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International Journal of Social Robotics

, Volume 9, Issue 3, pp 343–358 | Cite as

Evaluating the Child–Robot Interaction of the NAOTherapist Platform in Pediatric Rehabilitation

  • José Carlos Pulido
  • José Carlos González
  • Cristina Suárez-Mejías
  • Antonio Bandera
  • Pablo Bustos
  • Fernando Fernández
Article

Abstract

NAOTherapist is a cognitive robotic architecture whose main goal is to develop non-contact upper-limb rehabilitation sessions autonomously with a social robot for patients with physical impairments. In order to achieve a fluent interaction and an active engagement with the patients, the system should be able to adapt by itself in accordance with the perceived environment. In this paper, we describe the interaction mechanisms that are necessary to supervise and help the patient to carry out the prescribed exercises correctly. We also provide an evaluation focused on the child-robot interaction of the robotic platform with a large number of schoolchildren and the experience of a first contact with three pediatric rehabilitation patients. The results presented are obtained through questionnaires, video analysis and system logs, and have proven to be consistent with the hypotheses proposed in this work.

Keywords

Social human–robot interaction Rehabilitation robotics Socially assistive robotics Control architectures and programming Automated planning 

References

  1. 1.
    Alcázar V, Guzmán C, Prior D, Borrajo D, Castillo L, Onaindia E (2010) PELEA: planning, learning and execution architecture. In: Proceedings of the 28th workshop of the UK planning and scheduling special interest group (PlanSIG)Google Scholar
  2. 2.
    Boccanfuso L, O’Kane JM (2011) Charlie : an adaptive robot design with hand and face tracking for use in autism therapy. Int J Soc Robot 3(4):337–347. doi: 10.1007/s12369-011-0110-2 CrossRefGoogle Scholar
  3. 3.
    Borggraefe I, Kiwull L, Schaefer JS, Koerte I, Koerte I, Blaschek a, Meyer-Heim a, Heinen F (2010) Sustainability of motor performance after robotic-assisted treadmill therapy in children: an open, non-randomized baseline-treatment study. Eur J Phys Rehabil Med 46(2):125–131Google Scholar
  4. 4.
    Burgar CG, Lum PS, Shor PC, Van der Loos HM (2000) Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience. J Rehabil Res Dev 37(6):663–674Google Scholar
  5. 5.
    Calderita VL, Manso JL, Bustos P, Suárez-Mejías C, Fernández F, Bandera A (2014) THERAPIST: towards an autonomous socially interactive robot for motor and neurorehabilitation therapies for children. JMIR Rehabil Assist Technol (JRAT) 1(1):e1. doi: 10.2196/rehab.3151 Google Scholar
  6. 6.
    Castelli E (2011) Robotic movement therapy in cerebral palsy. Dev Med Child Neurol 53(6):481–481. doi: 10.1111/j.1469-8749.2011.03987.x CrossRefGoogle Scholar
  7. 7.
    Yk Choe, Jung HT, Baird J, Grupen RA (2013) Multidisciplinary stroke rehabilitation delivered by a humanoid robot: interaction between speech and physical therapies. Aphasiology 27(3):252–270. doi: 10.1080/02687038.2012.706798 CrossRefGoogle Scholar
  8. 8.
    Dehkordi PS, Moradi H, Mahmoudi M, Pouretemad HR (2015) The design, development, and deployment of roboparrot for screening autistic children. Int J Soc Robot 7(4):513–522. doi: 10.1007/s12369-015-0309-8 CrossRefGoogle Scholar
  9. 9.
    Drubicki M, Rusek W, Snela S, Dudek J, Szczepanik M, Zak E, Durmala J, Czernuszenko A, Bonikowski M, Sobota G (2013) Functional effects of robotic-assisted locomotor treadmill thearapy in children with cerebral palsy. J Rehabil Med Off J UEMS Eur Board Phys Rehabil Med 45(4):358–363. doi: 10.2340/16501977-1114 Google Scholar
  10. 10.
