Research on Gesture Based on Genetic Algorithms - Support Vector Machine

Gong, Yulin; Hu, Mingjia; Chen, Xiaojuan; Sun, Yunpeng

doi:10.1109/iciibms46890.2019.8991504

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…A five-fold cross-validation method is used, and the classification accuracy of each cross-validation is involved in the calculation of the feature weights, to improve the accuracy of the weights. Finally, the extracted motion features and sEMG features are fused and selected by applying the W-CVFS method, and the final feature selection results are obtained and compared for the classification effect, and the classifier is selected as SVM [20] . After the experimental comparison, when using SVM classification, the W-CVFS method was able to select the more important features that had a greater impact on the results, and the classification accuracy of the stroke patient class was higher, and the classification effect was better than that of mRMR and ILFS methods.…”

Section: Resultsmentioning

confidence: 99%

Fusion classification of stroke patients' biosignals by weighted cross-validation-based feature selection (W-CVFS) method

Chen

Pan

et al. 2023

Biomedical Signal Processing and Control

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Section: Resultsmentioning

confidence: 99%

Fusion classification of stroke patients' biosignals by weighted cross-validation-based feature selection (W-CVFS) method

Chen

Pan

et al. 2023

Biomedical Signal Processing and Control

Self Cite

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“…In this context, many works have been conducted in recent years with new and auspicious strategies for both functions (classification and regression). They can be divided into: propositions for proportional control (Anam et al 2019;Belyea et al 2019;Martinez et al 2020;Wang et al 2020;Yang et al 2019b;Yu et al 2020b), strategies designed around classical classification methodologies (Belyea et al 2019;DelPreto & Rus 2020;Donati et al 2019;Gong et al 2019;Guo et al 2019;Mantilla-Brito et al 2020;Moin et al 2019;Shin et al 2020;Vasanthi & Jayasree 2020), deep learning methods (Ameri et al 2020;Asif et al 2020;Chen et al 2020b;Côté-Allard et al 2020;Huang & Chen 2019;Kim et al 2020;Liu et al 2019;Mukhopadhyay & Samui 2020;Olsson et al 2019a;Olsson et al 2020;Olsson et al 2019b;Rahimian et al 2019;Shao et al 2020;Yamanoi et al 2020;Yang et al 2019a;Zanghieri et al 2020) and research associated to the refinement of the classifier output also called post-processing (Ahmed et al 2019;Cene et al 2019b;Jafarzadeh et al 2019;Yu et al 2020a).…”

Section: Classification / Regressionmentioning

confidence: 99%

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Tosin

Machado²,

Balbinot³

2022

jair

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Despite achieving accuracies higher than 90% on recognizing upper-limb movements through sEMG (surface Electromyography) signal with the state of art classifiers in the laboratory environment, there are still issues to be addressed for a myo-controlled prosthesis achieve similar performance in real environment conditions. Thereby, the main goal of this review is to expose the latest researches in terms of strategies in each block of the system, giving a global view of the current state of academic research. A systematic review was conducted, and the retrieved papers were organized according to the system step related to the proposed method. Then, for each stage of the upper limb motion recognition system, the works were described and compared in terms of strategy, methodology and issue addressed. An additional section was destined for the description of works related to signal contamination that is often neglected in reviews focused on sEMG based motion classifiers. Therefore, this section is the main contribution of this paper. Deep learning methods are a current trend for classification stage, providing strategies based on time-series and transfer learning to address the issues related to limb position, temporal/inter-subject variation, and electrode displacement. Despite the promising strategies presented for contaminant detection, identification, and removal, there are still some factors to be considered, such as the occurrence of simultaneous contaminants. This review exposes the current scenario of the movement classification system, providing valuable information for new researchers and guiding future works towards myo-controlled devices.

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Fusion Classification of Stroke Patients' Biosignals by Weighted Cross-Validation-based Feature Selection (W-CVFS) Method

Pan

Chen

Ji³

et al. 2022

Preprint

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A multi-source information fusion-based disease class classification of stroke patients was implemented to address the low classification accuracy of pure input motion and electromyographic signals. sEMG sensor MYO arm ring and wearable wireless motion sensor Shimmer were used as data acquisition devices. The Butterworth high-pass filter filtering and envelope thresholding method detected the activity segment. Detection and FIR filtering using the window function method remove interference from the motion signal. A weighted cross-validation-based feature selection (W-CVFS) method is proposed for feature fusion selection. The top 10 features selected by the W-CVFS method and all 18 features are input to the deep neural network for training and testing, and the feature classification result of the W-CVFS method is 79.17%, which is better than the existing mRMR method (66.67%) and ILFS method (62.50%). The classification accuracy of multi-source information fusion was 95.385%, which was higher than that of a single input motion signal or sEMG. The experiments showed that the proposed method can retain the features that have more influence on the classification results and can improve the classification accuracy of the rehabilitation model for stroke patients.

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Research on Gesture Based on Genetic Algorithms - Support Vector Machine

Cited by 3 publications

References 6 publications

Fusion classification of stroke patients' biosignals by weighted cross-validation-based feature selection (W-CVFS) method

Fusion classification of stroke patients' biosignals by weighted cross-validation-based feature selection (W-CVFS) method

sEMG-Based Upper Limb Movement Classifier: Current Scenario and Upcoming Challenges

Fusion Classification of Stroke Patients' Biosignals by Weighted Cross-Validation-based Feature Selection (W-CVFS) Method

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