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Development of a flexible rehabilitation system for bedridden patients

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Abstract

A portable mechatronic rehabilitation system easily adaptable to different situations—e.g., different body members, training modes and physical spaces—using Internet of Things communication and designed applying a methodology based on user requirements, named SARPA, is developed. Bedridden patients, i.e., those who stay in bed for long periods, often have diseases due to immobility. Conventional rehabilitation to mobility recovery is conducted by therapists who present humanly limited strength characteristics (force, speed, etc.), mainly if the patient is overweight. Mechatronic rehabilitation systems aim to optimize comfort, cost, force, and time to the user, i.e., the patient and therapist pair. However, in general, these systems are conceived to execute just one training mode on just one determined body member in the lower or upper limb. Therefore, a different system is often required for a different training mode or body member. Using SARPA, users may select a body member (among lower or upper limb), an active (isokinetic, isotonic, or isometric), or passive mode and configure it according to a specific therapy. SARPA is configured through a Human Machine Interface based on the Internet of Things, with characteristics that may exceed the values of a human-made conventional rehabilitation. The SARPA flexibility allows several rehabilitation options using a single system. Through SARPA, it would be possible to program games or competitions among patients using the Internet of Things technology, improving their mood and autonomy level during therapeutic sessions. In this paper, it is presented the SARPA system design methodology—based on user requirements, construction and preliminary applications in a hospital.

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References

  1. Accoto D, Sergi F, Tagliamonte N, Carpino G, Sudano A, Guglielmelli E (2014) Robomorphism: a nonanthropomorphic wearable robot. IEEE Robot Autom Mag 21:45–55

    Article  Google Scholar 

  2. Ada L, Dean CM, Vargas J, Ennis S (2010) Mechanically assisted walking with body weight support results in more independent walking than assisted overground walking in non-ambulatory patients early after stroke: a systematic review. J Physiother 56(3):153–161

    Article  Google Scholar 

  3. Agrawal A, Dube AN, Kansara D, Shah S, Sheth S (2016) Exoskeleton: the friend of mankind in context of rehabilitation and enhancement he mechanical structure. Indian J Sci Technol 9:1–8

    Article  Google Scholar 

  4. Akao Y, King B, Mazur GH (1990) Quality function deployment: integrating customer requirements into product design, vol 21. Productivity Press, Cambridge

    Google Scholar 

  5. Akdogan E, Adli MA (2011) The design and control of a therapeutic exercise robot for lower limb rehabilitation: physiotherabot. Mechatronics 21(3):509–522

    Article  Google Scholar 

  6. Behal A, Chen J, Dawson DM (2008) A novel path planning and control framework for passive resistance therapy with a robot manipulator. Int J Syst Sci 39(6):639–653

    Article  MathSciNet  MATH  Google Scholar 

  7. Bekdemir A, Ilhan N (2019) Predictors of caregiver burden in caregivers of bedridden patients. J Nurs Res 27(3):e24

    Article  Google Scholar 

  8. Brokaw E, Black I, Holley R, Lum P (2011) Hand spring operated movement enhancer (handsome): a portable, passive hand exoskeleton for stroke rehabilitation. IEEE Trans Neural Syst Rehabil Eng 19:391–399

    Article  Google Scholar 

  9. Brokaw EB, Nichols D, Holley RJ, Lum PS (2014) Robotic therapy provides a stimulus for upper limb motor recovery after stroke that is complementary to and distinct from conventional therapy. Neurorehabil Neural Repair 28(4):367–376

    Article  Google Scholar 

  10. Casadio M, Giannoni P, Morasso P, Sanguineti V (2009) A proof of concept study for the integration of robot therapy with physiotherapy in the treatment of stroke patients. Clin Rehabil 23(3):217–228 PMID: 19218297

    Article  Google Scholar 

  11. Chen S-H, Lien W-M, Wang W-W, Lee G-D, Hsu L-C, Lee K-W, Lin S-Y, Lin C-H, Fu L-C, Lai J-S et al (2016) Assistive control system for upper limb rehabilitation robot. IEEE Trans Neural Syst Rehabil Eng 24(11):1199–1209

    Article  Google Scholar 

  12. Cheng P-Y, Lai P-Y (2013) Comparison of exoskeleton robots and end-effector robots on training methods and gait biomechanics. In: International conference on intelligent robotics and applications. Springer, pp 258–266

  13. Coote S, Murphy T, Harwin W, Stokes E (2008) The effect of the gentle/s robot-mediated therapy system on arm function after stroke. Clin Rehabil 22:395–405

    Article  Google Scholar 

  14. Darwish A, Hassanien AE, Elhoseny M, Sangaiah AK, Muhammad K (2017) The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Ambient Intell Human Comput 1–16

  15. Dehghan SAM, Koofigar HR, Ekramian M (2018) An adaptive arm’s mechanical impedance estimator for rehabilitation robots without force and acceleration sensors. Int J Syst Sci 49(13):2784–2796

    Article  MathSciNet  Google Scholar 

  16. Diaz I, Gil JJ, Sanchez E (2011) Lower-limb robotic rehabilitation: literature review and challenges. J Robot 2011(i):1–11

