DOI : https://doi.org/10.62527/ijasce.5.2.169

Home Switch Control using Electromyograph and AVR Microcontroller

Lince Markis (1), P. Susetyo Wardana (2), Novi (3)
(1) Department of Electricity Technology, University of 17 Agustus 1945 Surabaya, East Java, Indonesia
(2) Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya, East Java, Indonesia
(3) Department of Information Technology, Politeknik Negeri Padang, West Sumatera, Indonesia
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How to cite (IJASEIT) :
Markis, L., Wardana, P. S., & Novi. (2023). Home Switch Control using Electromyograph and AVR Microcontroller. International Journal of Advanced Science Computing and Engineering, 5(2), 160–169. https://doi.org/10.62527/ijasce.5.2.169
Citation Format :
The increasingly rapid development of technology in the field of biomedical engineering, one of which is applying EMG (Electromyograph) signals to move mechanical devices.  Various studies have been carried out with various methods tested. The research entitled "Home Switch Control using Electromyograph and AVR Microcontroller" is directed at EMG signals in the arm muscles as input to actuate several switch devices which are usually used in homes or hospitals. The results of placing electrodes at 3 points, namely Bicep Brachii, Tricep Brachii, and Wrist Flexor, produce an EMG signal which has 2 different truth values during contraction after extraction using a moving average and thresholding algorithm, so that the final result produces an on-off control system for 1 switch.

Sarbast Rasheed, A Multiclassifier Approach to Motor Unit Potential Classification for EMG Signal Decomposition, Tesis Ph.D., University of Waterloo,Ontario, Canada, 2006.

Jun-Uk Chu, Inhyuk Moon, and Mu Seong Mun, A Real Time EMG Pattern Recognition System Based on Linear-NonLinear Feature Projection for a Multifunction Myoelectric Hand, IEEE Transaction on Biomedical Engineering vol 53 no 11, November 2006.

Ericka Janet Rechy – Ramirez and Huosheng Hu, Stages for Developing Control Systems Using EMG and EEG sinyal: A Survey, Technical Report: CES-513, School of Computer Science and Electronic Engineering, University of Essex, United Kingdom.

Nan Bu, Masaru Okamoto and Toshio Tsuji, A Hybrid Motion Classification Approach for EMG- Based Human – Robot Interfaces Using Bayesian and Neural Network, IEEE Transaction on Robotics vol 25 no 3, June 2009.

T. Moritani, D. Stegeman , R. Merletti, Basic Physiologi and Biophysics of EMG Signal Generation, Institut for Electrical and Electronics Engineerings Inc. 2004.

Toshio Tsuji, Osamu Fukuda, Hiroyuki Ichinobe, and Makoto Kaneko, A Log – Linearized Gaussian Mixture Network and its Application to EEG Pattern Classification, IEEE Transaction on Systems,Man, and Cybernetics – Part C : Applications and Reviews. vol 29 no 1, February 1999.

Biomed.brown.edu/Courses/BI108/BI108_2004_Groups/Group01/bioghj.htm, Glenohumeral Joint.

Shoulder Articulation, http://www.exrx.net/Articulations/Shoulder.html#anchor105322.

Bill Seller, Introduction to EMG, http://mac-huwis.lut.ac.uk/~wis/lectures/.

Angkoon Phinyomark,Chusak Limsakul, and Pornchai Phukpattaranont, A Novel Feature Extraction for Robust EMG Pattern Recognition, Journal of Computing, volume1,ISSUE 1, December 2009, ISSN: 2151-9617.

Frederic M. Ham, Ivica Kostanic, Principles of Neurocomputing for Science & Engineering, McGraw-Hill International Edition, New York, 2001.

Frederic H. Martini, Fundamental of Anatomy & Physiology, fifth edition, Prentice Hall International Edition, New Jersey, 2009.

Curtis D. Johnson, Process Control Instrumentation Technology, fifth edition, Prentice Hall International, New Jersey, 1997.

T. Kugelstadt, Chapter16 Active Filter Design Techniques, Literature Number SLOA088, Texas Instruments (http://www.dee.ufcg.edu.br/~gutemb/Active%20Filter%20Design.pdf).

M. Rivai, Diktat Kuliah Rangkaian Elektronika, Program Pascasarjana Teknik Elektro Bidang Keahlian Teknik Elektronika, ITS, 2010.

Atmel AVR XMega A Manual, www.atmel.com/Images/doc8077.pdf.