Application of Convolutional Neural Network in Identifying Different Levels of Isokinetic Exercise Efforts.
10.12116/j.issn.1004-5619.2020.02.012
- Author:
Shao Wen CHEN
1
;
Dan Ni CUI
2
;
Qing XIA
3
;
Wen Tao XIA
3
;
Jie Qing JIANG
1
;
Yi Wen SHEN
1
Author Information
1. Department of Forensic Medicine, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.
2. Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China.
3. Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
- Publication Type:Randomized Controlled Trial
- Keywords:
forensic medicine;
isokinetic exercise;
knee joint;
camouflage;
convolutional neural network
- MeSH:
Humans;
Knee;
Knee Joint;
Muscle Contraction;
Muscle, Skeletal;
Neural Networks, Computer
- From:
Journal of Forensic Medicine
2020;36(2):210-215
- CountryChina
- Language:English
-
Abstract:
Objective To develop a convolutional neural network （CNN） that can identify isokinetic knee exercises moment of force-time diagrams under different levels of efforts. Methods The 200 healthy young volunteers performed concentric isokinetic right knee flexion-extension reciprocating exercises with maximal effort as well as half the effort at angular velocities of 30°/s and 60°/s twice, respectively, with an interval of 45 min. The moment of force-time diagrams were collected. The 200 subjects were randomly divided into the training set （140 subjects） and the testing set （60 subjects）. The moment of force-time diagrams from subjects in the training set were used to train CNN model, and then the fully trained model was used to predict types of curves from the testing set. Random sampling of subjects along with subsequent development of models were performed 3 times. Results Under the circumstances of isokinetic knee exercises with maximal effort and half the effort, 2 400 moment of force-time diagrams were produced, respectively. Classification accuracy rates of the CNN models after the 3 trainings were 91.11%, 90.49% and 92.08%, respectively, and the average accuracy rate was 91.23%. Conclusion The CNN models developed in this study have a good effect on differentiating isokinetic moment of force-time diagrams of maximal effort exercises from those made with half the effort, which can contribute to identifying levels of efforts exerted by subjects during isokinetic knee exercises.