    Google Scholar 

  17. Doshi H, Shah M, Shaikh U (2017) Internet of things (iot): Integration of blynk for domestic usability. Vishwakarma J Eng Res 1(4):149–157

    Google Scholar 

  18. Fan YJ, Yin YH, Da Xu L, Zeng Y, Wu F (2014) Iot-based smart rehabilitation system. IEEE Trans Ind Inf 10(2):1568–1577

    Article  Google Scholar 

  19. Golik-Peric D, Drapsin M, Obradovic B, Drid P (2011) Short-term isokinetic training versus isotonic training: effects on asymmetry in strength of thigh muscles. J Hum Kinet 30:29–35

    Article  Google Scholar 

  20. Hamar D (2015) Universal linear motor driven leg press dynamometer and concept of serial stretch loading. Eur J Transl Myol 25(4):215

    Article  Google Scholar 

  21. Hesse S, Schulte-Tigges G, Konrad M, Bardeleben A, Werner C (2003) Robot-assisted arm trainer for the passive and active practice of bilateral forearm and wrist movements in hemiparetic subjects. Arch Phys Med Rehabil 84(6):915–920

    Article  Google Scholar 

  22. Housman S, Scott K, Reinkensmeyer D (2009) A randomized controlled trial of gravity-supported, computer-enhanced arm exercise for individuals with severe hemiparesis. Neurorehabil Neural Repair 23:505–14

    Article  Google Scholar 

  23. Islam SR, Kwak D, Kabir MH, Hossain M, Kwak K-S (2015) The internet of things for health care: a comprehensive survey. IEEE Access 3:678–708

    Article  Google Scholar 

  24. Jiang C, Xiang Z (2020) A novel gait training device for bedridden patients’ rehabilitation. J Mech Med Biol 20:2050024

    Article  Google Scholar 

  25. Kisner C, Colby LA (2007) Therapeutic exercise. Mendeley, London

    Google Scholar 

  26. Kranz M, Holleis P, Schmidt A (2010) Embedded interaction: interacting with the internet of things. IEEE Int Comput 14(2):46–53

    Article  Google Scholar 

  27. Kumar S, Yadav R, Afrin A (2020) The effectiveness of a robotic tilt table on the muscle strength and quality of life in individuals following stroke: a randomised control trial. Int J Ther Rehabil 27(12):1–9

    Article  Google Scholar 

  28. Li X, Zhong J (2020) Upper limb rehabilitation robot system based on internet of things remote control. IEEE Access 8:154461–154470

    Article  Google Scholar 

  29. Liao H, Chang Y, Wu D, Gou X (2020) Improved approach to quality function deployment based on pythagorean fuzzy sets and application to assembly robot design evaluation. Front Eng Manag 7(1):196–203

    Article  Google Scholar 

  30. Liu Y, Li C, Ji L, Bi S, Zhang X, Huo J, Ji R (2017) Development and implementation of an end-effector upper limb rehabilitation robot for hemiplegic patients with line and circle tracking training. J Healthcare Eng 2017

  31. Loureiro RC, Harwin WS, Nagai K, Johnson M (2011) Advances in upper limb stroke rehabilitation: a technology push. Med Biol Eng Comput 49(10):1103

    Article  Google Scholar 

  32. Maceira-Elvira P, Popa T, Schmid A-C, Hummel FC (2019) Wearable technology in stroke rehabilitation: toward improved diagnosis and treatment of upper-limb motor impairment. J Neuroeng Rehabil 16(1):142

    Article  Google Scholar 

  33. Maciejasz P, Eschweiler J, Gerlach-Hahn K, Jansen-Troy A, Leonhardt S (2014) A survey on robotic devices for upper limb rehabilitation. J Neuroeng Rehabil 11(1):3

    Article  Google Scholar 

  34. Madonski R, Kordasz M, Sauer P (2014) Application of a disturbance-rejection controller for robotic-enhanced limb rehabilitation trainings. ISA Trans 53(4):899–908

    Article  Google Scholar 

  35. Meyer S, Verheyden G, Kempeneers K, Michielsen M (2021) Arm-hand boost therapy during inpatient stroke rehabilitation: a pilot randomized controlled trial. medRxiv

  36. Meziani Y, Morère Y, Hadj-Abdelkader A, Benmansour M, Bourhis G (2021) toward adaptive and finer rehabilitation assessment: a learning framework for kinematic evaluation of upper limb rehabilitation on an armeo spring exoskeleton. Control Eng Pract 111:104804

    Article  Google Scholar 

  37. Michmizos KP, Rossi S, Castelli E, Cappa P, Krebs HI (2015) Robot-aided neurorehabilitation: a pediatric robot for ankle rehabilitation. IEEE Trans Neural Syst Rehabil Eng 23(6):1056–1067

    Article  Google Scholar 

  38. Nef T, Mihelj M, Riener R (2007) Armin: a robot for patient-cooperative arm therapy. Med Biol Eng Comput 45:887–900

    Article  Google Scholar 

  39. Owen A, Wiles J, Swaine I (2010) Effect of isometric exercise on resting blood pressure: a meta analysis. J Hum Hypertens 24:796–800

