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...
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Online Access: | http://dx.doi.org/10.1155/2018/7403471 |
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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 |
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