    Dubowsky S, Genot F, Godding S, Kozono H, Skwersky A, Yu H, Yu LS (2000) Pamm-a robotic aid to the elderly for mobility assistance and monitoring: a helping-hand for the elderly. In: Proceedings. ICRA’00. IEEE international conference on robotics and automation, vol 1, 2000. IEEE, pp 570–576Google Scholar
  11. 11.
    Eriksson J, Mataric MJ, Winstein C (2005) Hands-off assistive robotics for post-stroke arm rehabilitation. In: Proceedings of the 9th international conference on rehabilitation robotics (ICORR). IEEE, pp 21–24Google Scholar
  12. 12.
    Fasola J, Mataric M (2010) Robot exercise instructor: a socially assistive robot system to monitor and encourage physical exercise for the elderly. In: RO-MAN, 2010. IEEE, pp 416–421. doi: 10.1109/ROMAN.2010.5598658
  13. 13.
    Feil-Seifer D, Mataric MJ (2005) Defining socially assistive robotics. In: Proceedings of the 9th international conference on rehabilitation robotics (ICORR). IEEE, pp 465–468Google Scholar
  14. 14.
    Fong T, Nourbakhsh I, Dautenhahn K (2003) A survey of socially interactive robots. Robot Auton Syst 42(3):143–166CrossRefzbMATHGoogle Scholar
  15. 15.
    Fox M, Long D (2003) PDDL2.1: an extension to PDDL for expressing temporal planning domains. J Artif Intell Res (JAIR) 20(1):61–124zbMATHGoogle Scholar
  16. 16.
    Fridin M (2014) Kindergarten social assistive robot: first meeting and ethical issues. Comput Hum Behav 30:262–272. doi: 10.1016/j.chb.2013.09.005 CrossRefGoogle Scholar
  17. 17.
    Fridin M, Belokopytov M (2014) Robotics agent coacher for cp motor function (rac cp fun). Robotica 32:1265–1279. doi: 10.1017/S026357471400174X CrossRefGoogle Scholar
  18. 18.
    Garcia N, Sabater-Navarro J, Gugliemeli E, Casals A (2011) Trends in rehabilitation robotics. Med Biol Eng Comput 49(10):1089–1091. doi: 10.1007/s11517-011-0836-x CrossRefGoogle Scholar
  19. 19.
    Ghallab M, Nau D, Traverso P (2004) Automated planning: theory and practice. ElsevierGoogle Scholar
  20. 20.
    Gonzlez JC, Pulido JC, Fernndez F (2016) A three-layer planning architecture for the autonomous control of rehabilitation therapies based on social robots. Cogn Syst Res. doi: 10.1016/j.cogsys.2016.09.003 Google Scholar
  21. 21.
    Graf B, Reiser U, Hägele M, Mauz K, Klein P (2009) Robotic home assistant care-o-bot®3 - product vision and innovation platform. In: Workshop on advanced robotics and its social impacts (ARSO), 2009. IEEE, pp 139–144. doi: 10.1109/ARSO.2009.5587059
  22. 22.
    Hoffmann J (2003) The metric-FF planning system: translating ”ignoring delete lists” to numeric state variables. J Artif Intell Res (JAIR) 20(1):291–341zbMATHGoogle Scholar
  23. 23.
    Kahn LE, Averbuch M, Rymer WZ, Reinkensmeyer DJ, D P (2001) Comparison of robot-assisted reaching to free reaching in promoting recovery from chronic stroke. In: Proceedings 7th international conference on rehabilitation robotics in integration of assistive technology in the information age. IOS Press, pp 39–44Google Scholar
  24. 24.
    Kozima H, Michalowski MP, Nakagawa C (2008) Keepon. Int J Soc Robot 1(1):3–18. doi: 10.1007/s12369-008-0009-8 CrossRefGoogle Scholar
  25. 25.
    Lacey G, Dawson-Howe KM (1998) The application of robotics to a mobility aid for the elderly blind. Robot Auton Syst 23(4):245–252. doi: 10.1016/S0921-8890(98)00011-6 intelligent Robotics Systems - SIRS’97CrossRefGoogle Scholar
  26. 26.
    Leite I, Martinho C, Paiva A (2013) Social robots for long-term interaction: a survey. Int J Soc Robot 5(2):291–308. doi: 10.1007/s12369-013-0178-y CrossRefGoogle Scholar
  27. 27.
    Manso L, Bachiller P, Bustos P, Núñez P, Cintas R, Calderita L (2010) RoboComp: A tool-based robotics framework. In: Ando N, Balakirsky S, Hemker T, Reggiani M, von Stryk O (eds) Simulation, modeling, and programming for autonomous robots, lecture notes in computer science, vol 6472. Springer, Berlin, pp 251–262. doi: 10.1007/978-3-642-17319-6_25
