Fuzzy Classifier based on Muscle Fatigue Parameters

Author(s):  
V. Agostini ◽  
G. Balestra ◽  
M.F. Norese
1998 ◽  
Vol 30 (2) ◽  
pp. 67-72 ◽  
Author(s):  
Ulla Svantesson, Ulrika Österber
Keyword(s):  

1975 ◽  
Author(s):  
M. M. Ayoub ◽  
H. F. Martz ◽  
Ching H. Wu
Keyword(s):  

2005 ◽  
Author(s):  
Navrag B. Singh ◽  
Maury A. Nussbaum ◽  
Dingding Lin ◽  
Michael L. Madigan

2017 ◽  
Vol 63 (1) ◽  
pp. 95-104
Author(s):  
T.Yu. Matvienko ◽  
◽  
D.A. Zavodovskyi ◽  
D.N. Nozdrenko ◽  
I.V. Mishchenko ◽  
...  
Keyword(s):  

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.


Author(s):  
Evgeniya S. Shitova ◽  
Inga S. Malakhova ◽  
Vladislav I. Lemeshko

Introduction. The use of classical methods for diagnosing muscle fatigue of physical workers, including dynamometry and electromyography, is often limited due to the complexity of the process, the inability to use them in production, and the subjectivity of the methodology. At the same time, such a method as myotonometry does not have these disadvantages, but the main area of its use at the moment is clinical practice. The aim of study was to determine the possibility of using myotonometry to assess muscle fatigue. Materials and methods. In the course of the study, the biomechanical characteristics of muscles that differ in their depth were evaluated using the "MyotonPro" device (Myoton AS, Estonia). We determined the tone and stiffness of the biceps of the shoulder, the soleus muscle, and the muscle that straightens the back at different periods of time under the influence of maximum load on them. Results. It was found that the studied parameters of these muscles immediately after performing a series of exercises "to failure" statistically significantly increased, and after 30 minutes - again decreased, but did not reach the initial level. Conclusions. The myotonometric study showed objectivity, reliability, repeatability and proved to be one of the most promising modern methods for assessing muscle fatigue, including for solving problems in the field of occupational health.


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