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Signal identification system for developing rehabilitative device using deep learning algorithms.
- Source :
-
Artificial Intelligence in Medicine . Jan2020, Vol. 102, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- Paralyzed patients were increasing day by day. Some of the neurodegenerative diseases like amyotrophic lateral sclerosis, Brainstem Leison, Stupor and Muscular dystrophy affect the muscle movements in the body. The affected persons were unable to migrate. To overcome from their problem they need some assistive technology with the help of bio signals. Electrooculogram (EOG) based Human Computer Interaction (HCI) is one of the technique used in recent days to overcome such problem. In this paper we clearly check the possibilities of creating nine states HCI by our proposed method. Signals were captured through five electrodes placed on the subjects face around the eyes. These signals were amplified with ADT26 bio amplifier, filtered with notch filter, and processed with reference power and band power techniques to extract features to detect the eye movements and mapped with Time Delay Neural Network to classify the eye movements to generate control signal to control external hardware devices. Our experimental study reports that maximum average classification of 91.09% for reference power feature and 91.55%-for band power feature respectively. The obtained result confirms that band power features with TDNN network models shows better performance than reference features for all subjects. From this outcome we conclude that band power features with TDNN network models was more suitable for classifying the eleven difference eye movements for individual subjects. To validate the result obtained from this method we categorize the subjects in age wise to check the accuracy of the system. Single trail analysis was conducted in offline to identify the recognizing accuracy of the proposed system. The result summarize that band power features with TDNN network models exceed the reference power with TDNN network model used in this study. Through the outcome we conclude that that band power features with TDNN network was more suitable for designing EOG based HCI in offline mode. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SYSTEM identification
*AMYOTROPHIC lateral sclerosis
*MACHINE learning
*DEEP learning
*NOTCH filters
*EYE movements
*EYE tracking
*ELECTRODES
*ASSISTIVE technology
*HUMAN research subjects
*ELECTROOCULOGRAPHY
*BRAIN-computer interfaces
*PRODUCT design
*SIGNAL processing
*NEURODEGENERATION
*REHABILITATION
*ALGORITHMS
RESEARCH evaluation
Subjects
Details
- Language :
- English
- ISSN :
- 09333657
- Volume :
- 102
- Database :
- Academic Search Index
- Journal :
- Artificial Intelligence in Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 141319888
- Full Text :
- https://doi.org/10.1016/j.artmed.2019.101755