Anti-collision and Navigation Systems for Powered Wheelchairs
Keywords: Anti-collision, cognitive impairments, intelligent wheelchair, older adults, physical disability
In collaboration with: The Laboratory for Computational Intelligence, University of British Colombia and the School of Computer Science, Waterloo
Overview of Research
Older adults who have lost their ability to walk often rely on wheelchairs for their mobility. Adults who lack the physical strength to use manual wheelchairs are given powered wheelchairs, but only if they have the memory and judgment required to safely operate one. These necessary skills are often limited in adults who have cognitive impairments. As a result, cognitively impaired adults who are in need of powered wheelchairs are unable to obtain them and must rely on others for mobility. Independent mobility has been linked to quality of life and a decrease in mobility contributes to a decrease in quality of life .
This project aims to provide a safe and intuitive means of mobility for individuals who have cognitive impairments. To accomplish this goal an Intelligent Wheelchair System is proposed. This system will have four main features:
- The ability to assist a cognitively impaired individual with navigation through the use of audio prompts.
- An anti-collision system to prevent injury to the user and bystanders.
- A shared control approach where the system will aid the user with navigation according to user’s preferences/intents.
- The ability to add-on to existing powered wheelchairs.
Anti-collision protection is paramount when dealing with older adults. Studies have shown that a fall is the most probable outcome after being hit by a wheelchair. Falls for elderly individuals could have severe consequences such as hip fractures, which may even lead to death .
To accomplish the navigation and anti-collision objectives a stereovision camera (Figure 1) is mounted on the wheelchair and is used as an input to the Intelligent Wheelchair System. Images from the camera are used to compute the distance of objects from the wheelchair. This distance reading is used to prevent objects from getting too close to the wheelchair, thus avoiding collisions (Figure 2). The distance reading is also translated to an occupancy grid (Figure 3), which is used by the system to determine the least obstructed routes around objects.
A map of the environment is constructed and the wheelchair is localized using the Gmapping and VSLAM packages available in ROS (www.ros.org). A probabilistic user model (a partially observable Markov decision process) is used to determine the optimal strategy, based on the users’ level of independence and responsiveness. Wayfinding prompts are then issued to the user in order to help them navigate along the optimal route to desired locations, while avoiding obstacles along the way (Figure 4).
A user study has been conducted with six cognitively-impaired older adults with dementia . Participants were required to navigate through a maze with foam obstacles to a pre-specified location. Participants completed two phases, one with the intelligent system activated, and the other with the system de-activated. Results showed that the system increased safety for all participants by lowering the number of frontal collisions. In addition, the wayfinding prompts assisted all users in navigating along the shortest route to the goal. Case studies focusing on quantitative and qualitative data for three participants are reported in . Recommendations for future development and testing of intelligent wheelchairs for cognitively-impaired older adults are provided in .
Current research on this project includes:
- Developing hardware for the system into a user-friendly form;
- Improving anti-collision algorithms to increase robustness and accuracy;
- Improving planning/mapping algorithms to incorporate the user's schedule, preferences, and abilities;
- Object recognition to improve the wheelchair’s interaction with the environment;
- Clinical trials with older adults with dementia to determine system efficacy and obtain user feedback for continued development and testing.
Figure 4: Example of wayfinding prompts issued by intelligent system to cognitively-impaired driver during navigation task (Click on image to enlarge).
Alex Mihailidis, PhD, PEng (University of Toronto)
Jim Little, PhD (University of British Colombia)
Alan Mackworth, PhD (University of British Colombia)
Paul Oramasionwu, MHSc candidate (University of Toronto)
Rosalie Wang, Post-Doctoral Fellow (University of Toronto)
Pooja Viswanathan, Post-Doctoral Fellow (University of Toronto)
Babak Taati, Scientist, (Toronto Rehabilitation Institute)
- IATSL is a member of the CANWHEEL team. CANWHEEL is a CIHR-funded research program aimed to improve the mobility of older adult wheelchair users by enabling power wheelchair use in those who are normally excluded from use of these devices.
- NSERC Graduate Scholarship
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