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http://dx.doi.org/10.7471/ikeee.2019.23.3.883

The Study on Applying Ankle Joint Load Variable Lower-Knee Prosthesis to Development of Terrain-Adaptive Above-Knee Prosthesis  

Eom, Su-Hong (Dept. of Electronics Engineering, Korea Polytechnic University)
Na, Sun-Jong (Dept. of Electronics Engineering, Korea Polytechnic University)
You, Jung-Hwun (Dept. of Electronics Engineering, Korea Polytechnic University)
Park, Se-Hoon (Korea Orthopedics & Rehabilitation Engineering Center)
Lee, Eung-Hyuk (Dept. of Electronics Engineering, Korea Polytechnic University)
Publication Information
Journal of IKEEE / v.23, no.3, 2019 , pp. 883-892 More about this Journal
Abstract
This study is the method which is adapted to control ankle joint movement for resolving the problem of gait imbalance in intervals where gait environments are changed and slope walking, as applying terrain-adaptive technique to intelligent above-knee prosthesis. In this development of above-knee prosthesis, to classify the gait modes is essential. For distinguishing the stance phases and the swing phase depending on roads, a machine learning which combines decision tree and random forest from knee angle data and inertial sensor data, is proposed and adapted. By using this method, the ankle movement state of the prosthesis is controlled. This study verifies whether the problem is resolved through butterfly diagram.
Keywords
above-knee prosthesis; control ankle joint; decision tree; random forest; walking imbalance;
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  • Reference
1 Ministry of Employment and Labor, 2018 Disabled Statistics, 2018.
2 Korea Employment Agency for the Disabled, Panel Survey of Employment for the Disabled : Characteristics by Disability Type, 2017. http://kosis.kr/statHtml/statHtml.do?orgId=383&tblId=DT_383003_P009
3 Alcocer, W., Vela, L., Blanco, A., Gonzalez, J., and Oliver, M., "Major Trends in the Development of Ankle Rehabilitation Devices," Int. J. DYNA, Vol.79, No.176, pp.45-55, 2012. ISSN 0012-7353.
4 Quintero, D., Villarreal, D. J., & Gregg, R. D. "Preliminary experiments with a unified controller for a powered knee-ankle prosthetic leg across walking speeds," In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.5427-5433, 2016. DOI: 10.1109/IROS.2016.7759798
5 Su, B. Y., Wang, J., Liu, S. Q., Sheng, M., Jiang, J., & Xiang, K. "A CNN-Based Method for Intent Recognition Using Inertial Measurement Units and Intelligent Lower Limb Prosthesis," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.27, no5, pp.1032-1042, 2019. DOI: 10.1109/TNSRE.2019.2909585   DOI
6 Spanias, J. A., Simon, A. M., Finucane, S. B., Perreault, E. J., & Hargrove, L. J. "Online adaptive neural control of a robotic lower limb prosthesis," Journal of neural engineering, vol.15, no.1, 2016. DOI: 10.1088/1741-2552/aa92a8   DOI
7 Brantley, J. A., Luu, T. P., Nakagome, S., & Contreras-Vidal, J. L. "Prediction of lower-limb joint kinematics from surface EMG during overground locomotion," In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 1705-1709, 2017. DOI: 10.1109/SMC.2017.8122861   DOI
8 Afzal, T., Iqbal, K., White, G., & Wright, A. B. "A method for locomotion mode identification using muscle synergies," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.25, no.6, pp.608-617, 2017. DOI: 10.1109/TNSRE.2016.2585962   DOI
9 Huang, H., Crouch, D. L., Liu, M., Sawicki, G. S., & Wang, D. "A cyber expert system for auto-tuning powered prosthesis impedance control parameters," Annals of biomedical engineering, vol.44, no.5, pp.1613-1624, 2016. DOI: 10.1007/s10439-015-1464-7   DOI
10 Quintero, D., Villarreal, D. J., Lambert, D. J., Kapp, S., & Gregg, R. D. "Continuous-phase control of a powered knee-ankle prosthesis: Amputee experiments across speeds and inclines," IEEE Transactions on Robotics, vol.34, no.3, pp.686-701, 2018. DOI: 10.1109/TRO.2018.2794536   DOI
11 Tao, W., Liu, T., Zheng, R., Feng, H. "Gait Analysis Using Wearable Sensors," Sensors 2012, vol.12, pp.2255-2283, 2012. DOI: 10.3390/s120202255   DOI
12 Vasan, G., & Pilarski, P. M. "Learning from demonstration: Teaching a myoelectric prosthesis with an intact limb via reinforcement learning," In 2017 International Conference on Rehabilitation Robotics (ICORR) pp. 1457-1464. 2017. DOI: 10.1109/ICORR.2017.8009453   DOI
13 Rajtukova, V. a, Michalikova, M., Bednarcikova, L., Balogova, A., Zivcak, J. "Biomechanics of Lower Limb Prostheses," Procedia Engineering, vol.96, pp.382-391, 2014. DOI: 10.1016/j.proeng.2014.12.107   DOI
14 Maqbool, H. F., Husman, M. A. B., Awad, M. I., Abouhossein, A., Iqbal, N., & Dehghani-Sanij, A. A. "A real-time gait event detection for lower limb prosthesis control and evaluation," IEEE transactions on neural systems and rehabilitation engineering, vol.25, no.9, pp.1500-1509, 2016. DOI: 10.1109/TNSRE.2016.2636367   DOI
15 Won ho Heo, Euntai Kim, Hyun Sub Park, and Jun-Young Jung, "A Gait Phase Classifier using a Recurrent Neural Network," Journal of Institute of Control, Robotics and Systems, vol.21, no.6, pp.518-523, 2015. DOI: 10.5302/J.ICROS.2015.15.9024   DOI
16 Huang, Q., Yang, D., Jiang, L., Zhang, H., Liu, H., & Kotani, K. "A novel unsupervised adaptive learning method for long-term electromyography (EMG) pattern recognition," Sensors 2017, vol.17, no.6, pp.1370, 2017. DOI: 10.3390/s17061370   DOI
17 Wurdeman, S. R., Stevens, P. M., & Campbell, J. H. "Mobility Analysis of AmpuTees (MAAT 4): Classification tree analysis for probability of lower limb prosthesis user functional potential," Disability and Rehabilitation: Assistive Technology, pp.1-8, 2019. DOI: 10.1080/17483107.2018.1555290   DOI
18 Wu, H., Huang, Q., Wang, D., & Gao, L. "A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals," Journal of Electromyography and Kinesiology, vol.42, pp.136-142, 2018. DOI: 10.1016/j.jelekin.2018.07.005   DOI
19 Ekkachai, K., & Nilkhamhang, I. "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, vol.24, no.11, pp.1169-1178, 2016. DOI: 10.1109/TNSRE.2016.2521686   DOI
20 Liu, M., Wang, D., & Huang, H. H. "Development of an environment-aware locomotion mode recognition system for powered lower limb prostheses," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol.24, no.4, pp.434-443, 2015. DOI: 10.1109/TNSRE.2015.2420539   DOI
21 Genuer, R., Poggi, J. M., Tuleau-Malot, C., & Villa-Vialaneix, N, "Random forests for big data," Big Data Research, vol.9, pp.28-46, 2017. DOI: 10.1016/j.bdr.2017.07.003   DOI