Pilot Study on Gait Classification Using fNIRS Signals

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...

Full description

Bibliographic Details
Main Authors: Hedian Jin, Chunguang Li, Jiacheng Xu
Format: Article
Language:English
Published: Hindawi Limited 2018-01-01
Series:Computational Intelligence and Neuroscience
Online Access:http://dx.doi.org/10.1155/2018/7403471
id doaj-279337131bc4472ba291557e87a4af4d
record_format Article
spelling doaj-279337131bc4472ba291557e87a4af4d2020-11-24T21:15:36ZengHindawi LimitedComputational Intelligence and Neuroscience1687-52651687-52732018-01-01201810.1155/2018/74034717403471Pilot Study on Gait Classification Using fNIRS SignalsHedian Jin0Chunguang Li1Jiacheng Xu2Key Laboratory of Robotics and System of Jiangsu Province, School of Mechanical and Electric Engineering, Soochow University, Suzhou, ChinaKey Laboratory of Robotics and System of Jiangsu Province, School of Mechanical and Electric Engineering, Soochow University, Suzhou, ChinaKey Laboratory of Robotics and System of Jiangsu Province, School of Mechanical and Electric Engineering, Soochow University, Suzhou, ChinaRehabilitation 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.http://dx.doi.org/10.1155/2018/7403471
collection DOAJ
language English
format Article
sources DOAJ
author Hedian Jin
Chunguang Li
Jiacheng Xu
spellingShingle Hedian Jin
Chunguang Li
Jiacheng Xu
Pilot Study on Gait Classification Using fNIRS Signals
Computational Intelligence and Neuroscience
author_facet Hedian Jin
Chunguang Li
Jiacheng Xu
author_sort Hedian Jin
title Pilot Study on Gait Classification Using fNIRS Signals
title_short Pilot Study on Gait Classification Using fNIRS Signals
title_full Pilot Study on Gait Classification Using fNIRS Signals
title_fullStr Pilot Study on Gait Classification Using fNIRS Signals
title_full_unstemmed Pilot Study on Gait Classification Using fNIRS Signals
title_sort pilot study on gait classification using fnirs signals
publisher Hindawi Limited
series Computational Intelligence and Neuroscience
issn 1687-5265
1687-5273
publishDate 2018-01-01
description 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.
url http://dx.doi.org/10.1155/2018/7403471
work_keys_str_mv AT hedianjin pilotstudyongaitclassificationusingfnirssignals
AT chunguangli pilotstudyongaitclassificationusingfnirssignals
AT jiachengxu pilotstudyongaitclassificationusingfnirssignals
_version_ 1716744704691798017