  This paper introduces our methodology to estimate sidewalk accessibilities
from wheelchair behavior via a triaxial accelerometer in a smartphone installed
under a wheelchair seat. Our method recognizes sidewalk accessibilities from
environmental factors, e.g. gradient, curbs, and gaps, which influence
wheelchair bodies and become a burden for people with mobility difficulties.
This paper developed and evaluated a prototype system that visualizes sidewalk
accessibility information by extracting knowledge from wheelchair acceleration
using deep neural networks. Firstly, we created a supervised convolutional
neural network model to classify road surface conditions using wheelchair
acceleration data. Secondly, we applied a weakly supervised method to extract
representations of road surface conditions without manual annotations. Finally,
we developed a self-supervised variational autoencoder to assess sidewalk
barriers for wheelchair users. The results show that the proposed method
estimates sidewalk accessibilities from wheelchair accelerations and extracts
knowledge of accessibilities by weakly supervised and self-supervised
approaches.
