Descending stairs and floors classification as control reference in autonomous smart wheelchair

Abstract

Disability is a disruption or limitation of a person’s body functions in carrying out daily activities. A person with physical disabilities needs an assistive device such as a wheelchair. The latest wheelchair development is the smart wheelchair. Smart wheelchairs require a control system to detect obstacles quickly. This aims to provide safety, especially for users. One of the obstacles that are quite dangerous is descending stairs. Therefore the researchers propose a descending stairs detection system for smart wheelchairs. The proposed method in this study is the gray level co-occurrence matrix (GLCM) as the feature extraction algorithm, learning vector quantization (LVQ) as the classification algorithm, and sequential forward selection (SFS) for feature selection. Based on the simulation result, the SFS feature selection gets two selected GLCM features. The best accuracy is 94.5% with the selected features, namely contrast and dissimilarity. This result has an increase in accuracy when compared to using the GLCM 6 feature method with the LVQ classification that does not use feature selection, where the method gets 92.5% accuracy in off-time testing. Accuracy decreased to 78.21% when detecting floors and 89.06% when detecting descending stairs in real-time system testing.