System for registaration and processing of a Surface electromyogram

Main Article Content

Арсен Савчук
Борис Іванович Лупина
О. О. Борисов

Abstract

The microelectronic system for the surface electromyogram (EMG) signal registratration including the instrumentation amplifier and the analog filters circuits, the block diagram of the hardware-software complex, the software for signals registration, processing and evaluation of the surface EMG signal has designed and studied. The designed system includes Multifunction Data Acquisition NI USB 6009 and software in the LabVIEW environment. The layout of the hardware-software complex is presented. Special software has designed. The surface EMG signals have been registered, processed and analyzed. The results include the analysis of the self-noise and the signal-to-noise ratio of the amplifier stage, the signals of the surface EMG. The obtained results can be used for the medical diagnosis, sports research and prosthesis. The possibility of autonomous systems developing based on the demonstration board STM32F3 Discovery, suggested possible areas of use in the treatment and analysis of surface EMG signals are analyzed in the work.

Библ. 6, рис. 4.

Article Details

How to Cite
Савчук, А., Лупина, Б. І., & Борисов, О. О. (2016). System for registaration and processing of a Surface electromyogram. Electronics and Communications, 20(5), 58–63. https://doi.org/10.20535/2312-1807.2015.20.5.70059
Section
Biomedical devices and systems

References

Geethanjali, P., Krishna Mohan, Y., Bhaska, P. (2013). A Low-cost EMG-EOG Signal Conditioning System for Brain Computer Interface Applications. International Journal of Engineering and Technology. Vol 5, №3, 2013. p.p. 2268-2271.

Andrea, M., Campanini, I. (2010). Technical Aspects of Surface Electromyography for Clinicians. The Open Rehabilitation Journal, №3. p.p. 98-109.

Thongpanja, S., Phinyomark, A., Phukpattaranont, P., Limsakul, C. (2011). A Feasibility Study of Fatigue and Muscle Contraction Indices Based on EMG Time-dependent Spectral Analysis. I-SEEC 2011, №32. p.p. 239 - 245.

Raisy, C. D., Sharda, Vashisth, Ashok, K. Salhan. (2013). Real Time Acquisition of EMG Signal and Head Movement Recognition. International Journal of Computer Applications, Vol 73, №1. p.p.19-22.

Yiu, Joseph. (2007). The Definitive Guide to the ARM Cortex-M3. ELSEVIER, Р. 330.

Evdokimov, Yu. К., Lindval, V. R., Stcherbakov, G. I. (2007). LabVIEW for radioengineer: from a virtual model to a real device. Practical guide for working in LabVIEW. DMK Press, Р. 400. (Rus).