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Feature-Based Gait Pattern Classification for a Robotic Walking Frame

  • 2020
  • OriginalPaper
  • Chapter
Published in:
Authors
Christopher M. A. Bonenberger
Benjamin Kathan
Wolfgang Ertel
Published in
Advanced Analytics and Learning on Temporal Data
Publisher
Springer International Publishing

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Abstract

This paper presents a system for fast detection of gait patterns of walking frame users, where the challenge is to recognize a change in activity before the signal behaves stationary. The system is used as a basis for inferring the user’s intention in order to develop an improved shared-control strategy for an electric-driven walking frame. The data required for gait pattern identification is recorded by a set of low budget infrared distance sensors. We compare different sliding window based feature extraction methods in combination with classical machine learning algorithms in order to realize a fast real-time online gait classification. Moreover, a simple hierarchical feature extraction method is proposed and evaluated on our data-set.

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previous chapter Localized Random Shapelets
next chapter How to Detect Novelty in Textual Data Streams? A Comparative Study of Existing Methods
Title
Feature-Based Gait Pattern Classification for a Robotic Walking Frame
Authors
Christopher M. A. Bonenberger
Benjamin Kathan
Wolfgang Ertel
Copyright Year
2020
Book
Advanced Analytics and Learning on Temporal Data

Print ISBN: 978-3-030-39097-6

Electronic ISBN: 978-3-030-39098-3

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-39098-3_8
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