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Pathological Gait Abnormality Detection and Segmentation by Processing the Hip Joints Motion Data to Support Mobile Gait Rehabilitation

Khan, W and Badii, A (2019) Pathological Gait Abnormality Detection and Segmentation by Processing the Hip Joints Motion Data to Support Mobile Gait Rehabilitation. Research in C Medical & Engineering Sciences, 07 (03). ISSN 2576-8816

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Abstract

An accurate detection of the gait sub-phases is fundamental in clinical gait analysis to interpret kinetic and kinematic data. In general, detecting the gait events that mark the transition from one gait sub-phase to another as well as the sequence of sub-phases is essential to evaluate gait abnormalities. However, finding a reliable segmentation for pathological gait has been a challenging task. This manuscript entails a generic approach for the gait segmentation into sub-phases in the CORBYS1 system. A number of distinctive features are extracted from the Hip joints motion data which are able to partition and segment the gait cycles in an efficient way. The degree of deviation (i.e. anomaly) in each sub-phase is then calculated with respect to an optimal gait reference which is used for robot-assisted gait rehabilitation. The proposed gait segmentation method is applicable to gait with many types of pathology since training on the pathology specific templates is not required. Performance of the proposed algorithm is evaluated by statistical analysis of results which produced 100% gait segmentation accuracy for healthy subjects and over 99% for pathological subjects.

Item Type: Article
Uncontrolled Keywords: Gait rehabilitation; Pathological gait segmentation; Gait sub-phases; Gait Analysis; Robot assisted gait; Gait anomaly detection
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine > R Medicine (General)
Divisions: Computer Science
Publisher: Crimson Publishers
Related URLs:
Date Deposited: 13 Feb 2019 11:42
Last Modified: 13 Feb 2019 11:44
DOI or Identification number: 10.31031/RMES.2019.07.000662
URI: http://researchonline.ljmu.ac.uk/id/eprint/10156

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