Classification of desired motion speed-based on cerebral hemoglobin information

Sui, Yanxiang; Li, Chunguang; Li, Juan; Guo, Hao; Zhang, Hongmiao; Jin, Hedian

doi:10.1109/sii.2016.7844018

Cited by 3 publications

(6 citation statements)

References 15 publications

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“…Therefore, more subbands and the combination of multiple subbands are more conducive to characterize the differences between motion states. Moreover, to reduce the error caused by the subjects' different skull sizes and intersubject variability, the extraction of feature vectors focused on the key channels with the maximum or minimum values instead of focusing on fixed channels [22][23][24], and the channels were subdivided into more regions. Even though the number of features will increase a lot accordingly, the features will be simplified by statistical analysis when searching the common features among multistates in another dimension and different features between states in the current dimension (to be identified).…”

Section: Discussionmentioning

confidence: 99%

“…The subjects were instructed to walk under six given sets of gait states (small step (SP) with low/midspeed (LD/MD), midstep (MP) with low/mid/high speed (LD/MD/HD), and large step (LP) with midspeed (MD)). During the walking experiment, the subjects' total hemoglobin (totalHb), oxygenated hemoglobin (oxyHb), and deoxygenated hemoglobin (deoxyHb) levels were measured by a FORIE-3000 optical topography system [ 22 ]. This system consists of eight emitters and eight detectors and measures wavelengths of 780 nm, 805 nm, and 830 nm.…”

Section: Methodsmentioning

confidence: 99%

“…For example, Kim et al and Mihara et al found that the activation of the sensorimotor cortex (SMC), premotor cortex (PMC), and supplementary motor area (SMA) is largely associated with an increase in walking speed [19,20], and the main activation area due to changes in the step length is believed to be the prefrontal cortex (PFC) [21]. Simultaneously, research on the classification of multiple gait motion states has also made strong progress [22][23][24]. These studies have focused on step length and walking speed, but there is little research on the simultaneous recognition of these two parameters.…”

Section: Introductionmentioning

confidence: 99%

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Classifying Motion Intention of Step Length and Synchronous Walking Speed by Functional Near-Infrared Spectroscopy

Zhu¹,

Li²,

Jin³

et al. 2021

Cyborg Bionic Syst

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In some patients who have suffered an amputation or spinal cord injury, walking ability may be degraded or deteriorated. Helping these patients walk independently on their own initiative is of great significance. This paper proposes a method to identify subjects’ motion intention under different levels of step length and synchronous walking speed by using functional near-infrared spectroscopy technology. Thirty-one healthy subjects were recruited to walk under six given sets of gait parameters (small step with low/midspeed, midstep with low/mid/high speed, and large step with midspeed). The channels were subdivided into more regions. More frequency bands (6 subbands on average in the range of 0-0.18 Hz) were decomposed by applying the wavelet packet method. Further, a genetic algorithm and a library for support vector machine algorithm were applied for selecting typical feature vectors, which were represented by important regions with partial important channels mentioned above. The walking speed recognition rate was 71.21% in different step length states, and the step length recognition rate was 71.21% in different walking speed states. This study explores the method of identifying motion intention in two-dimensional multivariate states. It lays the foundation for controlling walking-assistance equipment adaptively based on cerebral hemoglobin information.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classifying Motion Intention of Step Length and Synchronous Walking Speed by Functional Near-Infrared Spectroscopy

Zhu¹,

Li²,

Jin³

et al. 2021

Cyborg Bionic Syst

Self Cite

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“…A FOIRE-3000 optical topography system (Shimadzu Corporation, Kyoto, Japan) [ 32 ] with eight emitters and eight detectors was used to measure the light sources of wavelengths of 830 nm, 805 nm, and 780 nm, that represent the oxygenated hemoglobin (oxyHb), total hemoglobin (totalHb), and deoxygenated hemoglobin (deoxyHb), respectively. The sampling period of hemoglobin signals was 130 ms.…”

Section: Experiments Designmentioning

confidence: 99%

“…In addition, fNIRS technology for the identification of similar patterns also has great application prospects. Sui et al [ 32 ] identified three levels of bicycling speed, based on the difference of oxyHb and deoxyHb, with a corresponding classification accuracy of 74%. Hong et al [ 33 ] identified mental arithmetic (MA), right-hand motor imagery (RI), and left-hand motor imagery (LI) with an average classification accuracy of 75.6% across ten subjects.…”

Section: Introductionmentioning

confidence: 99%

Pilot Study on Gait Classification Using fNIRS Signals

Jin

2018

Computational Intelligence and Neuroscience

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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.

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