Analysis of Electromyographic Signals from Rats’ Stomaches for Detection and Classification of Motility

This paper deals with the statistical analysis and pattern classification of electromyographic signals from the biceps of a person with amputation below the humerus. Such signals collected from an amputation simulator are synergistically generated to produce discrete elbow movements. The purpose of this study is to utilise these signals to control an electrically driven prosthetic or orthotic elbow with minimum extra mental effort on the part of the subject. The results show very good separability of classes of movements when a learning pattern classification scheme is used, and a superposition of any composite motion to the three basic primitive motions—humeral rotation in and out, flexion and extension, and pronation and supination. Since no synergy was detected for the wrist movement, different inputs have to be provided for a grip. In addition, the method described is not limited by the location of the electrodes. For amputees with shorter stumps, synergistic signals could be obtained from the shoulder muscles. However, the presentation in this paper is limited to biceps signal classification only.

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Acquisition and processing of electromyographic signals for the control of a virtual vehicle in real time

Respuestas ◽

10.22463/0122820x.2402 ◽

2020 ◽

Vol 25 (1) ◽

pp. 25-32

Author(s):

José Luis Carrero-Carrero ◽

Luis Enrique Mendoza ◽

Zulmary Nieto-Sánchez

Keyword(s):

Wavelet Analysis ◽

Fourier Analysis ◽

Video Game ◽

Digital Filtering ◽

Left Turn ◽

Graphic Interface ◽

Virtual Vehicle ◽

Electromyographic Signals ◽

Error Percentage

This work presents the registration and classification of the electromyographic (EMG) signals of the lower extremities, specifically of the gross muscle, in order to control a virtual vehicle designed in Blender. The system has 4 channels, with a graphic interface, which allows the control of a virtual vehicle. For the processing of the signals, different mathematical tools were used such as: Fourier analysis and wavelet analysis. These techniques were used in order to compress data, obtain characteristic patterns in each set of signals and perform digital filtering. The control of the car consists of 4 commands such as: accelerate, stop, right turn and left turn, which are the basic instructions for the real operation of a car. The results showed that it is possible to use biological signals to perform virtual controls (video game). Likewise, it was verified that the parameterization found for each group of EMG signals was satisfactory, since the percentage of errors of the 4 variables studied was 0.04% for a total of 400 executions. This error percentage corroborates that the system has great potential for possible future applications.

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Comparison of AM-FM Features with Standard Features for the Classification of Surface Electromyographic Signals

XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010 - IFMBE Proceedings ◽

10.1007/978-3-642-13039-7_18 ◽

2010 ◽

pp. 69-72

Author(s):

C. I. Christodoulou ◽

P. A. Kaplanis ◽

V. Murray ◽

M. S. Pattichis ◽

C. S. Pattichis

Keyword(s):

Electromyographic Signals

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Electromyographic Grasp Recognition for a Five Fingered Robotic Hand

IAES International Journal of Robotics and Automation (IJRA) ◽

10.11591/ijra.v2i1.pp1-10 ◽

2012 ◽

Vol 2 (1) ◽

pp. 1

Author(s):

Nayan M. Kakoty ◽

Mantoo Kaiborta ◽

Shyamanta M. Hazarika

Keyword(s):

Daily Living ◽

Wavelet Decomposition ◽

Recognition Rate ◽

Support Vector ◽

Robotic Hand ◽

Radial Basis Function Kernel ◽

Kernel Support Vector Machine ◽

Average Recognition Rate ◽

Electromyographic Signals

This paper presents classification of grasp types based on surface electromyographic signals. Classification is through radial basis function kernel support vector machine using sum of wavelet decomposition coefficients of the EMG signals. In a study involving six subjects, we achieved an average recognition rate of 86%. The electromyographic grasp recognition together with a 8-bit microcontroller has been employed to control a five<br />fingered robotic hand to emulate six grasp types used during 70% daily living activities.<br /><br />

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