Pilot Study on Gait Classification Using fNIRS Signals
Joint Authors
Jin, Hedian
Li, Chunguang
Xu, Jiacheng
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-17
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Rehabilitation training is essential for motor dysfunction patients, and the training through their subjective motion intention, comparing to passive training, is more conducive to rehabilitation.
This study proposes a method to identify motion intention of different walking states under the normal environment, by using the functional near-infrared spectroscopy (fNIRS) technology.
Twenty-two healthy subjects were recruited to walk with three different gaits (including small-step with low-speed, small-step with midspeed, midstep with low-speed).
The wavelet packet decomposition was used to find out the main characteristic channels in different motion states, and these channels with links in frequency and space were combined to define as feature vectors.
According to different permutations and combinations of all feature vectors, a library for support vector machines (libSVM) was used to achieve the best recognition model.
Finally, the accuracy rate of these three walking states was 78.79%.
This study implemented the classification of different states’ motion intention by using the fNIRS technology.
It laid a foundation to apply the classified motion intention of different states timely, to help severe motor dysfunction patients control a walking-assistive device for rehabilitation training, so as to help them restore independent walking abilities and reduce the economic burdens on society.
American Psychological Association (APA)
Jin, Hedian& Li, Chunguang& Xu, Jiacheng. 2018. Pilot Study on Gait Classification Using fNIRS Signals. Computational Intelligence and Neuroscience،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130832
Modern Language Association (MLA)
Jin, Hedian…[et al.]. Pilot Study on Gait Classification Using fNIRS Signals. Computational Intelligence and Neuroscience No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1130832
American Medical Association (AMA)
Jin, Hedian& Li, Chunguang& Xu, Jiacheng. Pilot Study on Gait Classification Using fNIRS Signals. Computational Intelligence and Neuroscience. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1130832
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1130832