Electromyography (EMG) measures muscle response or electrical activity triggered by a nerve as it stimulates a muscle. The most effective method of EMG uses intramuscular, needle-like electrodes precisely detect changes in muscular action potentials with high accuracy; however, because the scope of the project is limited by time, budget, and safety, we will instead be using surface EMG, which can be used to achieve the same at the cost of accuracy and the number of targetable muscles. [1]
2. How should a processed EMG signal data appear?
EMG SIGNAL NOISED AND DENOISED [2]
The above image is an example of an actual EMG signal both before and after it has been denoised. The noise of an EMG signal can be difficult for a computer to analyze and/or use. In controlling our robotic arm it is important to denoise our signal to allow for faster processing and resulting responses and also to allow for smoother control and movement of the arm.
3. What is the unamplified voltage range of a typical signal?
A typical EMG signal at the surface of the skin can range between 100 and 1000 microVolts (uV), where 1 uV = 1x10^-6 V. Since these signals are so minuscule, it is essential to amplify them. [3]
4. How does EMG detect muscle activation inside the body?
Muscular activation is an electrical dipole that runs through a muscle fiber, changing the electrical potential of the membranes of the cells being activated. [3] This electrical potential is target of EMG.
5. How does force exerted affect the EMG measurements?
A motor neuron and control muscle fibers are a motor unit. When the muscle is activated, a temporary action potential is generated, this is called insertion potential. The voltage difference across muscle fibers governed by the synapse can range from 3 to 1600. Inactive muscles do not produce potential changes. On the oscilloscope, this appears as linear electrical silence.
6. How is EMG instrumentation used by the interface?
The design records EMG signals generated by the pronator teres and anterior deltoid during radioulnar pronation and glenohumeral flexion, respectively, then translates the data into MATLAB commands through the USB-6009 to control the robotic arm.
[#] A. Author, "Document title," Webpage name, Source/production information, Date of internet publication. [Format]. Available: internet address. [Accessed: Date of access].
[1] De Luca, Carlo J., "The Use of Surface Electromyography in Biomechanics," Journal Of Applied Biomechanics; 13 (2), 135-163, July 5, 1993. [Online journal article]. Available: http://www.delsys.com/Attachments_pdf/WP_Biomechanics.pdf. [Accessed: May 10, 2014].
[2] "Surface Electromyography Signal Processing and Classification Techniques," Sensors; 13 (9), 12431-12466, Aug. 21, 2013. [Online journal article]. Available: http://www.mdpi.com/1424-8220/13/9/12431. [Accessed: May 24, 2014].
[3] "BMES 642: EMG Analysis," Drexel University School of Biomedical Engineering, Science and Health Systems. [Online]. Available: http://www.biomed.drexel.edu/labs/biomechanics/emg_analy.htm. [Accessed: May 7, 2014].
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