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2020 | OriginalPaper | Chapter

Feature-Based Gait Pattern Classification for a Robotic Walking Frame

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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Metadata
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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