Advertisement

Autonomous Robots

, Volume 41, Issue 3, pp 539–554 | Cite as

Intelligent wheelchair control strategies for older adults with cognitive impairment: user attitudes, needs, and preferences

  • Pooja Viswanathan
  • Ellen P. Zambalde
  • Geneviève Foley
  • Julianne L. Graham
  • Rosalie H. Wang
  • Bikram Adhikari
  • Alan K. Mackworth
  • Alex Mihailidis
  • William C. Miller
  • Ian M. Mitchell
Article

Abstract

Intelligent powered wheelchairs can increase mobility and independence for older adults with cognitive impairment by providing collision avoidance and navigation support. The level and/or type of control desired by this target population during intelligent wheelchair use have not been previously explored. In this paper, we present user attitudes, needs, and preferences in a study conducted with a mock intelligent wheelchair offering three different modes of user control. Users wanted to be in the loop during wheelchair operation and/or high-level decision making, and also provided specific contexts where an autonomous wheelchair would be helpful. Participants identified benefits of and concerns with intelligent wheelchairs, along with desired features and functionality. The paper presents the implication of these findings and provides specific recommendations for future intelligent wheelchair development and deployment.

Keywords

Intelligent wheelchairs Rapid prototyping Qualitative interviews Control strategies 

Notes

Acknowledgments

The authors would like to acknowledge all CanWheel team members, especially Kate Keech, Pouria TalebiFard, Emma Smith, Laura Hurd Clarke, Ben Mortenson, Paula Rushton, and Eric Rothfels for their feedback and assistance in conducting the study. We would also like to thank Ellen Maki for conducting statistical analysis, as well as GF Strong, Advanced Mobility, and LTC staff (particularly Sheralyn Manning and Guylaine Desharnais) for all their support. This research was supported by CIHR CanWheel team in Wheeled Mobility for Older Adults (AMG-100925), the Collaborative Health Research Program, Alzheimer’s Society Research Program, AGE-WELL NCE Inc.—a member of the Networks of Centres of Excellence program, Science Without Borders funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)—Ministry of Education of Brazil, NSERC Discovery Grant #298211, an NSERC Undergraduate Student Research Award, the Canadian Foundation for Innovation (CFI) Leaders Opportunity Fund / British Columbia Knowledge Development Fund Grant #13113, the Institute for Computing, Information and Cognitive Systems (ICICS) at UBC, NSERC Grant CRDPJ 434659-12 and the ICICS/TELUS People & Planet Friendly Home Initiative at UBC.

Supplementary material

10514_2016_9568_MOESM1_ESM.docx (82 kb)
Supplementary material 1 (docx 82 KB)

