To classify two-dimensional motion state of step length and walking speed by applying cerebral hemoglobin information

Jin, Haiyan; Chunguagn, Li; Sun, Lining; Hu, Haiyan; Xu, Jiacheng; Qu, Wenyu

doi:10.1109/hsi.2017.8005032

Cited by 1 publication

(3 citation statements)

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Section: Action Initiation Intent Feature Extractionmentioning

confidence: 99%

“…Near-infrared signals are non-stationary and nonlinear. Among a variety of feature categories based on biological information to distinguish resting/exercise status, Teager-Kaiser energy operator and slope value were used in many initial detection studies of biological signals [14,15]. By calculating the Teager-Kaiser energy operator at each time point and the blood oxygen slope characteristics at each time point, the instantaneous changes attributed to the signal at each time point were obtained, and the waveform changes of the measured signal were tracked in real time.…”

Section: Action Initiation Intent Feature Extractionmentioning

confidence: 99%

“…Compared with the traditional RF feature selection algorithm, this paper proposes an improved feature selection method based on RF. For the original feature set, the algorithm first set the final number of retained features, then used the sequential backward search method to remove the features with low importance score from the feature set, until the number of features was the specified number, the algorithm stopped, and the reserved feature subset was taken as the final feature set [14,15].…”

Section: Movement State Feature Selectionmentioning

confidence: 99%

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Research on fNIRS Recognition Method of Upper Limb Movement Intention

et al. 2021

Electronics

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This paper aims at realizing upper limb rehabilitation training by using an fNIRS-BCI system. This article mainly focuses on the analysis and research of the cerebral blood oxygen signal in the system, and gradually extends the analysis and recognition method of the movement intention in the cerebral blood oxygen signal to the actual brain-computer interface system. Fifty subjects completed four upper limb movement paradigms: Lifting-up, putting down, pulling back, and pushing forward. Then, their near-infrared data and movement trigger signals were collected. In terms of the recognition algorithm for detecting the initial intention of upper limb movements, gradient boosting tree (GBDT) and random forest (RF) were selected for classification experiments. Finally, RF classifier with better comprehensive indicators was selected as the final classification algorithm. The best offline recognition rate was 94.4% (151/160). The ReliefF algorithm based on distance measurement and the genetic algorithm proposed in the genetic theory were used to select features. In terms of upper limb motion state recognition algorithms, logistic regression (LR), support vector machine (SVM), naive Bayes (NB), and linear discriminant analysis (LDA) were selected for experiments. Kappa coefficient was used as the classification index to evaluate the performance of the classifier. Finally, SVM classification got the best performance, and the four-class recognition accuracy rate was 84.4%. The results show that RF and SVM can achieve high recognition accuracy in motion intentions and the upper limb rehabilitation system designed in this paper has great application significance.

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Section: Action Initiation Intent Feature Extractionmentioning

confidence: 99%

Section: Action Initiation Intent Feature Extractionmentioning

confidence: 99%

Section: Movement State Feature Selectionmentioning

confidence: 99%

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