![](/images/graphics-bg.png)
String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases
Joint Authors
Theera-Umpon, Nipon
Auephanwiriyakul, Sansanee
Klomsae, Atcharin
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-13
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Neurodegenerative diseases that affect serious gait abnormalities include Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington disease (HD).
These diseases lead to gait rhythm distortion that can be determined by stride time interval of footfall contact times.
In this paper, we present a new method for gait classification of neurodegenerative diseases.
In particular, we utilize a symbolic aggregate approximation algorithm to convert left-foot stride-stride interval into a sequence of symbols using a symbolic aggregate approximation.
We then find string prototypes of each class using the newly proposed string grammar unsupervised possibilistic fuzzy C-medians.
Then in the testing process the fuzzy k-nearest neighbor is used.
We implement the system on three 2-class problems, i.e., the classification of ALS against healthy patients, that of HD against healthy patients , and that of PD against healthy patients.
The system is also implemented on one 4-class problem (the classification of ALS, HD, PD, and healthy patients altogether) called NDDs versus healthy.
We found that our system yields a very good detection result.
The average correct classification for ALS versus healthy is 96.88%, and that for HD versus healthy is 97.22%, whereas that for PD versus healthy is 96.43%.
When the system is implemented on 4-class problem, the average accuracy is approximately 98.44%.
It can provide prototypes of gait signals that are more understandable to human.
American Psychological Association (APA)
Klomsae, Atcharin& Auephanwiriyakul, Sansanee& Theera-Umpon, Nipon. 2018. String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130612
Modern Language Association (MLA)
Klomsae, Atcharin…[et al.]. String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1130612
American Medical Association (AMA)
Klomsae, Atcharin& Auephanwiriyakul, Sansanee& Theera-Umpon, Nipon. String Grammar Unsupervised Possibilistic Fuzzy C-Medians for Gait Pattern Classification in Patients with Neurodegenerative Diseases. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1130612
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130612