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Abstract
The electromyographic activity of muscles was measured using a wireless biofeedback device. The aim of the study was to examine the possibility of creating an automatic muscle tension classifier. Several measurement series were conducted and the participant performed simple physical exercises - forcing the muscle to increase its activity accordingly to the selected scale. A small wireless device was attached to the electrodes placed on the patient's body in the area of biceps muscle. The patient body position, electrode placement and performed exercises were the features that as much as possible, minimized the impact of the surrounding muscles influence. The data were recorded and an analysis was made using QT /C++ environment. The exercises were designed to enable evaluation of muscle activity according to Lovett scale. The aim of the research described in this article was to help in the assessing of the muscle strength tension to assess progress in rehabilitation. The designed feed-forward neural network allowed classification of recorded signals with 78% accuracy.
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- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Title of issue:
- 2018 11th International Conference on Human System Interaction (HSI) strony 255 - 260
- Language:
- English
- Publication year:
- 2018
- Bibliographic description:
- Toczko H., Troka P., Przystup P., Kocejko T., Krzyżanowski P., Kaczmarek M.: ReFlexeNN - the Wearable EMG Interface with Neural Network Based Gesture Classification// 2018 11th International Conference on Human System Interaction (HSI)/ : , 2018, s.255-260
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/hsi.2018.8431188
- Verified by:
- Gdańsk University of Technology
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