User-centered Implementation of Rehabilitation Exercising on an Assistive Robotic Platform

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12426)


The paper focuses on the method and steps implementing a suite of rehabilitation exercises on an assistive robotic platform. The suite is based on extensive user needs identification procedures and consultation with medical and rehabilitation experts. For the design of the human-robot interaction (HRI) component of the platform, the user centered approach was adopted, which in this case employed a range of multimodal interaction facilities including a free user-robot dialogue, visual and speech signals.


Human-robot interaction Multimodal HRI design User-centered design Assistive HRI User group recruitment methodology Rehabilitation strategy 



This research has been co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK- 01248/MIS: 5030856), as well as POLYTROPON project (KRIPIS-GSRT, MIS: 448306).


  1. 1.
    OECD/EU. Health at a Glance: Europe 2018: State of Health in the EU Cycle, OECD Publishing, Paris (2019).
  2. 2.
    EUROSTAT. Disability statistics. Statistics Explained. Online publication, ISSN 2443-8219. Accessed Sep 2019.
  3. 3.
    Hirvensalo, Μ., Rantanen, T., Heikkinen, M.: Mobility difficulties and physical activity as predictors of mortality and loss of independence in the community-living older population. J. Am. Geriatr. Soc. 48, 493–498 (2005).
  4. 4.
    Chuy, O.J., Hirata, Y., Wang, Z., Kosuge, K.: Approach in assisting a sit-to-stand movement using robotic walking support system. In: 2006 IEEE/RSJ Int’l Conference on Intelligent Robots and Systems, Beijing, pp. 4343–4348 (2006).
  5. 5.
    Chugo, D., Asawa, T., Kitamura, T., Jia, S., Takase, K.: A rehabilitation walker with standing and walking assistance. In: 2008 IEE/RSJ Int’l Conf. on Intelligent Robots and Systems, Nice, pp. 260–265 (2008).
  6. 6.
    Hirata, Y., Komatsuda, S., Kosuge, K.: Fall prevention control of passive intelligent walker based on human model. In: 2008 IEE/RSJ Int’l Conference on Intelligent Robots and Systems, Nice, pp. 1222–1228 (2008).
  7. 7.
    Morris, A., Donamukkala, R., et al.: A robotic walker that provides guidance. In: ICRA 2003 (2003)Google Scholar
  8. 8.
    Dubowsky, S., et al.: Pamm - a robotic aid to the elderly for mobility assistance and monitoring: a ‘helping-hand’ for the elderly. In: IEEE Int’l Conf. on Robotics and Automation, pp. 570–576 (2000)Google Scholar
  9. 9.
    Graf, B., Hans, M., Schraft, R.D.: Mobile robot assistants. IEEE Rob. Autom. Mag. 11(2), 67–77 (2004)Google Scholar
  10. 10.
    Rodriguez-Losada, D., Matia, F., Jimenez, A., Galan, R., Lacey, G.: Implementing map based navigation in guido, the robotic smartwalker. In: Proceedings of the 2005 IEEE International Conference on Robotics and Automation, pp. 3390–3395, April 2005Google Scholar
  11. 11.
    Kulyukin, V., Kutiyanawala, A., LoPresti, E., Matthews, J., Simpson, R.: iwalker: toward a rollator-mounted wayfinding system for the elderly. In: 2008 IEEE International Conference on RFID, pp. 303–311, April 2008Google Scholar
  12. 12.
    Ohnuma, T., et al.: Particle filter based lower limb prediction and motion control for JAIST active robotic walker. In: 2014 RO-MAN (2014)Google Scholar
  13. 13.
    Jenkins, S., et al.: Care, monitoring, and companionship: Views on care robots from older people and their carers. Int. J. Soc. Robot. 7, 673–683 (2015)Google Scholar
  14. 14.
    Efthimiou, E., et al.: The MOBOT rollator human-robot interaction model and user evaluation process. In: Proceedings of the IEEE SSCI Conference, 6–9 December 2016, Athens, pp. 1–8 (2016).
  15. 15.
    Härdi, I., Bridenbaugh, S.A., Gschwind, Y.J., Kressig, R.W.: The effect of three different types of walking aids on spatio-temporal gait parameters in community-dwelling older adults. Aging Clin. Exp. Res. 26(2), 221–228 (2014). Scholar
  16. 16.
    Applebaum, E.V., et al.: Modified 30-second sit to stand test predicts falls in a cohort of institutionalized older veterans. PLoS ONE 12(5), e0176946 (2017). Scholar
  17. 17.
    Berg, K., Wood-Dauphinee, S., Williams, J.I.: The balance scale: reliability assessment with elderly residents and patients with an acute stroke. Scand. J. Rehabil. Med. 27, 27–36 (1995).
  18. 18.
    Bateni, H., Maki, B.E.: Assistive devices for balance and mobility: benefits, demands, and adverse consequences. Arch. Phys. Med. Rehabil. 86(1), 134–145 (2005). Scholar
  19. 19.
    Donoghue, O., Savva, G., Cronin, H., Kenny, R.A., Horgan, F.: Using timed up and go and usual gait speed to predict incident disability in daily activities among community-dwelling adults aged 65 and older. Arch. Phys. Med. Rehabil. 95(10), 1954–1961 (2014).
  20. 20.
    Efthimiou, E., Fotinea, S.-E., Vacalopoulou, A., Papageorgiou, X., Karavasili, A., Goulas, T.: User centered design in practice: adapting HRI to real user needs. In: PETRA 2019 Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments. Rhodes, Greece—05–07 June 2019, pp. 425–429. ACM, New York (2019).
  21. 21.
    Kötteritzsch, A., Weyers, B.: Assistive technologies for older adults in urban areas: a literature review. Cogn. Comput. 8(2), 299–317 (2016). Scholar
  22. 22.
    Stuck, R.E., Rogers, W.A.: Understanding older adult’s perceptions of factors that support trust in human and robot care providers. In: 10th Int’l Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2017), Rhodes, 21–23 June 2017, pp. 372–377. ACM New York (2017). ISBN: 978-1-4503-5227-7.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute for Language and Speech Processing/ATHENA RCAthensGreece
  2. 2.DIAPLASIS Rehabilitation CenterKalamataGreece

Personalised recommendations