projects/Index/3223
  35. 35.
    Postolache, O., Viegas, V.V., Dias Pereira, J.M., Girao, P.M.: Smart Sensors Architectures for Vital Signs and Motor Activity Monitoring. Chapter in Advanced Interfacing Techniques for Sensors Measurement Circuits and Systems for Intelligent Sensors. Springer International Publishing, Cham, Switzerland (2017)CrossRefGoogle Scholar
  36. 36.
    Postolache, O., Postolache, G., Carvalho, H.C., Catarino, A.C.: Smart Clothes for Rehabilitation Context: Technical and Technological Issues. Chapter in Sensors for Everyday Life Healthcare Settings. Springer Berlin Heidelberg, Berlin (2016)Google Scholar
  37. 37.
    Postolache, O., Dias Pereira, J.M., Ribeiro, M.R., Girao, P.M.: Assistive Smart Sensing Devices for Gait Rehabilitation Monitoring. Chapter in ICTs for Improving Patients Rehabilitation Research Techniques. Springer International Publishing, Berlin Heidelberg (2015)Google Scholar
  38. 38.
    Postolache, O., Girao, P.M., Postolache, G.: Pervasive Sensing and M-Health: Vital Signs and Daily Activity Monitoring. Chapter in Pervasive and Mobile Sensing and Computing for Healthcare. Springer International Publishing, Heidelberg (2012a)Google Scholar
  39. 39.
    Postolache, G., Girao, P.M., Postolache, O.: Requirements and Barriers to Pervasive Health Adoption. Chapter in Pervasive and Mobile Sensing and Computing for Healthcare—Technological and Social Issues. Springer International Publishing, Heildelberg (2012b)Google Scholar
  40. 40.
    Postolache, O, Girao, P.M., Dias Pereira, J. M.: Water Quality Assessment Through Smart Sensing and Computational Intelligence. Chapter in New Developments and Applications in Sensing. Technology Springer International Publishing. Berlin Heidelberg (2011a)Google Scholar
  41. 41.
    Postolache, O., Dias Pereira, J. M., Girao, P.M.: Underwater Acoustic Source Localization and Sounds Classification in Distributed Measurement Networks. Chapter in Advances in Sound Localization, Pawel Strumillo, In-Tech, Wien (2011b)Google Scholar
  42. 42.
    Postolache, O., Dias Pereira, J.M., Girao, P.M., Postolache, G.: Distributed Smart Sensing Systems for Indoor Monitoring of Respiratory Distress Triggering factors. Chapter in chemistry, emission, control, radioactive pollution and indoor air quality. Intech, In-Tech, Rijeka (2011c)Google Scholar
  43. 43.
    Postolache, O., Girao, P.M., Pinheiro, E.C., Postolache, G.: Unobtrusive and Non-invasive Sensing Solutions for on-line Physiological Parameters Monitoring. Chapter in Wearable and Autonomous Biomedical Devices and Systems for Smart Environment. Springer International Publishing, Berlin (2010)CrossRefGoogle Scholar
  44. 44.
    Ribeiro, J.M., Postolache, O., Girao, P.M.: A Novel Smart Sensing Platform for Vital Signs and Motor Activity Monitoring. Chapter in Sensing Technology: Current Status and Future Trends. Springer International Publishing, Heidelberg (2014)Google Scholar
  45. 45.
    Swanson, L.R., Whittinghill, D.M.: Intrinsic or extrinsic? using videogames to motivate stroke survivors: a systematic review. Games Health J. 4(3), 253–258 (2015)CrossRefGoogle Scholar
  46. 46.
    Webster, D., Celik, O.: Systematic review of Kinect applications in elderly care and stroke rehabilitation. J. Neuroeng. Rehabil. 3 (2014)Google Scholar
  47. 47.
    Wittland, J., Brauner, P., Ziefle, M.: Serious games for cognitive training in ambient assisted living environments—a technology acceptance perspective. In: Proceedings of 15th Interact 2015 Conference, LNCS Vol. 9296, Springer International Publishing, pp. 453–471(2015)Google Scholar
  48. 48.
    World Health Organization: WHO guidelines on integrated care for older people (ICOPE). ISBN: 9789241550109 (2017)Google Scholar
  49. 49.
    Yonghee, Y., Sangmun, S.: Job stress evaluation using response surface data mining. Int. J. Industr. Ergonom. 40, 379–385 (2010)CrossRefGoogle Scholar

Additional Reading Section (Resource List)

