The purpose of this research was to investigate object classification algorithms for the application of wheelchair interaction with the environment for the cognitively impaired wheelchair user. Towards this end, top performing object classification algorithms were trained on images of the target object classes (chair, dresser, and sink/washbasin) obtained from the internet and tested on images of the target object classes obtain in the home and patient room environments; these algorithms were Locality-constrained Linear Coding (LLC) [1], Kernel Descriptors (KDES) [2], and Hierarchical Matching Pursuit (HMP) [3]. It was found that HMP achieved the highest over classification accuracy (71.3%) in the home environment and LLC achieved the greatest accuracy (85.0%) in the patient room environment. This research also sought to investigate the potential of active learning to improve upon the obtained classification performance. A maximum mean classification accuracy of 98.6% was achieved when active learning was applied.

The purpose of this research was to investigate object classification algorithms for the application of wheelchair interaction with the environment for the cognitively impaired wheelchair user. Towards this end, top performing object classification algorithms were trained on images of the target object classes (chair, dresser, and sink/washbasin) obtained from the internet and tested on images of the target object classes obtain in the home and patient room environments; these algorithms were Locality-constrained Linear Coding (LLC) [1], Kernel Descriptors (KDES) [2], and Hierarchical Matching Pursuit (HMP) [3]. It was found that HMP achieved the highest over classification accuracy (71.3%) in the home environment and LLC achieved the greatest accuracy (85.0%) in the patient room environment. This research also sought to investigate the potential of active learning to improve upon the obtained classification performance. A maximum mean classification accuracy of 98.6% was achieved when active learning was applied.

Powered wheelchairs offer a means of independent mobility for older adults who are unable to walk and cannot propel a manual wheelchair. Unfortunately, cognitively impaired older adults may be denied this means of independent mobility. There is concern that these adults are unable to drive a powered wheelchair safely or properly. Intelligent wheelchairs offer an approach to address this problem. This research outlines the development and evaluation of an Intelligent Wheelchair System (IWS) that is proposed to make powered wheelchairs safer and easier to use for cognitively impaired older adults. The IWS has anti-collision and navigation functions. Hardware results show a 1000% increase in computational speed compared to the previous IWS. Clinical results with dementia patients show that the IWS has the potential to increase safety by reducing frontal collisions, and by promoting safe completion of movement tasks. Usability of the system may be an issue.

Powered wheelchairs offer a means of independent mobility for older adults who are unable to walk and cannot propel a manual wheelchair. Unfortunately, cognitively impaired older adults may be denied this means of independent mobility. There is concern that these adults are unable to drive a powered wheelchair safely or properly. Intelligent wheelchairs offer an approach to address this problem. This research outlines the development and evaluation of an Intelligent Wheelchair System (IWS) that is proposed to make powered wheelchairs safer and easier to use for cognitively impaired older adults. The IWS has anti-collision and navigation functions. Hardware results show a 1000% increase in computational speed compared to the previous IWS. Clinical results with dementia patients show that the IWS has the potential to increase safety by reducing frontal collisions, and by promoting safe completion of movement tasks. Usability of the system may be an issue.