scholarly journals Effect of table top slope and height on body posture and muscular activity pattern

2015 ◽  
Vol 58 (2) ◽  
pp. 86-91 ◽  
Author(s):  
M. Hassaïne ◽  
A. Hamaoui ◽  
P.-G. Zanone
Keyword(s):  
Activity Pattern ◽  
Muscular Activity ◽  
Body Posture

Some Features of the Central Co-Ordination of a Fast Movement in the Crayfish

1968 ◽  
Vol 49 (3) ◽  
pp. 645-656
Author(s):  
ALAN ROBERTS
Keyword(s):  
Activity Pattern ◽  
Abdominal Segment ◽  
Muscular Activity ◽  
Giant Fibre ◽  
Flexor Muscles ◽  
Fast Movement

(i) The nervous and muscular activity pattern produced in an abdominal segment by a single giant-fibre impulse when all peripheral reflexes are abolished is described. (ii) This description depends on criteria for the identification of efferent neurones to the tail muscles. These are discussed and the identification of the inhibitor to the fast flexor muscles is established by anatomical homology. (iii) The pattern of response to a giant-fibre impulse is summarized in Fig. 4. (iv) Inhibition of the fast flexors, the lack of an extension command, the slow flexor contraction, the accessory neurone discharge and central nervous inhibition during the escape movement are among the features discussed.

Effects of 8-week strength training with two models of chest press machines on muscular activity pattern and strength

2008 ◽  
Vol 18 (4) ◽  
pp. 618-627 ◽  
Author(s):  
Angelo Cacchio ◽  
Romildo Don ◽  
Alberto Ranavolo ◽  
Enrico Guerra ◽  
Steven T. McCaw ◽  
...  
Keyword(s):  
Strength Training ◽  
Activity Pattern ◽  
Muscular Activity

Neural Network for Automatic Analysis of Motility Data

1994 ◽  
Vol 33 (01) ◽  
pp. 157-160 ◽  
Author(s):  
S. Kruse-Andersen ◽  
J. Kolberg ◽  
E. Jakobsen
Keyword(s):  
Neural Network ◽  
Muscular Activity ◽  
Recording System ◽  
Automatic Analysis ◽  
Biologically Relevant ◽  
System A ◽  
The Neural Network ◽  
Valuable Method ◽  
Motor Abnormalities ◽  
Trained Network

Abstract:Continuous recording of intraluminal pressures for extended periods of time is currently regarded as a valuable method for detection of esophageal motor abnormalities. A subsequent automatic analysis of the resulting motility data relies on strict mathematical criteria for recognition of pressure events. Due to great variation in events, this method often fails to detect biologically relevant pressure variations. We have tried to develop a new concept for recognition of pressure events based on a neural network. Pressures were recorded for over 23 hours in 29 normal volunteers by means of a portable data recording system. A number of pressure events and non-events were selected from 9 recordings and used for training the network. The performance of the trained network was then verified on recordings from the remaining 20 volunteers. The accuracy and sensitivity of the two systems were comparable. However, the neural network recognized pressure peaks clearly generated by muscular activity that had escaped detection by the conventional program. In conclusion, we believe that neu-rocomputing has potential advantages for automatic analysis of gastrointestinal motility data.

Group exercises aimed at poor body posture correction assisted by humanoid robot - A case study

2019 ◽  
Author(s):  
Aleksandra Mikov ◽  
Dragana Vukliš ◽  
Branislav Borovac ◽  
Milan Gnjatović ◽  
Jovica Tasevski ◽  
...  
Keyword(s):  
Humanoid Robot ◽  
Body Posture
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