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


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.


Intelligent wheelchairs Rapid prototyping Qualitative interviews Control strategies 



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)


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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

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