Paper Info
Title
Gait Phase Partitioning and Footprint Detection Using Mutually Constrained Piecewise Linear Approximation with Dynamic Programming
Abstract
Human gait analysis has been widely used in medical and health fields. It is essential to extract spatio-temporal gait features (e.g., single support duration, step length, and toe angle) by partitioning the gait phase and estimating the footprint position/orientation in such fields. Therefore, we propose a method to partition the gait phase given a foot position sequence using mutually constrained piecewise linear approximation with dynamic programming, which not only represents normal gait well but also pathological gait without training data. We also propose a method to detect footprints by accumulating toe edges on the floor plane during stance phases, which enables us to detect footprints more clearly than a conventional method. Finally, we extract four spatial/temporal gait parameters for accuracy evaluation: single support duration, double support duration, toe angle, and step length. We conducted experiments to validate the proposed method using two types of gait patterns, that is, healthy and mimicked hemiplegic gait, from 10 subjects. We confirmed that the proposed method could estimate the spatial/temporal gait parameters more accurately than a conventional skeleton-based method regardless of the gait pattern.
Year
DOI
Venue
2021
10.1587/transinf.2020ZDP7503
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
DocType
Volume
gait phase partitioning, piecewise linear approximation, dynamic programming, footprint detection, rehabilitation
Journal
E104D
Issue
ISSN
Citations 
11
1745-1361
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Makoto Yasukawa100.34
Yasushi Makihara2101270.67
Toshinori Hosoi300.34
Masahiro Kubo400.34
Yasushi Yagi51752186.22