Rifky Ismail*
Volume3-Issue5
Dates: Received: 2022-05-24 | Accepted: 2022-05-30 | Published: 2022-05-31
Pages: 664-668
Abstract
Recent demand and interest in patient health monitoring has driven significant interest toward developing varies alternative rehabilitation monitoring instrument. Muscle power can be seen from how many power it’s generate from contraction effort that can be sense by its voltage potential in microvolt order. Surface Electromyogram (EMG) is used to take the record acquisition of muscle power data as patient take the rehabilitation program for some period. In this paper the surface EMG designed to get some level amplification to maintain the data readable and filter to minimize noise that always is the problem from instrument to get data. The aim of the research is to design compact surface EMG device that can be record data development of muscle power over time.
FullText HTML
FullText PDF
DOI: 10.37871/jbres1493
Certificate of Publication
Copyright
© 2022 Ismail R. Distributed under Creative Commons CC-BY 4.0 
How to cite this article
Ismail R. Muscle Power Signal Acquisition Monitoring Using Surface EMG. J Biomed Res Environ Sci. 2022 May 31; 3(5): 664-668. doi: 10.37871/jbres1493, Article ID: JBRES1493, Available at: https://www.jelsciences.com/articles/jbres1493.pdf
Subject area(s)
References
- Jiménez FR, Verlinden O. Review of control algorithms for robotic ankle systems in lower-limb orthoses, prostheses, and exoskeletons. Med Eng Phys. 2012;37:505–511. https://tinyurl.com/26dzmpbr
- Ferris DP, Gordon KE, Sawicki GS, Peethambaran A. An improved powered ankle-foot orthosis using proportional myoelectric control. Gait Posture. 2006 Jun;23(4):425-428. doi: 10.1016/j.gaitpost.2005.05.004. Epub 2005 Aug 10. PMID: 16098749.
- Konrad P. The ABC of EMG, A Practical Introduction to Kinesiological Electromyography. Noraxon Inc Scottsdale, AZ, USA. 2005. https://tinyurl.com/6czyz5zu
- Wan Daud, Yahya, Horng, Sulaima, Sudirman R. Features extraction of electromyography signals in time domain on biceps brachii muscle. International Journal of Modeling and Optimization. Bucharest, Romania. 2013;3(6):515-519. https://tinyurl.com/yc6zrjku
- Raez MB, Hussain MS, Mohd-Yasin F. Techniques of EMG signal analysis: Detection, processing, classification and applications. Biol Proced Online. 2006;8:11-35. doi: 10.1251/bpo115. Epub 2006 Mar 23. Erratum in: Biol Proced Online. 2006;8:163. PMID: 16799694; PMCID: PMC1455479.
- Chowdhury RH, Reaz MB, Ali MA, Bakar AA, Chellappan K, Chang TG. Surface electromyography signal processing and classification techniques. Sensors (Basel). 2013 Sep 17;13(9):12431-12466. doi: 10.3390/s130912431. PMID: 24048337; PMCID: PMC3821366.
- Bin Ahmad Nadzri AA, Ahmad SA, Marhaban MH, Jaafar H. Characterization of surface electromyography using time domain features for determining hand motion and stages of contraction. Australas Phys Eng Sci Med. 2014 Mar;37(1):133-137. doi: 10.1007/s13246-014-0243-3. Epub 2014 Jan 18. PMID: 24443218.
- Di Nardo F, Mengarelli A, Maranesi E, Burattini L, Fioretti S. Assessment of the ankle muscle co-contraction during normal gait: a surface electromyography study. J Electromyogr Kinesiol. 2015 Apr;25(2):347-354. doi: 10.1016/j.jelekin.2014.10.016. Epub 2014 Nov 7. PMID: 25465985.
- Ahmad SA, Chappell PH. Surface EMG pattern analysis of the wrist muscles at different speeds of contraction. J Med Eng Technol. 2009;33(5):376-385. doi: 10.1080/03091900802491246. PMID: 19440916. яндекс
- Jamal MZ. Computational intelligence in electromyography analysis – A perspective on current applications and future challenges. Intech. 2012;18. https://tinyurl.com/yj9td4nn
- Scott Day. Important factors in surface EMG measurement. Bortec Biomedical Incorporated. https://tinyurl.com/4cfjckx2
