Authors :
Seyed Masoud Ghoreishi Mokri; Newsha Valadbeygi; Khafaji Mohammed Balyasimovich
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/t783bzdt
Scribd :
https://tinyurl.com/d8feb25m
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR754
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Measuring power transmission in organs
poses a significant challenge for researchers in the field,
with various methods being explored, including the use
of artificial intelligence algorithms. This study focused
on developing a new neural network model to predict
force transmission and performance in an artificial
elbow. Rather than evaluating natural joints, the study
simulated a prosthetic model using medical software.
Empirical data was collected using MIMICS software to
estimate power properties and transmission methods,
which were then used to train a neural network in
MATLAB. The neural network demonstrated strong
performance, particularly with the use of CNN
architecture. The model's accuracy was validated by
comparing results with experimental data from Anatomy
and Physiology Comparison software, showing that the
neural network provided precise results.
Keywords :
Power Transmission, Anatomy and Physiology, Matlab, CNN Neural Network, Dynamic and Cinematic Power.
Measuring power transmission in organs
poses a significant challenge for researchers in the field,
with various methods being explored, including the use
of artificial intelligence algorithms. This study focused
on developing a new neural network model to predict
force transmission and performance in an artificial
elbow. Rather than evaluating natural joints, the study
simulated a prosthetic model using medical software.
Empirical data was collected using MIMICS software to
estimate power properties and transmission methods,
which were then used to train a neural network in
MATLAB. The neural network demonstrated strong
performance, particularly with the use of CNN
architecture. The model's accuracy was validated by
comparing results with experimental data from Anatomy
and Physiology Comparison software, showing that the
neural network provided precise results.
Keywords :
Power Transmission, Anatomy and Physiology, Matlab, CNN Neural Network, Dynamic and Cinematic Power.
