Author:
Masoud Ghoreishi Mokri Seyed,Valadbeygi Newsha,G. Stelnikova Irina
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.
Publisher
International Journal of Innovative Science and Research Technology
Reference28 articles.
1. [1]. Mawatari T, Hayashida Y, Katsuragawa S, Yoshimatsu Y, Hamamura T, Anai K, Ueno M, Yamaga S, Ueda I, Terasawa T, Fujisaki A. The effect of deep convolutional neural networks on radiologists' performance in the detection of hip fractures on digital pelvic radiographs. European journal of radiology. 2020 Sep 1;130:109188.
2. [2]. Gale W, Oakden-Rayner L, Carneiro G, Bradley AP, Palmer LJ. Detecting hip fractures with radiologist-level performance using deep neural networks. arXiv preprint arXiv:1711.06504. 2017 Nov 17.
3. [3]. Yang W, Ye Q, Ming S, Hu X, Jiang Z, Shen Q, He L, Gong X. Feasibility of automatic measurements of hip joints based on pelvic radiography and a deep learning algorithm. European Journal of Radiology. 2020 Nov 1;132:109303.
4. [4]. Boniatis I, Costaridou L, Cavouras D, Kalatzis I, Panagiotopoulos E, Panayiotakis G. Assessing hip osteoarthritis severity utilizing a probabilistic neural network based classification scheme. Medical engineering & physics. 2007 Mar 1;29(2):227-37.
5. [5]. Urakawa T, Tanaka Y, Goto S, Matsuzawa H, Watanabe K, Endo N. Detecting intertrochanteric hip fractures with orthopedist-level accuracy using a deep convolutional neural network. Skeletal radiology. 2019 Feb;48:239-44.
Cited by
510 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献