PN Signal Acquisition and Convolution Via the Characters of the Additive Group

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 systemâ€™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.

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METHODS OF ELECTROMAGNETIC INTERFERENCE REDUCTION IN ELECTROCARDIOGRAPHIC SIGNAL ACQUISITION

Environmental Engineering and Management Journal ◽

10.30638/eemj.2011.077 ◽

2011 ◽

Vol 10 (4) ◽

pp. 553-559 ◽

Cited By ~ 5

Author(s):

Narcis Iulian Adochiei ◽

Valeriu David ◽

Ioan Tudosa

Keyword(s):

Electromagnetic Interference ◽

Signal Acquisition ◽

Interference Reduction ◽

Electrocardiographic Signal

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High-sensitive GPS signal acquisition method based on wavelet filtering

Journal of Computer Applications ◽

10.3724/sp.j.1087.2013.02769 ◽

2013 ◽

Vol 33 (10) ◽

pp. 2769-2771

Author(s):

Yanmin LI ◽

Qingming YI ◽

Min SHI

Keyword(s):

Signal Acquisition ◽

Wavelet Filtering ◽

Gps Signal Acquisition ◽

Acquisition Method

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Review on Electromyography Signal Acquisition, Processing and Its Applications

SSRN Electronic Journal ◽

10.2139/ssrn.3598928 ◽

2020 ◽

Author(s):

Ninad Mehendale ◽

Vidhi Gohel

Keyword(s):

Signal Acquisition

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

The Open Biomedical Engineering Journal ◽

10.2174/1874120701206010005 ◽

2012 ◽

Vol 6 (1) ◽

pp. 5-15 ◽

Cited By ~ 2

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.

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