    Article  Google Scholar 

  40. Ozgur AG, Wessel MJ, Johal W, Sharma K, Ozgur A, Vuadens P, Mondada F, Hummel FC, Dillenbourg P (2018) Iterative design of an upper limb rehabilitation game with tangible robots. In: ACM/IEEE international conference on human–robot interaction (HRI), p 187

  41. Ozkul F, Barkana DE (2013) Upper-extremity rehabilitation robot rehabroby: methodology, design, usability and validation. Int J Adv Rob Syst 10(12):401

    Article  Google Scholar 

  42. Ponce Saldias DA, Martins D, Martin C, Da Silva Rosa F, de Mello Roesler CR, Ocampo More AD (2015) Development of a scale prototype of isokinetic dynamometer. Ingeniare Revista chilena de ingenieria 23(2)

  43. Pons JL (2008) Wearable robots: biomechatronics exoskeletons. Wiley, Hoboken

    Book  Google Scholar 

  44. Pons JL, Raya R, Gonzalez J (2016) Emerging therapies in neurorehabilitation II, vol 10. Springer, Berlin

    Book  Google Scholar 

  45. Rahman MH, Rahman MJ, Cristobal O, Saad M, Kenné J-P, Archambault PS (2015) Development of a whole arm wearable robotic exoskeleton for rehabilitation and to assist upper limb movements. Robotica 33(1):19–39

    Article  Google Scholar 

  46. Raja TYMV (2016) Internet of things: benefits and risk of smart healthcare application. Innovation 10(3):37–42

    Google Scholar 

  47. Richardson R, Jackson A, Culmer P, Bhakta B, Levesley MC (2006) Pneumatic impedance control of a 3-dof physiotherapy robot. Adv Robot 20(12):1321–1339

    Article  Google Scholar 

  48. Schuster C, Schuster J, Oliveira A (2015) Application of the mudge diagram and qfd using the hierarchization of the requirements for a flying car as an example. Revista Gestao da Producao, Operacoes e Sistemas 10:197–214

    Article  Google Scholar 

  49. Smith MJ, Melton P (1981) Isokinetic versus isotonic variable-resistance training. Am J Sports Med 9(4):275–279

    Article  Google Scholar 

  50. Systems E (2019) ESP32 technicar reference manual—version 41. Espressif Systems, Shanghai

    Google Scholar 

  51. TeachPE (2017) Types of muscle contraction

  52. Tun S, Madanian S, Mirza F (2020) Internet of things (iot) applications for elderly care: a reflective review. Aging Clinical and Experimental Research, New York

    Google Scholar 

  53. Ugurlu B, Nishimura M, Hyodo K, Kawanishi M, Narikiyo T (2015) Proof of concept for robot-aided upper limb rehabilitation using disturbance observers. IEEE Trans Hum Mach Syst 45(1):110–118

    Article  Google Scholar 

  54. Valdivia C, Ortega A, Salazar M, Rivera H (2014) Modeling and simulation of a therapeutic robot for lower limbs rehabilitation. Revista Ingenieria Biomedica 7(14)

  55. Wallard L, Dietrich G, Kerlirzin Y, Bredin J (2015) Effects of robotic gait rehabilitation on biomechanical parameters in the chronic hemiplegic patients. Neurophysiol Clin 45(3):215–219

    Article  Google Scholar 

  56. Washabaugh E, Guo J, Chang C-K, Remy D, Krishnan C (2018) A portable passive rehabilitation robot for upper-extremity functional resistance training. IEEE Trans Biomed Eng

  57. Werner C (2006) Machines to support motor rehabilitation after stroke: 10 years of experience in berlin. J Rehabil Res Dev 43(5):671–678

    Article  Google Scholar 

  58. Williams K, Christenbury J, Niemeier JP, Newman M, Pinto S (2020) Is robotic gait training feasible in adults with disorders of consciousness? J Head Trauma Rehabil 35(3)

  59. Wu J, Gao J, Song R, Li R, Li Y, Jiang L (2016) The design and control of a 3dof lower limb rehabilitation robot. Mechatronics 33:13–22

    Article  Google Scholar 

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Acknowledgements

This study was partially funded by the Brazilian foundations CAPES—Project PGPTA-3686/2014001- and CNPq.

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Authors and Affiliations

  1. Universidade do Estado de Santa Catarina, SC, Joinville, Brazil

    Alexandre Campos & Eliseo Cortés

  2. Universidade Federal de Santa Catarina, SC, Florianópolis, Brazil

    Daniel Martins

  3. Universidad Politécnica de Madrid, Madrid, Spain

    Manuel Ferre & Aldo Contreras

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  1. Alexandre Campos
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Correspondence to Alexandre Campos.

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Campos, A., Cortés, E., Martins, D. et al. Development of a flexible rehabilitation system for bedridden patients. J Braz. Soc. Mech. Sci. Eng. 43, 361 (2021). https://doi.org/10.1007/s40430-021-03073-7

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