References

  1. Adhikari, B. (2014). A single subject participatory action design method for powered wheelchairs providing automated back-in parking assistance to cognitively impaired older adults: A pilot study. Vancouver: University of British Columbia.Google Scholar
  2. Allison, P. D., & Christakis, N. A. (1994). Logit models for sets of ranked items. Sociological Methodology, 24, 199–228. doi: 10.2307/270983.CrossRefGoogle Scholar
  3. Baltodano, S., Sibi, S., Martelaro, N., Gowda, N., & Ju, W. (2015). RRADS: real road autonomous driving simulation. In Proceedings of the 10th annual ACM/IEEE international conference on human-robot interaction extended abstracts (p. 283). New York, NY, USA: ACM. doi: 10.1145/2701973.2702099.
  4. Borson, S., & Raskind, M. A. (1997). Clinical features and pharmacologic treatment of behavioral symptoms of Alzheimer’s disease. Neurology, 48(5 Suppl 6), S17–24.CrossRefGoogle Scholar
  5. Brandt, A., Iwarsson, S., & Stahle, A. (2004). Older people’s use of powered wheelchairs for activity and participation. Journal of Rehabilitation Medicine, 36(2), 70–77.CrossRefGoogle Scholar
  6. Brighton, C. (2003). Rules of the road. Rehab Managment, 16(3), 18–21.Google Scholar
  7. Carlson, T., & Demiris, Y. (2012). Collaborative control for a robotic wheelchair: evaluation of performance, attention, and workload. IEEE Transaction on Systems Man and Cybernetics B Cybernetics, 42(3), 876–888. doi: 10.1109/tsmcb.2011.2181833.CrossRefGoogle Scholar
  8. Dawson, D. R., Chan, R., & Kaiserman, E. (1994). Development of the power-mobility indoor driving assessment for residents of long term care facilities. Canadian Journal of Occupational Therapy, 61(5), 269–276.CrossRefGoogle Scholar
  9. Demers, L., Weiss-Lambrou, R., & Ska, B. (2002). The Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0): An overview and recent progress. Technology and Disability, 14, 101–105.Google Scholar
  10. Fehr, L., Langbein, W. E., & Skaar, S. B. (2000). Adequacy of power wheelchair control interfaces for persons with severe disabilities: A clinical survey. Journal of Rehabilitation Research and Development, 37(3), 353–360.Google Scholar
  11. Foley, G., Zambalde, E. P., Viswanathan, P., & Mihailidis, A. (2014). A table-docking feature for intelligent powered wheelchairs: defining user needs. In: Toronto Rehabilitation Research Day, 2014 (Vol. Toronto). Chicago: IEEE.Google Scholar
  12. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198.CrossRefGoogle Scholar
  13. Hardy, P. (2004). Examining the barriers: Powered wheelchair mobility for people with cognitive and/or sensory impairments. In ARATA 2004 National Conference, Melbourne, Australia.Google Scholar
  14. Hart, S. G., & Staveland, L. E. (1998). Development of the NASA-TLX (Task Load Index): Results of empirical and theoretical research, Meshkati (N ed., pp. 239–250). Amsterdam: North Holland Press.Google Scholar
  15. How, T. V., Wang, R. H., & Mihailidis, A. (2013). Evaluation of an intelligent wheelchair system for older adults with cognitive impairments. Journal of Neuroengineering and Rehabilitation, 10, 90. doi: 10.1186/1743-0003-10-90.CrossRefGoogle Scholar
  16. Jipp, M. (2013). Levels of automation: Effects of individual differences on wheelchair control performance and user acceptance. Theoretical Issues in Ergonomics Science, 15(5), 479–504. doi: 10.1080/1463922X.2013.815829.CrossRefGoogle Scholar
  17. Kairy, D., Rushton, P. W., Archambault, P., Pituch, E., Torkia, C., El Fathi, A., et al. (2014). Exploring powered wheelchair users and their caregivers’ perspectives on potential intelligent power wheelchair use: a qualitative study. International Journal of Environmental Research and Public Health, 11(2), 2244–2261. doi: 10.3390/ijerph110202244.CrossRefGoogle Scholar
  18. Lewis, C. H. (1982). Using the “thinking Aloud” method in cognitive interface design. New York: IBM T.J. Watson Research Center.Google Scholar
  19. Li, Q., Chen, W., & Wang, J. (2011). Dynamic shared control for humanwheelchair cooperation. In IEEE international conference on robotics and automation (ICRA) (pp. 4278–4283).Google Scholar
