Bionic ankle-assisted rehabilitation training system based on biomechanical evaluation
Abstract
In modern society, people’s life rhythm is getting faster and faster. Ankle injury would significantly reduce the frequency of people’s activities, which has a great impact on people’s normal work and life. As a new medical method, the bionic ankle rehabilitation training system is used to assist rehabilitation doctors to help patients complete joint flexibility and recovery training. In the research method of ankle biomechanical characteristics, the detection of ankle joint patient’s motion is particularly important. The purpose of this paper is to study how to design a bionic ankle assisted rehabilitation training system based on image processing. Therefore, this paper proposes an image-based moving target detection method, which has the advantages of high reliability and simple operation, and can improve the recognition rate of the ankle joint movement. The experimental results of this paper showed that the system can realize the predetermined trajectory movement and run the system stably. In terms of patient following error, it was kept within 0.03cm. The following error of the ankle joint trajectory was up to 0.03cm and the lowest was 0.01cm, which was almost negligible. In terms of accuracy, the accuracy of the system was also very high, and it can respond and determine the patient’s actions quickly, thereby helping patients to better perform rehabilitation training.
References
1. Wang RL, Zhou ZH, Xi YC, et al. Preliminary study of robot-assisted ankle rehabilitation for children with cerebral palsy. Journal of Peking University. Health sciences. 2018; 50(2):207-212.
2. Pizzolato C, Reggiani M, Saxby DJ, et al. Biofeedback for Gait Retraining Based on Real-Time Estimation of Tibiofemoral Joint Contact Forces. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2017; 25(9): 1612-1621. doi: 10.1109/tnsre.2017.2683488
3. Wagener J, Schweizer C, Zwicky L, et al. Arthroscopically assisted fixation of Hawkins type II talar neck fractures. The Bone & Joint Journal. 2018; 100-B (4): 461-467. doi: 10.1302/0301-620x.100b4.bjj-2017-0772.r3
4. Zhang Q, Kim K, Sharma N. Prediction of Ankle Dorsiflexion Moment by Combined Ultrasound Sonography and Electromyography. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020; 28(1): 318-327. doi: 10.1109/tnsre.2019.2953588
5. Kwon SH, Lee BS, Lee HJ, et al. Energy Efficiency and Patient Satisfaction of Gait With Knee-Ankle-Foot Orthosis and Robot (ReWalk)-Assisted Gait in Patients With Spinal Cord Injury. Annals of Rehabilitation Medicine. 2020; 44(2): 131-141. doi: 10.5535/arm.2020.44.2.131
6. Cheung G, Magli E, Tanaka Y, et al. Graph Spectral Image Processing. Proceedings of the IEEE. 2018; 106(5): 907-930. doi: 10.1109/jproc.2018.2799702
7. Ragan-Kelley J, Adams A, Sharlet D, et al. Halide. Communications of the ACM. 2017; 61(1): 106-115. doi: 10.1145/3150211
8. Frid-Adar M, Diamant I, Klang E, et al. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing. 2018; 321: 321-331. doi: 10.1016/j.neucom.2018.09.013
9. Cheng J. Sparse Range-Constrained Learning and Its Application for Medical Image Grading. IEEE Transactions on Medical Imaging. 2018; 37(12): 2729-2738. doi: 10.1109/tmi.2018.2851607
10. Ding WL, Zheng YZ, Su YP, et al. Kinect-based virtual rehabilitation and evaluation system for upper limb disorders: A case study. Journal of Back and Musculoskeletal Rehabilitation. 2018; 31(4): 611-621. doi: 10.3233/bmr-140203
11. Anderson GB. Enhancing Interpreting Services Delivery within the State/Federal Rehabilitation System. JADARA. 2019; 29(3):8-8.
12. Lee CS, Hou HT, Jiang WW. A DTW-based fast searching heuristic with application on establishing a frozen shoulder rehabilitation system. Applied Science and Management Research. 2019; 6(1):1-17.
13. Kong W, Fu S, Deng B, et al. Embedded BCI Rehabilitation System for Stroke. Journal of Beijing Institute of Technology. 2019; 28(99):39-45.
14. Sun Z, Lu Y, Xu L, Wang L. Bionic Attitude Transformation Combined with Closed Motion for a Free Floating Space Robot. Journal of Beijing Institute of Technology. 2018; 27(1):118-126.
15. Yan Y, Yan H, Yin S, et al. Single/multi-objective optimizations on hydraulic and thermal management in micro-channel heat sink with bionic Y-shaped fractal network by genetic algorithm coupled with numerical simulation. International Journal of Heat and Mass Transfer. 2019; 129: 468-479. doi: 10.1016/j.ijheatmasstransfer.2018.09.120
16. Baydin AG, Pearlmutter BA, Radul AA. Automatic differentiation in machine learning: A survey. Journal of Machine Learning Research. 2018; 18(153):1-43.
17. Lamperti F, Roventini A, Sani A. Agent-based model calibration using machine learning surrogates. Journal of Economic Dynamics and Control. 2018; 90: 366-389. doi: 10.1016/j.jedc.2018.03.011
18. Butler KT, Davies DW, Cartwright H, et al. Machine learning for molecular and materials science. Nature. 2018; 559(7715): 547-555. doi: 10.1038/s41586-018-0337-2
19. Goodfellow I, McDaniel P, Papernot N. Making machine learning robust against adversarial inputs. Communications of the ACM. 2018; 61(7): 56-66. doi: 10.1145/3134599
20. Yin X,. Identification of subthreshold depression based on deep learning and multimodal medical image fusion. Chinese Journal of Medical Imaging Technology. 2020; 36(8):1158-1162.
21. Nijmegen RU, Cognit DIB, & Netherlands BN. Adaptive Integration Algorithm for Distributed System Based on Particle Swarm Optimization. Distributed Processing System. 2021; 2(3). doi: 10.38007/dps.2021.020307
22. Zhao J, Wang Q. Design and Implementation of an Intelligent Moving Target Robot System for Shooting Training. International Journal of Information Technologies and Systems Approach. 2023; 16(2): 1-19. doi: 10.4018/ijitsa.320512
23. Lv M, Cao X, Tian M, et al. A novel electrochemical biosensor based on MIL-101-NH2 (Cr) combining target-responsive releasing and self-catalysis strategy for p53 detection. Biosensors and Bioelectronics. 2022; 214: 114518. doi: 10.1016/j.bios.2022.114518
24. Yoo J, Choi S, Hwang Y, et al. The Role of User Resistance and Social Influences on the Adoption of Smartphone. Journal of Organizational and End User Computing. 2021; 33(2): 36-58. doi: 10.4018/joeuc.20210301.oa3
25. Li B, Liang H, Shi L, et al. Complex dynamics of Kopel model with nonsymmetric response between oligopolists. Chaos, Solitons & Fractals. 2022; 156: 111860. doi: 10.1016/j.chaos.2022.111860
26. Lv Z, Guo J, Singh AK, et al. Digital Twins Based VR Simulation for Accident Prevention of Intelligent Vehicle. IEEE Transactions on Vehicular Technology. 2022; 71(4): 3414-3428. doi: 10.1109/tvt.2022.3152597
Copyright (c) 2024 Jiandong Wang, Yuanwei Li, Baoku Sui
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
