Design and validation of an intelligent wheelchair towards a clinically-functional outcome
1 Department de Génie Electrique, Ecole Polytechnique de Montréal, Montréal, Canada
2 School of Computer Science, University of Southern California, Los Angeles, USA
3 Department de Génie Mécanique, Université du Québec à Trois-Rivières, Trois-Rivières, Canada
4 Centre de réadaptation Lucie-Bruneau, Montréal, Canada
5 School of Computer Science, McGill University, Montréal, Canada
6 Department of Rehabilitation, Université Laval, Québec, Canada
7 Ecole de readaptation, Universite de Montreal, Montréal, Canada
Journal of NeuroEngineering and Rehabilitation 2013, 10:58 doi:10.1186/1743-0003-10-58Published: 17 June 2013
Many people with mobility impairments, who require the use of powered wheelchairs, have difficulty completing basic maneuvering tasks during their activities of daily living (ADL). In order to provide assistance to this population, robotic and intelligent system technologies have been used to design an intelligent powered wheelchair (IPW). This paper provides a comprehensive overview of the design and validation of the IPW.
The main contributions of this work are three-fold. First, we present a software architecture for robot navigation and control in constrained spaces. Second, we describe a decision-theoretic approach for achieving robust speech-based control of the intelligent wheelchair. Third, we present an evaluation protocol motivated by a meaningful clinical outcome, in the form of the Robotic Wheelchair Skills Test (RWST). This allows us to perform a thorough characterization of the performance and safety of the system, involving 17 test subjects (8 non-PW users, 9 regular PW users), 32 complete RWST sessions, 25 total hours of testing, and 9 kilometers of total running distance.
User tests with the RWST show that the navigation architecture reduced collisions by more than 60% compared to other recent intelligent wheelchair platforms. On the tasks of the RWST, we measured an average decrease of 4% in performance score and 3% in safety score (not statistically significant), compared to the scores obtained with conventional driving model. This analysis was performed with regular users that had over 6 years of wheelchair driving experience, compared to approximately one half-hour of training with the autonomous mode.
The platform tested in these experiments is among the most experimentally validated robotic wheelchairs in realistic contexts. The results establish that proficient powered wheelchair users can achieve the same level of performance with the intelligent command mode, as with the conventional command mode.