  20. Lo, J., Pham, P., Viswanathan, P., & Mihailidis, A. (2014). Intelligent wheelchairs: Training & assessment. In Canadian Association of Occupational Therapists annual conference, Fredericton, NB.Google Scholar
  21. Marcantonio, E. R. (2000). Dementia. In M. H. Beers, T. V. Jones, M. Berkwits, J. L. Kaplan, & R. Porter (Eds.), The merck manual of geriatrics (3rd ed., pp. 357–371). Whitehouse Station, NJ: Merck & Co., Inc.Google Scholar
  22. Masson, F., Maurette, P., Salmi, L. R., Dartigues, J. F., Vecsey, J., Destaillats, J. M., et al. (1996). Prevalence of impairments 5 years after a head injury, and their relationship with disabilities and outcome. Brain Injury, 10(7), 487–497.CrossRefGoogle Scholar
  23. Mitchell, I. M., Viswanathan, P., Adhikari, B., Rothfels, E., & Mackworth, A. K. (2014). Shared control policies for safe wheelchair navigation of elderly adults with cognitive and mobility impairments: Designing a wizard of oz study. In Proceedings of the American Controls Conference, Portland, OR (pp. 4087-4094).Google Scholar
  24. Morris, J. N., Fries, B. E., Mehr, D. R., Hawes, C., Phillips, C., Mor, V., et al. (1994). MDS cognitive performance scale. Journal of Gerontology, 49(4), M174–182.CrossRefGoogle Scholar
  25. Mortenson, W. B., Miller, W. C., Boily, J., Steele, B., Odell, L., Crawford, E. M., et al. (2005). Perceptions of power mobility use and safety within residential facilities. Canadian Journal of Occupational Therapy, 72(3), 142–152.CrossRefGoogle Scholar
  26. Mosimann, U. P., Mather, G., Wesnes, K. A., O’Brien, J. T., Burn, D. J., & McKeith, I. G. (2004). Visual perception in Parkinson disease dementia and dementia with Lewy bodies. Neurology, 63(11), 2091–2096.CrossRefGoogle Scholar
  27. Nasreddine, Z. S., Phillips, N. A., Bedirian, V., Charbonneau, S., Whitehead, V., Collin, I., et al. (2005). The montreal cognitive assessment, MoCA: A brief screening tool for mild cognitive impairment. Journal of the American Geriatrics Society, 53(4), 695–699. doi: 10.1111/j.1532-5415.2005.53221.x.CrossRefGoogle Scholar
  28. Parikh, S. P., Grassi, V, Jr., Kumar, V., & Okamoto, Jun, Jr. (2007). Integrating human inputs with autonomous behaviors on an intelligent wheelchair platform. IEEE Intelligent Systems, 22(2), 33–41.CrossRefGoogle Scholar
  29. Park, J. J. & Kuipers, B. (2015). Feedback motion planning via non-holonomic RRT* for mobile robots. IEEE/RSJ International conference on intelligent robots and systems (IROS-15).Google Scholar
  30. Patton, M. Q. (2002). Qualitative research and evaluation methods. London: Sage Publications Inc.Google Scholar
  31. Peinado, G., Urdiales, C., Peula, J. M., Fernandez-Carmona, M., Annicchiarico, R.,&Sandoval, F., et al. (2011). Navigation skills based profiling for collaborative wheelchair control. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 2229-2234).Google Scholar
  32. Pineau, J., Moghaddam, A. K., Yuen, H. K., Archambault, P. S., Routhier, F., Michaud, F., et al. (2014). Automatic detection and classification of unsafe events during power wheelchair use. IEEE Journal of Translational Engineering in Health and Medicine (JTEHM), 2, 1–9. doi: 10.1109/JTEHM.2014.2365773.CrossRefGoogle Scholar
  33. Ricker, J. H., Keenan, P. A., & Jacobson, M. W. (1994). Visuoperceptual-spatial ability and visual memory in vascular dementia and dementia of the Alzheimer type. Neuropsychologia, 32(10), 1287–1296.CrossRefGoogle Scholar
  34. Riek, L. D. (2012). Wizard of oz studies in hri: a systematic review and new reporting guidelines. Journal of Human-Robot Interaction, 1(1).Google Scholar
  35. Rushton, P., Mortenson, W. B., Viswanathan, P., Wang, R. H., & Hurd Clark, L. (2014). Intelligent power wheelchairs for residents in long-term care facilities: Potential users’ experiences and perceptions. In Rehabilitation Engineering and Assistive Technology Society of North America, Indianapolis, IN.Google Scholar
  36. Rushton, P. W., Kairy, D., Archambault, P., Pituch, E., Torkia, C., El Fathi, A., et al. (2015). The potential impact of intelligent power wheelchair use on social participation: Perspectives of users, caregivers and clinicians. Disability and Rehabilitation: Assistive Technology, 10(3), 191–197. doi: 10.3109/17483107.2014.907366.CrossRefGoogle Scholar
