A novel human--machine interface based on recognition of multi-channel facial bioelectric signals
| Autor: | Huosheng Hu, S. Mohammad Reza Hashemi Golpayegani, S. Mohammad Firoozabadi, Iman Mohammad Rezazadeh |
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| Rok vydání: | 2010 |
| Předmět: |
Adult
Engineering Adolescent Speech recognition Feature vector Biomedical Engineering Biophysics General Physics and Astronomy Facial Muscles Fuzzy logic User-Computer Interface Fuzzy Logic medicine Cluster Analysis Humans Radiology Nuclear Medicine and imaging Cluster analysis Child business.industry Eye movement Pattern recognition Electroencephalography Signal Processing Computer-Assisted Filter bank Self-Help Devices Facial Expression Facial muscles Electrooculography medicine.anatomical_structure Artificial intelligence business Classifier (UML) Gesture |
| Zdroj: | Australasian physicalengineering sciences in medicine. 34(4) |
| ISSN: | 0158-9938 |
| Popis: | This paper presents a novel human-machine interface for disabled people to interact with assistive systems for a better quality of life. It is based on multi- channel forehead bioelectric signals acquired by placing three pairs of electrodes (physical channels) on the Fron- talis and Temporalis facial muscles. The acquired signals are passed through a parallel filter bank to explore three different sub-bands related to facial electromyogram, electrooculogram and electroencephalogram. The root mean square features of the bioelectric signals analyzed within non-overlapping 256 ms windows were extracted. The subtractive fuzzy c-means clustering method (SFCM) was applied to segment the feature space and generate initial fuzzy based Takagi-Sugeno rules. Then, an adaptive neuro-fuzzy inference system is exploited to tune up the premises and consequence parameters of the extracted SFCMs rules. The average classifier discriminating ratio for eight different facial gestures (smiling, frowning, pulling up left/right lips corner, eye movement to left/right/ up/down) is between 93.04% and 96.99% according to different combinations and fusions of logical features. Experimental results show that the proposed interface has a high degree of accuracy and robustness for discrimination of 8 fundamental facial gestures. Some potential and fur- ther capabilities of our approach in human-machine interfaces are also discussed. |
| Databáze: | OpenAIRE |
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