Abstract
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.
Highlights
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 surface electromyography (sEMG)
We utilized a dataset name “Dataset 01” to verify the feasibility of primary taste sensation qualitative recognizing with sEMG
Random forest was performed on Dataset 01, giving a five-fold cross-validation accuracy of 74.46%
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In the taste sensation recognition field, an EEG could be utilized to analyze the effect of taste stimuli from food, such as flavorful creams, tomato sauce, and wine, etc. Human facial muscle movements and salivary glands activity could be recorded with non-invasive electrodes in an sEMG system [25,26,27]. In the taste sensation recognition field, most research has focused on analyzing the variation of sEMG after taste stimuli. Research on taste sensation recognition based on sEMG is still a blank. 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. The sEMG signals were preprocessed, feature extracted, and pattern recogMaterials and Methods nized
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