Development of Intention Inference Algorithm Based on EMG Signals at Judging Directional of Arrow Cues

2018 ◽  
pp. 405-412
Author(s):  
Yuzo Takahashi
Keyword(s):  
Inference Algorithm ◽  
Intention Inference

A Precise Link Loss Inference Algorithm with Minimal Cover Set

2013 ◽  
Vol 34 (12) ◽  
pp. 2905-2912
Author(s):  
Jing-li Yang ◽  
Yong-hui Xu ◽  
Shou-da Jiang
Keyword(s):  
Inference Algorithm ◽  
Minimal Cover ◽  
Link Loss

Link Loss Inference Algorithm with Nonlinear Programming

2012 ◽  
Vol 34 (6) ◽  
pp. 1425-1431
Author(s):  
Ran Gu ◽  
Xue-song Qiu ◽  
Yan Qiao ◽  
Juan Li ◽  
Luo-ming Meng
Keyword(s):  
Nonlinear Programming ◽  
Inference Algorithm ◽  
Link Loss

Design Intention Inference for Virtual Co-Design Agents

2020 ◽  
Author(s):  
Matthew V. Law ◽  
Amritansh Kwatra ◽  
Nikhil Dhawan ◽  
Matthew Einhorn ◽  
Amit Rajesh ◽  
...  
Keyword(s):  
Intention Inference

Ergonomics analysis based on intention inference

2021 ◽  
pp. 1-16
Author(s):  
First A. Wenbo Huang ◽  
Second B. Changyuan Wang ◽  
Third C. Hongbo Jia
Keyword(s):  
Eye Movement ◽  
Recognition Rate ◽  
Tactile Feedback ◽  
Movement Direction ◽  
Support Vector ◽  
Eeg Signals ◽  
Intention Recognition ◽  
Baseline Removal ◽  
Inference Methods ◽  
Intention Inference

Traditional intention inference methods rely solely on EEG, eye movement or tactile feedback, and the recognition rate is low. To improve the accuracy of a pilot’s intention recognition, a human-computer interaction intention inference method is proposed in this paper with the fusion of EEG, eye movement and tactile feedback. Firstly, EEG signals are collected near the frontal lobe of the human brain to extract features, which includes eight channels, i.e., AF7, F7, FT7, T7, AF8, F8, FT8, and T8. Secondly, the signal datas are preprocessed by baseline removal, normalization, and least-squares noise reduction. Thirdly, the support vector machine (SVM) is applied to carry out multiple binary classifications of the eye movement direction. Finally, the 8-direction recognition of the eye movement direction is realized through data fusion. Experimental results have shown that the accuracy of classification with the proposed method can reach 75.77%, 76.7%, 83.38%, 83.64%, 60.49%,60.93%, 66.03% and 64.49%, respectively. Compared with traditional methods, the classification accuracy and the realization process of the proposed algorithm are higher and simpler. The feasibility and effectiveness of EEG signals are further verified to identify eye movement directions for intention recognition.

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