Remote Control System of Spherical Robot based on Silent Speech Recognition

Guan, Xinping; Zhang, Ming; Wu, Ru Feng; Gao, Han; Ai, Qing; Jin, Song; Wang, You; Li, Guang

doi:10.1109/bci48061.2020.9061648

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…After preprocessing and feature extraction, random forest classifiers were performed and analyzed. As a type of ensemble learning algorithm, random forest is popular for its good performance and generalization and has been widely used on the pattern recognition of sEMG [ 37 , 38 , 39 ]. Performed on a dataset containing 3054 samples with six types of labels, the random forest classifier gave a five-fold cross-validation accuracy of 74.46%, which achieved the original research purpose, i.e., developing a primary taste qualitative recognition method based on sEMG.…”

Section: Introductionmentioning

confidence: 99%

Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography

Wang

et al. 2021

Sensors

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Based on surface electromyography (sEMG), a novel recognition method to distinguish six types of human primary taste sensations was developed, and the recognition accuracy was 74.46%. The sEMG signals were acquired under the stimuli of no taste substance, distilled vinegar, white granulated sugar, instant coffee powder, refined salt, and Ajinomoto. Then, signals were preprocessed with the following steps: sample augments, removal of trend items, high-pass filter, and adaptive power frequency notch. Signals were classified with random forest and the classifier gave a five-fold cross-validation accuracy of 74.46%, which manifested the feasibility of the recognition task. To further improve the model performance, we explored the impact of feature dimension, electrode distribution, and subject diversity. Accordingly, we provided an optimized feature combination that reduced the number of feature types from 21 to 4, a preferable selection of electrode positions that reduced the number of channels from 6 to 4, and an analysis of the relation between subject diversity and model performance. This study provides guidance for further research on taste sensation recognition with sEMG.

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Section: Introductionmentioning

confidence: 99%