Background: The transmission of human body movements to other devices through wearable smart bracelets have attracted more and more attentions in the field of human-machine interface (HMI) applications. However, due to the limitation of the collection range of wearable bracelets, it is necessary to study the relationship between the superposition of wrist and finger motion and their cooperative motion to simplify the collection system of the device.Methods: The multi-channel high-density surface electromyogram (HD-sEMG) signal has high spatial resolution and can improve the accuracy of multi-channel fitting. In this study, we quantified the HD-sEMG forearm spatial activation features of 256 channels of hand movement, and performed a linear fitting of the quantified features of fingers and wrist movements to verify the linear superposition relationship between fingers and wrist cooperative movements and their independent movements. The most important thing is to classify and predict the results of the fitting and the actual measured fingers and wrist cooperative actions by four commonly used classifiers: Linear Discriminant Analysis (LDA) ,K-Nearest Neighbor (KNN) ,Support Vector Machine (SVM) and Random Forest (RF), and evaluate the performance of the four classifiers in gesture fitting in detail according to the classification results.Results: In a total of 12 kinds of synthetic gesture actions, in the three cases where the number of fitting channels was selected as 8, 32 and 64, four classifiers of LDA, SVM, RF and KNN are used for classification prediction. When the number of fitting channels was 8, the prediction accuracy of LDA classifier was 99.70%, the classification accuracy of KNN was 99.40%, the classification accuracy of SVM was 99.20%, and the classification accuracy of RF was 93.75%. When the number of fitting channels was 32, the accuracy of LDA was 98.51%, the classification accuracy of KNN was 97.92%, the accuracy of SVM is 96.73%, and the accuracy of RF was 86.61%. When the number of fitting channels is 64, the accuracy of LDA is 95.83%, the classification accuracy of KNN is 91.67%, the accuracy of SVM is 86.90%, and the accuracy of RF is 83.30%.Conclusion: It can be seen from the results that when the number of fitting channels is 8, the classification accuracy of the three classifiers of LDA, KNN and SVM is basically the same, but the time-consuming of SVM is very small. When the amount of data is large, the priority should be selected SVM as the classifier. When the number of fitting channels increases, the classification accuracy of the LDA classifier will be higher than the other three classifiers, so the LDA classifier should be more appropriate. The classification accuracy of the RF classifier in this type of problem has always been far lower than the other three classifiers, so it is not recommended to use the RF classifier as a classifier for gesture stacking related work.
The authors have retracted this conference paper because, during the pre-processing of the dataset in this study, the training set, test set and validation set were normalised together. As a result, the authors no longer have confidence in the veracity of the results and conclusions presented.
The transmission of human body movement signals to other devices through wearable smart bracelets has attracted increasing attention in the field of human-machine interfaces. However, owing to the limited data collection range of wearable bracelets, it is necessary to study the relationship between the superposition of the wrist and fingers and their cooperative motions to simplify the data collection system of such devices. Multichannel high-density surface electromyogram (HD-sEMG) signals exhibit high spatial resolutions, and they can help improve the accuracy of the multichannel fitting. In this study, we quantified the HD-sEMG forearm spatial activation features of 256 channels of hand movement and performed a linear fitting of the data obtained for finger and wrist movements in order to verify the linear superposition relationship between the cooperative and independent movements of the wrist and fingers. This study aims to classify and predict the results of the fitting and measured fingers and wrist cooperative actions using four commonly adopted classifiers and evaluate the performance of the classifiers in gesture fitting. The results indicated that linear discriminant analysis affords the highest classification performance, whereas the random forest method achieved the worst performance. This study can serve as a guide for gesture signal simplification in the future.
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