Authors :
Seyed Masoud Ghoreishi Mokri; Newsha Valadbeygi; Irina G. Stelnikova
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
http://tinyurl.com/swpnfkhj
Scribd :
http://tinyurl.com/cjcdaspf
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24FEB1059
Abstract :
In order to improve the dynamic and
kinematic adaptability of the hip joint, this paper
presented a control attitude and kinematics and torque
of the hip joint with power based neural network control.
The CNN neural network uses input data only from the
limb designed by the medical software, and is trained by
different natural and artificially altered step patterns of
healthy individuals. This type of network has been used
for deep learning to realize adaptive speed control,
dynamic and motion attitude, as well as prediction of
force and torque performance. Detailed movement and
torque tests were performed using MIMICS and
ANATOMY AND PHYSIOLOGY software, and the
obtained data were checked and varied by a healthy
person, and finally, the test results showed that the
neural network control system was able to control the
selection. It has a variable and high speed with proper
adaptation in various conditions. Finally, MATLAB
software was used to design and predict the data of the
problem, and favorable results were obtained.
Keywords :
Kinematic and Dynamic Approach, Rotational Force and Torque, Medical Software, Anatomy, CNN Neural Network, Hip Joint in the Pelvis.
In order to improve the dynamic and
kinematic adaptability of the hip joint, this paper
presented a control attitude and kinematics and torque
of the hip joint with power based neural network control.
The CNN neural network uses input data only from the
limb designed by the medical software, and is trained by
different natural and artificially altered step patterns of
healthy individuals. This type of network has been used
for deep learning to realize adaptive speed control,
dynamic and motion attitude, as well as prediction of
force and torque performance. Detailed movement and
torque tests were performed using MIMICS and
ANATOMY AND PHYSIOLOGY software, and the
obtained data were checked and varied by a healthy
person, and finally, the test results showed that the
neural network control system was able to control the
selection. It has a variable and high speed with proper
adaptation in various conditions. Finally, MATLAB
software was used to design and predict the data of the
problem, and favorable results were obtained.
Keywords :
Kinematic and Dynamic Approach, Rotational Force and Torque, Medical Software, Anatomy, CNN Neural Network, Hip Joint in the Pelvis.