  28. 28.
    Manso LJ, Calderita LV, Bustos P, García J, Martínez M, Fernández F, Garcés AR, Bandera A (2014) A general-purpose architecture to control mobile robots. In: Proceedings of the 15th workshop of physical agents (WAF 2014). León, pp 105–116Google Scholar
  29. 29.
    Mataric M, Eriksson J, Feil-Seifer D, Winstein C (2007) Socially assistive robotics for post-stroke rehabilitation. J NeuroEng Rehabil 4(1):5. doi: 10.1186/1743-0003-4-5 CrossRefGoogle Scholar
  30. 30.
    McMurrough C, Ferdous S, Papangelis A, Boisselle A, Heracleia FM (2012) A survey of assistive devices for cerebral palsy patients. In: Proceedings of the 5th international conference on PErvasive technologies related to assistive environments. ACM, New York, PETRA ’12, pp 17:1–17:8. doi: 10.1145/2413097.2413119
  31. 31.
    Meyer-Heim A, van Hedel HJ (2013) Robot-assisted and computer-enhanced therapies for children with cerebral palsy: current state and clinical implementation. Semin Pediatr Neurol 20(2):139–145. doi: 10.1016/j.spen.2013.06.006 update on Cerebral Palsy: Diagnostics, Therapies and the Ethics of it AllCrossRefGoogle Scholar
  32. 32.
    Nalin M, Baroni I, Sanna A (2012) A motivational robot companion for children in therapeutic setting. In: IROS 2012Google Scholar
  33. 33.
    Nau D, Au TC, Ilghami O, Kuter U, Murdock JW, Wu D, Yaman F (2003) SHOP2: an HTN planning system. J Artif Intell Res (JAIR) 20:379–404zbMATHGoogle Scholar
  34. 34.
    Ni D, Song A, Tian L, Xu X, Chen D (2015) A walking assistant robotic system for the visually impaired based on computer vision and tactile perception. Int J Soc Robot 7(5):617–628. doi: 10.1007/s12369-015-0313-z CrossRefGoogle Scholar
  35. 35.
    Perry J, Rosen J, Burns S (2007) Upper-limb powered exoskeleton design. IEEE/ASME Trans Mechatron 12(4):408–417. doi: 10.1109/TMECH.2007.901934 CrossRefGoogle Scholar
  36. 36.
    Pulido JC, González JC, González-Ferrer A, García J, Fernández F, Bandera A, Bustos P, Suárez C (2014) Goal-directed generation of exercise sets for upper-limb rehabilitation. In: Proceedings of knowledge engineering for planning and scheduling workshop (KEPS). ICAPS, pp 38–45Google Scholar
  37. 37.
    Song A, Wu C, Ni D, Li H, Qin H (2016) One-therapist to three-patient telerehabilitation robot system for the upper limb after stroke. Int J Soc Robot 8(2):319–329. doi: 10.1007/s12369-016-0343-1 CrossRefGoogle Scholar
  38. 38.
    Suárez Mejías C, Echevarría C, Núñez P, Manso L, Bustos P, Leal S, Parra C (2013) Ursus: a robotic assistant for training of children with motor impairments. In: Converging clinical and engineering research on neurorehabilitation, biosystems and biorobotics, vol 1. Springer, Berlin, pp 249–253. doi: 10.1007/978-3-642-34546-3_39
  39. 39.
    Tapus A, Mataric M, Scasselati B (2007) Socially assistive robotics [grand challenges of robotics]. IEEE Robot Autom Mag 14(1):35–42. doi: 10.1109/MRA.2007.339605 CrossRefGoogle Scholar
  40. 40.
    Wainer J, Dautenhahn K, Robins B, Amirabdollahian F (2013) A pilot study with a novel setup for collaborative play of the humanoid robot kaspar with children with autism. Int J Soc Robot 6(1):45–65. doi: 10.1007/s12369-013-0195-x CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • José Carlos Pulido
    • 1
  • José Carlos González
    • 1
  • Cristina Suárez-Mejías
    • 2
  • Antonio Bandera
    • 3
  • Pablo Bustos
    • 4
  • Fernando Fernández
    • 1
  1. 1.Computer Science and EngineeringUniversidad Carlos III de MadridMadridSpain
  2. 2.Hospital Universitario Virgen del RocíoSevillaSpain
  3. 3.Universidad de MálagaMálagaSpain
  4. 4.Robolab, Universidad de ExtremaduraCáceresSpain

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