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Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography
- Source :
- Sensors (Basel, Switzerland), Sensors, Volume 21, Issue 15, Sensors, Vol 21, Iss 4994, p 4994 (2021)
- Publication Year :
- 2021
- Publisher :
- MDPI, 2021.
-
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.
- Subjects :
- Taste
Computer science
02 engineering and technology
Electromyography
TP1-1185
surface electromyography (sEMG)
Biochemistry
Taste sensation
Article
Analytical Chemistry
03 medical and health sciences
primary tastes
0302 clinical medicine
Classifier (linguistics)
0202 electrical engineering, electronic engineering, information engineering
medicine
Feature (machine learning)
Humans
Electrical and Electronic Engineering
Instrumentation
Electrodes
medicine.diagnostic_test
business.industry
Chemical technology
brain-computer interface (BCI)
Pattern recognition
Filter (signal processing)
Atomic and Molecular Physics, and Optics
Random forest
taste sensation recognition
Feature Dimension
020201 artificial intelligence & image processing
Artificial intelligence
business
030217 neurology & neurosurgery
Algorithms
random forest
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 21
- Issue :
- 15
- Database :
- OpenAIRE
- Journal :
- Sensors (Basel, Switzerland)
- Accession number :
- edsair.doi.dedup.....b5a39eae76de4cfc4e0578179b6fa9ad