  1. 50.
    Allen, D.M.: Mean square error of prediction as a criterion for selecting variables. Technometrics, 13 (1971), ISSN:469-475CrossRefGoogle Scholar
  2. 51.
    Allen, D.M.: The relationship between variable selection and data augmentation and a method for prediction. Technometrics, 16(1974), ISSN:125-127Google Scholar
  3. 52.
    Ahanathapillai, V., Amorx, J., James, C.: Assistive tech-nology to monitor activity, health and wellbeing in old age: the wrist wearable unit in the USEFIL project. Techno. lDisabil. 27, 17–29 (2015)Google Scholar
  4. 53.
    Bo, A. P. L., Hayashibe, P. Poignet: Joint angle estimation in rehabilitation with inertial sensors and its integration with Kinect. In: Lovell, N. (ed) Engineering in Medicine and Biology Society Annual International Conference, pp. 3479 − 83 (2011)Google Scholar
  5. 54.
    Box, G.E.P., Draper, N.R.: Empirical Model-Building and Response Surfaces. Wiley, New York (1987)zbMATHGoogle Scholar
  6. 55.
    Bolandzadeh, N., Kording, K., Salowitz, N., Davis, J.C., Hsu, L., Chan, A., et al.: Predicting cognitive function from clinical measures of physical function and health status in older adults. PLoS ONE 10, e0119075 (2015)CrossRefGoogle Scholar
  7. 56.
    Bridenbaugh, S.A., Kressig, R.W.: Motor cognitive dual tasking: early detection of gait impairment, fall risk and cognitive decline. Z. Gerontol. Geriatr. 48, 15–21 (2015)CrossRefGoogle Scholar
  8. 57.
    Cho, K.H., Lee, W.H.: Virtual walking training program using a real-world video recording for patients with chronic stroke: a pilot study. Am. J. Phys. Med. Rehabil. 92, 371–380 (2013)CrossRefGoogle Scholar
  9. 58.
    Chang, I.S.J., Boger, J. Qiu, J. Mihailidis, A.: Pervasive Computing and Ambient Physiological Monitoring Devices. Assistive Technologies in Smart Environments for People with Disabilities. Boca Raton, FL: CRC Press (2015)Google Scholar
  10. 59.
    de Joode, E., van Heugten, C., Verhey, F., van Boxtel, M.: Efficacy and usability of assistive technology for patientswith cognitive deficits: a systematic review. Clin. Rehabil. 24, 701–714 (2010)CrossRefGoogle Scholar
  11. 60.
    Ijsselsteijn, W.A., Nap, H.H., De Kort, Y., Poels, K.: Digital game design for elderly users. In: Proceedings of the 2007 Conference on Future Play, ACM press, New York, NY, pp. 17–22 (2007)Google Scholar
  12. 61.
    Ienca, M., Jotterand, F., Vica, C., Elger, B.: Social and assistive robotics in dementia care: ethical recommendations for research and practice. Int. J. Soc. Robot. 8, 565–573 (2016)CrossRefGoogle Scholar
  13. 62.
    Joshi., S., Sherali, H.D., Tew, J.D.: An Enhanced Response Surface Methodology (RSM) Algorithm Using Gradient Deflection and Second-Order Search Strategies. Comput. Oper. 2 (7/8), pp. 531–541 (1998)MathSciNetCrossRefGoogle Scholar
  14. 63.
    Khuri, A.I., Cornell, J.A.: Response Surfaces, 2nd edn. Marcel Dekker, New York (1996)zbMATHGoogle Scholar
  15. 64.
    Khosravi, P., Ghapanchi, A.H.: Investigating the effec-tiveness of technologies applied to assist seniors: asystematic literature review. Int. J. Med. Inform. 85, 17–26 (2016)CrossRefGoogle Scholar
  16. 65.
    Laver, K.E., George, S., Thomas, S., Deutsch, J.E., Crotty, M.: Virtual reality for stroke rehabilitation. Cochrane Database Syst. Rev. 9 (2011)Google Scholar
  17. 66.
    Myers, R.H., Montgomery, D.C.: Response Surface Methodology: Process and Product Optimization Using Designed Experiment. A Wiley-Interscience Publication (2002)Google Scholar
  18. 67.
    Montgomery, D.C., Peck, E.A., Vining, G.G.: Introduction to linear regression analysis, 3rd ed. Willey, New York (2001)Google Scholar
  19. 68.
    Myers, R.H.: Classical and Modern Regression with Applications, 2nd edn. Duxbury Press, Boston (1990)Google Scholar
  20. 69.
    Neddermeijer, H.G., van Oortmarssen G.J., Piersma N., Dekker R.: A Framework for Response Surface Methodology for Simulation Optimization Models. Proceedings of the 2000 Winter Simulation Conference, pp. 129–136 (2000)Google Scholar
  21. 70.
    Pasch, M., Bianchi-Berthouze, N., Van Dijk, B., Nijholt, A.: Movement-based sports video games: investigating motivation and gaming experience. Entertain. Comput. 1, 49–61 (2009)CrossRefGoogle Scholar
  22. 71.
    Russel, S.J., Norvig P.: Artificial Intelligence: A Modern Approach. Prentice Hall (1995)Google Scholar
  23. 72.
    Simpson, J.: Challenges and trends driving telerehabilitation. Telerehabilitation, pp. 13–27. Springer-Verlag, London (2013)Google Scholar
  24. 73.
    Taguchi, G.: System of Experimental Design: Engineering Methods to Optimize Quality and Minimize Cost. UNIPUB/Kraus International, White Plains, NY (1987)Google Scholar
  25. 74.
    Zhang, Q., Su, Y., Yu, P.: Assisting an elderly with earlydementia using wireless sensors data in smarter safer home. In: 15th IFIP WG 8.1 international Conference on Informatics and Semiotics in Organisations, ICISO 2014, Springer New York LLC, pp. 398–404 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Oana Geman
    • 1
    Email author
  • Octavian Postolache
    • 2
  • Iuliana Chiuchisan
    • 3
  1. 1.Department of Health and Human DevelopmentStefan cel Mare University SuceavaSuceavaRomania
  2. 2.ISCTE-Instituto Universitário de Lisboa and Instituto de TelecomunicaçõesLisbonPortugal
  3. 3.Computers, Electronics and Automation DepartmentStefan cel Mare University SuceavaSuceavaRomania

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