Review on Electromyography Signal Acquisition, Processing and Its Applications

2020 ◽  
Author(s):  
Ninad Mehendale ◽  
Vidhi Gohel
Keyword(s):  
Signal Acquisition

A New Robust GPS Satellite Signal Acquisition Using Lifting Wavelet

2005 ◽  
Vol 63 (5) ◽  
pp. 389-403 ◽  
Author(s):  
D. Djebouri ◽  
A. Djebbari ◽  
M. Djebbouri
Keyword(s):  
Signal Acquisition ◽  
Satellite Signal ◽  
Lifting Wavelet

HYBRID CLASSIFICATION SCHEMES FOR HEART MURMUR DETECTION TO ASSIST PHONOCARDIOGRAM BASED SIGNAL ACQUISITION

2017 ◽  
Vol 13 (9) ◽  
pp. 6480-6488 ◽  
Author(s):  
A.D. Jeyarani ◽  
Reena Daphne ◽  
Solomon Roach
Keyword(s):  
Neural Network ◽  
Signal Acquisition ◽  
Heart Murmur ◽  
Classification Techniques ◽  
Classification Schemes ◽  
Neural Network Classification ◽  
Nonlinear Classification ◽  
Processing Step ◽  
System Characterization ◽  
Hybrid Classification

The main contribution of this paper has been to introduce nonlinear classification techniques to extract more information from the PCG signal. Especially, Artificial Neural Network classification techniques have been used to reconstruct the underlying system’s state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction.

The Development of a Myoelectric Training Tool for Above-Elbow Amputees

2012 ◽  
Vol 6 (1) ◽  
pp. 5-15 ◽  
Author(s):  
Michael R Dawson ◽  
Farbod Fahimi ◽  
Jason P Carey
Keyword(s):  
Degrees Of Freedom ◽  
Learning Task ◽  
Robotic Arm ◽  
Signal Acquisition ◽  
Myoelectric Prosthesis ◽  
Training Tool ◽  
Myoelectric Prostheses ◽  
Training Systems ◽  
Targeted Muscle Reinnervation

The objective of above-elbow myoelectric prostheses is to reestablish the functionality of missing limbs and increase the quality of life of amputees. By using electromyography (EMG) electrodes attached to the surface of the skin, amputees are able to control motors in myoelectric prostheses by voluntarily contracting the muscles of their residual limb. This work describes the development of an inexpensive myoelectric training tool (MTT) designed to help upper limb amputees learn how to use myoelectric technology in advance of receiving their actual myoelectric prosthesis. The training tool consists of a physical and simulated robotic arm, signal acquisition hardware, controller software, and a graphical user interface. The MTT improves over earlier training systems by allowing a targeted muscle reinnervation (TMR) patient to control up to two degrees of freedom simultaneously. The training tool has also been designed to function as a research prototype for novel myoelectric controllers. A preliminary experiment was performed in order to evaluate the effectiveness of the MTT as a learning tool and to identify any issues with the system. Five able-bodied participants performed a motor-learning task using the EMG controlled robotic arm with the goal of moving five balls from one box to another as quickly as possible. The results indicate that the subjects improved their skill in myoelectric control over the course of the trials. A usability survey was administered to the subjects after their trials. Results from the survey showed that the shoulder degree of freedom was the most difficult to control.

scholarly journals Identification and Quantification of Olive Oil Quality Parameters Using an Electronic Nose

Agriculture ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 674
Author(s):  
Nawaf Abu-Khalaf
Keyword(s):  
Olive Oil ◽  
Electronic Nose ◽  
Chemical Sensor ◽  
Chemical Constituents ◽  
Oil Quality ◽  
Quality Parameters ◽  
Signal Acquisition ◽  
Good Precision ◽  
Olive Oil Quality ◽  
Quality Categories

An electronic nose (EN), which is a kind of chemical sensor, was employed to check olive oil quality parameters. Fifty samples of olive oil, covering the four quality categories extra virgin, virgin, ordinary virgin and lampante, were gathered from different Palestinian cities. The samples were analysed chemically using routine tests and signals for each chemical were obtained using EN. Each signal acquisition represents the concentration of certain chemical constituents. Partial least squares (PLS) models were used to analyse both chemical and EN data. The results demonstrate that the EN was capable of modelling the acidity parameter with a good performance. The correlation coefficients of the PLS-1 model for acidity were 0.87 and 0.88 for calibration and validation sets, respectively. Furthermore, the values of the standard error of performance to standard deviation (RPD) for acidity were 2.61 and 2.68 for the calibration and the validation sets, respectively. It was found that two principal components (PCs) in the PLS-1 scores plot model explained 86% and 5% of EN and acidity variance, respectively. PLS-1 scores plot showed a high performance in classifying olive oil samples according to quality categories. The results demonstrated that EN can predict/model acidity with good precision. Additionally, EN was able to discriminate between diverse olive oil quality categories.

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