scholarly journals Research on fNIRS Recognition Method of Upper Limb Movement Intention

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1239
Author(s):  
Chunguang Li ◽  
Yongliang Xu ◽  
Liujin He ◽  
Yue Zhu ◽  
Shaolong Kuang ◽  
...  
Keyword(s):  
Upper Limb ◽  
Recognition Accuracy ◽  
Limb Movement ◽  
Gradient Boosting ◽  
Support Vector ◽  
Blood Oxygen ◽  
Recognition Method ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation ◽  
Movement Intention

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.

scholarly journals Emergence of Virtual Reality as a Tool for Upper Limb Rehabilitation: Incorporation of Motor Control and Motor Learning Principles

2015 ◽  
Vol 95 (3) ◽  
pp. 415-425 ◽  
Author(s):  
Mindy F. Levin ◽  
Patrice L. Weiss ◽  
Emily A. Keshner
Keyword(s):  
Virtual Reality ◽  
Motor Control ◽  
Motor Learning ◽  
Upper Limb ◽  
Limb Movement ◽  
Motor Recovery ◽  
Upper Limb Rehabilitation ◽  
Wide Range ◽  
Learning Principles ◽  
Limb Rehabilitation

The primary focus of rehabilitation for individuals with loss of upper limb movement as a result of acquired brain injury is the relearning of specific motor skills and daily tasks. This relearning is essential because the loss of upper limb movement often results in a reduced quality of life. Although rehabilitation strives to take advantage of neuroplastic processes during recovery, results of traditional approaches to upper limb rehabilitation have not entirely met this goal. In contrast, enriched training tasks, simulated with a wide range of low- to high-end virtual reality–based simulations, can be used to provide meaningful, repetitive practice together with salient feedback, thereby maximizing neuroplastic processes via motor learning and motor recovery. Such enriched virtual environments have the potential to optimize motor learning by manipulating practice conditions that explicitly engage motivational, cognitive, motor control, and sensory feedback–based learning mechanisms. The objectives of this article are to review motor control and motor learning principles, to discuss how they can be exploited by virtual reality training environments, and to provide evidence concerning current applications for upper limb motor recovery. The limitations of the current technologies with respect to their effectiveness and transfer of learning to daily life tasks also are discussed.

scholarly journals Feature subset evaluation method for upper limb rehabilitation training based on joint feature discernibility

2019 ◽  
Vol 15 (3) ◽  
pp. 155014771983846
Author(s):  
Guoyu Zuo ◽  
Zhaokun Xu ◽  
Jiahao Lu ◽  
Daoxiong Gong
Keyword(s):  
Support Vector Machine ◽  
Upper Limb ◽  
Evaluation Method ◽  
Support Vector ◽  
Feature Subset ◽  
Fisher Score ◽  
Upper Limb Rehabilitation ◽  
Rehabilitation Training ◽  
Limb Rehabilitation ◽  
Candidate Feature

A feature subset discernibility hybrid evaluation method using Fisher score based on joint feature and support vector machine is proposed for the feature selection problem of the upper limb rehabilitation training motion of Brunnstrom 4–5 stage patients. In this method, the joint feature is introduced to evaluate the discernibility between classes due to the joint effect of both candidate and selected features. A feature subset search strategy is used to search a set of candidate feature subsets. The Fisher score based on joint feature method is used to evaluate the candidate feature subsets and the best subset is selected as a new selected feature subset. From these selected subsets such as obtained by the above process, the subset with the best performance of support vector machine classification is finally selected as the optimal feature subset. Experiments were carried out on the upper limb routine rehabilitation training samples of the Brunnstrom 4–5 stage. Compared with both the F-score and the discernibility of feature subset methods, the experimental results show the effectiveness and feasibility of the proposed method which can obtain the feature subsets with higher accuracy and smaller feature dimension.

Towards a Gaze-Informed Movement Intention Model for Robot-Assisted Upper-Limb Rehabilitation

2021 ◽  
Author(s):  
Vincent Crocher ◽  
Ronal Singh ◽  
Joshua Newn ◽  
Denny Oetomo
Keyword(s):  
Upper Limb ◽  
Robot Assisted ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation ◽  
Movement Intention

scholarly journals The Association between Mental Motor Imagery and Real Movement in Stroke

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1568
Author(s):  
Ana Poveda-García ◽  
Carmen Moret-Tatay ◽  
Miguel Gómez-Martínez
Keyword(s):  
Motor Imagery ◽  
Upper Limb ◽  
Daily Living ◽  
Cognitive Disorders ◽  
Limb Movement ◽  
Spatial Skills ◽  
Upper Limb Rehabilitation ◽  
Visual Spatial ◽  
Limb Rehabilitation

Background: Stroke is the main cause of disability in adults; the most common and long-term sequela is upper-limb hemiparesis. Many studies support the idea that mental motor imagery, which is related to the visualization of movement patterns, activates the same areas of the cortex as if the movement occurred. Objectives: This study aims to examine the capacity to elaborate mental motor images, as well as its relationship to loss of movement in the upper limbs after a stroke. Method: An observational study, in a sample of 39 adults who suffered a stroke, was carried out. The upper limb movement and functionality, cognitive disorders, the ability to visualize mental images, and activities of daily living were examined. Results: The results depicted a statistically significant correlation between the ability to visualize upper limb mental motor images with movement, functionality, and strength. In addition, a correlation between visual–spatial skills and mental visualization of motor ability and upper limb movement was found. Conclusions: These results suggest that the rehabilitation approach focused on the improvement of mental motor imagery could be of interest for the upper limb rehabilitation of movement and functionality.

Upper-limb Rehabilitation Robot Based on Motor Imagery EEG

ROBOT ◽  
2011 ◽  
Vol 33 (3) ◽  
pp. 307-313 ◽  
Author(s):  
Baoguo XU ◽  
Si PENG ◽  
Aiguo SONG
Keyword(s):  
Motor Imagery ◽  
Upper Limb ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation

Upper-limb Rehabilitation Robot Motion Control Based on Dynamic Interpolation

ROBOT ◽  
2012 ◽  
Vol 34 (5) ◽  
pp. 539 ◽  
Author(s):  
Lizheng PAN ◽  
Aiguo SONG ◽  
Guozheng XU ◽  
Huijun LI ◽  
Baoguo XU
Keyword(s):  
Motion Control ◽  
Upper Limb ◽  
Robot Motion ◽  
Rehabilitation Robot ◽  
Upper Limb Rehabilitation ◽  
Limb Rehabilitation
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