노면 적응형 대퇴 의족개발을 위한 발목 관절 부하가변형 하퇴 의족 적용에 대한 연구
The Study on Applying Ankle Joint Load Variable Lower-Knee Prosthesis to Development of Terrain-Adaptive Above-Knee Prosthesis
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초록
This study is the method which is adapted to control ankle joint movement for resolving the problem of gait imbalancein intervals where gait environments are changed and slope walking, as applying terrain-adaptive technique to intelligentabove-knee prosthesis. In this development of above-knee prosthesis, to classify the gait modes is essential. Fordistinguishing the stance phases and the swing phase depending on roads, a machine learning which combines decisiontree and random forest from knee angle data and inertial sensor data, is proposed and adapted. By using this method, theankle movement state of the prosthesis is controlled. This study verifies whether the problem is resolved throughbutterfly diagram.
Abstract
This study is the method which is adapted to control ankle joint movement for resolving the problem of gait imbalancein intervals where gait environments are changed and slope walking, as applying terrain-adaptive technique to intelligentabove-knee prosthesis. In this development of above-knee prosthesis, to classify the gait modes is essential. Fordistinguishing the stance phases and the swing phase depending on roads, a machine learning which combines decisiontree and random forest from knee angle data and inertial sensor data, is proposed and adapted. By using this method, theankle movement state of the prosthesis is controlled. This study verifies whether the problem is resolved throughbutterfly diagram.
참고문헌 (21)
- 2018Disabled Statistics, "Ministry of Employment and Labor", ~ (2018)
- "Panel Survey of Employment for the Disabled：Characteristics by Disability Type" (2017)[URL]
- Alcocer, W., "Major Trends in the Development of Ankle Rehabilitation Devices", Int. J. DYNA, 79(176), pp.45~55 (2012)
- Quintero, D., "Preliminary experiments with a unified controller for a powered knee-ankle prosthetic leg across walking speeds", 2016 IEEE, 2016, 5427~5433
- Ben-Yue Su, "A CNN-Based Method for Intent Recognition Using Inertial Measurement Units and Intelligent Lower Limb Prosthesis", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(5), pp.1032~1042 (2019), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Spanias, J. A., "Online adaptive neural control of a robotic lower limb prosthesis", Journal of neural engineering, 15(1) (2016)[DOI]
- Brantley, J. A., "Prediction of lower-limb joint kinematics from surface EMG during overground locomotion", 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp.1705~1709 (2017)
- Taimoor Afzal, "A Method for Locomotion Mode Identification Using Muscle Synergies", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(6), pp.608~617 (2017), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- David Quintero, "Continuous-Phase Control of a Powered Knee–Ankle Prosthesis: Amputee Experiments Across Speeds and Inclines", IEEE Transactions on Robotics, 34(3), pp.686~701 (2018), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- He Huang, "A Cyber Expert System for Auto-Tuning Powered Prosthesis Impedance Control Parameters", Annals of Biomedical Engineering, 44(5), pp.1613~1624 (2016), Springer Science and Business Media LLC[DOI]
- Weijun Tao, "Gait Analysis Using Wearable Sensors", Sensors, 12(2), pp.2255~2283 (2012), MDPI AG[DOI]
- V. Rajťúková, "Biomechanics of Lower Limb Prostheses", Procedia Engineering, 96, pp.382~391 (2014), Elsevier BV[DOI]
- H. F. Maqbool, "A Real-Time Gait Event Detection for Lower Limb Prosthesis Control and Evaluation", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(9), pp.1500~1509 (2017), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- 허원호, "순환 신경망을 이용한 보행단계 분류기", 제어.로봇.시스템학회 논문지, 21(6), pp.518~523 (2015), 제어·로봇·시스템학회[DOI]
- Vasan, G., "Learning from demonstration: Teaching a myoelectric prosthesis with an intact limb via reinforcement learning", 2017 International Conference on Rehabilitation Robotics (ICORR), pp.1457~1464 (2017)
- Qi Huang, "A Novel Unsupervised Adaptive Learning Method for Long-Term Electromyography (EMG) Pattern Recognition", Sensors, 17(6), pp.1370~ (2017), MDPI AG[DOI]
- Haifeng Wu, "A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals", Journal of Electromyography and Kinesiology, 42, pp.136~142 (2018), Elsevier BV[DOI]
- Kittipong Ekkachai, "Swing Phase Control of Semi-Active Prosthetic Knee Using Neural Network Predictive Control With Particle Swarm Optimization", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(11), pp.1169~1178 (2016), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Ming Liu, "Development of an Environment-Aware Locomotion Mode Recognition System for Powered Lower Limb Prostheses", IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(4), pp.434~443 (2016), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
- Shane R. Wurdeman, "Mobility Analysis of AmpuTees (MAAT 4): classification tree analysis for probability of lower limb prosthesis user functional potential", Disability and Rehabilitation: Assistive Technology, 15(2), pp.211~218 (2019), Informa UK Limited[DOI]
- Robin Genuer, "Random Forests for Big Data", Big Data Research, 9, pp.28~46 (2017), Elsevier BV[DOI]