Operator Attitude Algorithm for an Isomorphic Master-slave Telerobotic System

Zuo, Guoyu; Shuangyue, Yu; Gong, Daoxiong; Lu, Jiahao; Qiu, Yongkang

doi:10.1109/cyber.2017.8446211

Cited by 2 publications

(2 citation statements)

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“…In Figure 4, four classes of 1v3, 1v5, 1v6, and 3v4 in the experiment are taken as examples to show the classification accuracy curve of the feature subset selected by the F-score-SVM, DFS-SVM, and FSJF-SVM methods 5,10,18,23,31,36,44,49,24,21,16,37,8,34,22,29,19,13,26,11,25,35,47,32,3,20,9,38,15,14,1,46,2,12,39,17,33,45,6,27,28 1.26293142 43 50, 5,10,18,23,31,36,44,49,24,21,16,37,8,34,...…”

Section: Comparison Experiments With Sfs Methodsmentioning

confidence: 99%

“…The motion data of the rehabilitation training are obtained by a wearable human motion acquisition system we developed to acquire the motion data of each joint of human body. 21 Figure 2 shows the wearing demonstration of the acquisition system, which consists of eight inertial measurement modules which include one head, one waist (or back), two forearms, two upper arms, and two hand modules, respectively. Two data gloves can acquire the actions of 10 fingers, but they are not used in this article.…”

Section: Experimental Designmentioning

confidence: 99%

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

Zuo

et al. 2019

International Journal of Distributed Sensor Networks

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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.

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