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A robust, real-time ground change detector for a "smart" walker

Publication date2014
Abstract

Nowadays, there are many different types of mobility aids for elderly people. Nevertheless, these devices may lead to accidents, depending on the terrain where they are being used. In this context, the goal of the EyeWalker project is to develop a ultralight computer vision device for users with mobility problems. One of the main objective of this work is to develop a ground change detection module that will warn the user before entering dangerous terrains or hostile situations. This software component integrated on a ”smart” walker will be able to react in real time, to operate both indoor and outdoor, as well as in familiar or unfamiliar environments. Specifically, we propose a classification algorithm using colour and texture as a descriptor to detect ground changes. In our classifier, the distributions of HSV colours and Local Edge Patterns are used to compare the similarity between the current frame and the average of the k previous frames. To compare similarities, we used four different techniques (Histogram Intersection, Kolmogorov-Smirnov, Cumulative Integral and Artificial Neural Networks) with outdoor training images. Preliminary results reveal that artificial neural networks achieved the best performances.

Keywords
  • Ground change detection
  • HSV colour model
  • Local Edge Patter (LEP)
  • Elderly care
  • Gerontechnology
Citation (ISO format)
WEISS VELANDIA, Viviana Lucia et al. A robust, real-time ground change detector for a ‘smart’ walker. [s.l.] : [s.n.], 2014.
Main files (1)
Proceedings (Accepted version)
accessLevelPublic
Identifiers
  • PID : unige:74461
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Technical informations

Creation07/28/2015 4:23:00 PM
First validation07/28/2015 4:23:00 PM
Update time03/14/2023 11:31:07 PM
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