  37. Simpson, R. C. (2005). Smart wheelchairs: A literature review. Journal of Rehabilitation Research and Development, 42(4), 423–436.CrossRefGoogle Scholar
  38. Shiomi, M., Iio, T., Kamei, K., Sharma, C., & Hagita, N. (2015). Effectiveness of social behaviors for autonomous wheelchair robot to support elderly people in Japan. PLoS One, 10(5), e0128031. doi: 10.1371/journal.pone.0128031.CrossRefGoogle Scholar
  39. Smith, E. M., Miller, W. C., Mortenson, W. B., Mihailidis, A., Viswanathan, P., & Lo, J., et al. (2014). Interface design for shared control tele-operated power wheelchair technology. In 8th International convention on rehabilitation engineering & assistive technology (i-CREATE), Singapore.Google Scholar
  40. Strubel, D., & Corti, M. (2008). Wandering in dementia. Psychologie & Neuropsychiatrie du Vieillissement, 6(4), 259–264. doi: 10.1684/pnv.2008.0147.Google Scholar
  41. Urdiales, C., Peula, J. M., Fdez-Carmona, M., Barrué, C., Pérez, E. J., Sánchez-Tato, I., et al. (2011). A new multi-criteria optimization strategy for shared control in wheelchair assisted navigation. Autonomous Robots, 30(2), 179–197.CrossRefGoogle Scholar
  42. Viswanathan, P., Little, J., Mackworth, A., & Mihailidis, A. (2011). Navigation and obstacle avoidance help (NOAH) for older adults with cognitive impairment: A pilot study. In ACM SIGACCESS conference on computers and accessibility (ASSETS), Dundee, Scotland.Google Scholar
  43. Viswanathan, P., Little, J. J., Mackworth, A. K., How, T. V., Wang, R. H., & Mihailidis, A. (2013a). Intelligent wheelchairs for cognitively-impaired older adults in Long-term care: A review. In Rehabilitation engineering and assistive technology society of North America, Bellevue, WA.Google Scholar
  44. Viswanathan, P., Wang, R. H., & Mihailidis, A. (2013b). Wizard-of-Oz and mixed-methods studies to inform intelligent wheelchair design forolder adults with dementia. In Association for the advancement of assistive technology in Europe, Vilamoura, Portugal.Google Scholar
  45. Wang, R. H. (2011). Enabling power wheelchair mobility with long-term care home residents with cognitive impairments. Toronto: University of Toronto.Google Scholar
  46. Wang, R. H., Mihailidis, A., Dutta, T., & Fernie, G. R. (2011). Usability testing of multimodal feedback interface and simulated collision-avoidance power wheelchair for long-term-care home residents with cognitive impairments. Journal of Rehabilitation Research and Development, 48(7), 801–822.CrossRefGoogle Scholar
  47. Wei, Z., Chen, W., & Wang, J. (2012). 3d semantic map-based shared control for smart wheelchair. In Intelligent robotics and applications (pp. 41–51).Google Scholar
  48. Wind, A. W., Schellevis, F. G., Van Staveren, G., Scholten, R. P., Jonker, C., & Van Eijk, J. T. (1997). Limitations of the mini-mental state examination in diagnosing dementia in general practice. International Journal of Geriatric Psychiatry, 12(1), 101–108.CrossRefGoogle Scholar
  49. Wood, L. E. (1997). Semi-structured interviewing for user-centered design. Interactions, 4(2), 48–61.CrossRefGoogle Scholar
  50. Zeng, Q., Burdet, E., & Teo, C. L. (2008). User evaluation of a collaborative wheelchair system. In Proceedings of IEEE Engineering in Medicine and Biology Society Conference (pp. 1956–1960). doi: 10.1109/iembs.2008.4649571.
  51. Zeng, Q., Teo, C. L., Rebsamen, B., & Burdet, E. (2008). A collaborative wheelchair system. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 16(2), 161–170. doi: 10.1109/tnsre.2008.917288.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Pooja Viswanathan
    • 1
  • Ellen P. Zambalde
    • 1
  • Geneviève Foley
    • 1
  • Julianne L. Graham
    • 1
  • Rosalie H. Wang
    • 1
  • Bikram Adhikari
    • 2
  • Alan K. Mackworth
    • 2
  • Alex Mihailidis
    • 1
  • William C. Miller
    • 3
  • Ian M. Mitchell
    • 2
  1. 1.Intelligent Assistive Technology and Systems Lab (IATSL)Department of Occupational Science and Occupational Therapy, University of TorontoTorontoCanada
  2. 2.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada
  3. 3.Department of Occupational Science and Occupational TherapyUniversity of British ColumbiaVancouverCanada

Personalised recommendations