Recognition of gait cycle phases using wearable sensors

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The analysis and monitoring of the human daily living activities plays an important role for rehabilitation goals, fall prevention rehabilitation and general health-care treatments. Among these activities, walking is the most important daily motion. Studying the evolution of the gait cycle through the analysis of the human center of force is beneficial to predict any abnormal walking pattern. The analysis is based on the use of pressure-based mapping system that collects pressure and force measurement during the gait cycle. This paper deals mainly with the detection of the main characteristics of the gait phases. To this end, a segmentation of the center of force of the human body measure through the in-shoe pressure mapping system is performed to identify the gait phases. The proposed segmentation approach consists in modeling each segment by a regression model and using logistic functions to model the transitions between segments. This flexible modeling through the logistic functions has the advantage of detecting abrupt and smooth transitions between segments. (C) 2014 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2016-01
Language
English
Article Type
Article
Citation

ROBOTICS AND AUTONOMOUS SYSTEMS, v.75, pp.50 - 59

ISSN
0921-8890
DOI
10.1016/j.robot.2014.10.012
URI
http://hdl.handle.net/10203/250035
Appears in Collection
ME-Journal Papers(저널논문)
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