최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine, v.235 no.6, 2021년, pp.676 - 687
Halim, Ahmed (Mechanical Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt) , Abdellatif, A. (Mechanical Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt) , Awad, Mohammed I (Mechatronics Engineering Department, Faculty of Engineering, AinShams University, Cairo, Egypt) , Atia, Mostafa R A (Mechanical Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Cairo, Egypt)
This paper aims to enhance the accuracy of human gait prediction using machine learning algorithms. Three classifiers are used in this paper: XGBoost, Random Forest, and SVM. A predefined dataset is used for feature extraction and classification. Gait prediction is determined during several locomot...
Desmond, Deirdre M., MacLachlan, Malcolm. Affective Distress and Amputation-Related Pain Among Older Men with Long-Term, Traumatic Limb Amputations. Journal of pain and symptom management, vol.31, no.4, 362-368.
Lui, Zhen Wei, Awad, Mohammed I, Abouhossein, Alireza, Dehghani-Sanij, Abbas A, Messenger, Neil. Virtual prototyping of a semi-active transfemoral prosthetic leg. Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine, vol.229, no.5, 350-361.
IEEE Syst J Huo W 1 10 3 2014
Biomed Eng Online Windrich M 5 15 3 2016
2010 IEEE/RSJ International conference intelligent robots and systems Unal R 343
Awad, M.I., Abouhossein, A., Dehghani-Sanij, A.A., Richardson, R., Moser, D., Zahedi, S., Bradley, D.. Towards a Smart Semi-Active Prosthetic Leg: Preliminary Assessment and Testing. IFAC-papersonline, vol.49, no.21, 170-176.
El-Sayed, Amr M., Hamzaid, Nur Azah, Abu Osman, Noor Azuan. Technology Efficacy in Active Prosthetic Knees for Transfemoral Amputees: A Quantitative Evaluation. The Scientific World Journal, vol.2014, 297431-.
Ekkachai, Kittipong, Nilkhamhang, Itthisek. 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 : a publication of the IEEE Engineering in Medicine and Biology Society, vol.24, no.11, 1169-1178.
Gailey, Robert S., Roach, Kathryn E., Applegate, E.Brooks, Cho, Brandon, Cunniffe, Bridgid, Licht, Stephanie, Maguire, Melanie, Nash, Mark S.. The Amputee Mobility Predictor: An instrument to assess determinants of the lower-limb amputee's ability to ambulate. Archives of physical medicine and rehabilitation, vol.83, no.5, 613-627.
J Rehabil Assist Technol Eng Orendurff MS 3 2016
Vu, Huong Thi Thu, Dong, Dianbiao, Cao, Hoang-Long, Verstraten, Tom, Lefeber, Dirk, Vanderborght, Bram, Geeroms, Joost. A Review of Gait Phase Detection Algorithms for Lower Limb Prostheses. Sensors, vol.20, no.14, 3972-.
Krausz, Nili E., Hu, Blair H., Hargrove, Levi J.. Subject- and Environment-Based Sensor Variability for Wearable Lower-Limb Assistive Devices. Sensors, vol.19, no.22, 4887-.
Adv Intell Syst Comput Gupta A 1155 2020
Behr, James, Friedly, Janna, Molton, Ivan, Morgenroth, David, Jensen, Mark P, Smith, Douglas G. Pain and pain-related interference in adults with lower-limb amputation: comparison of knee-disarticulation, transtibial, and transfemoral surgical sites.. Journal of rehabilitation research and development, vol.46, no.7, 963-972.
Martinez-Hernandez, Uriel, Dehghani-Sanij, Abbas A.. Adaptive Bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors. Neural networks : the official journal of the International Neural Network Society, vol.102, 107-119.
Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS Woodward RB 6405
Proceedings of the IEEE RAS and EMBS international conference on biomedical robotics and biomechatronics Martinez-Hernandez U 897
Huang, He, Zhang, Fan, Hargrove, Levi J., Dou, Zhi, Rogers, Daniel R., Englehart, Kevin B.. Continuous Locomotion-Mode Identification for Prosthetic Legs Based on Neuromuscular–Mechanical Fusion. IEEE transactions on bio-medical engineering, vol.58, no.10, 2867-2875.
Wu, Yunfeng, Krishnan, Sridhar. Statistical Analysis of Gait Rhythm in Patients With Parkinson's Disease. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, vol.18, no.2, 150-158.
Si, Wen, Yang, Gelan, Chen, XiangGui, Jia, Jie. Gait identification using fractal analysis and support vector machine. Soft computing : a fusion of foundations, methodologies and applications, vol.23, no.19, 9287-9297.
Shi, Ling-Feng, Qiu, Chao-Xi, Xin, Dong-Jin, Liu, Gong-Xu. Gait recognition via random forests based on wearable inertial measurement unit. Journal of ambient intelligence and humanized computing, vol.11, no.11, 5329-5340.
Luo, Guoliang, Zhu, Yean, Wang, Rui, Tong, Yang, Lu, Wei, Wang, Haolun. Random forest-based classsification and analysis of hemiplegia gait using low-cost depth cameras. Medical & biological engineering & computing, vol.58, no.2, 373-382.
Wang, Chao, Chan, Peter P. K., Lam, Ben M. F., Wang, Sizhong, Zhang, Janet H., Chan, Zoe Y. S., Chan, Rosa H. M., Ho, Kevin K. W., Cheung, Roy T. H.. Real-Time Estimation of Knee Adduction Moment for Gait Retraining in Patients With Knee Osteoarthritis. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, vol.28, no.4, 888-894.
Wu, Yuchuan, Qi, Shengfeng, Hu, Feng, Ma, Shuangbao, Mao, Wen, Li, Wei. Recognizing activities of the elderly using wearable sensors: a comparison of ensemble algorithms based on boosting. Sensor review, vol.39, no.6, 743-751.
Hu, Blair, Rouse, Elliott, Hargrove, Levi. Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals. Frontiers in robotics and AI, vol.5, 14-.
Hu, Blair, Rouse, Elliott, Hargrove, Levi. Fusion of Bilateral Lower-Limb Neuromechanical Signals Improves Prediction of Locomotor Activities. Frontiers in robotics and AI, vol.5, 78-.
Zhang, Kuangen, Wang, Jing, de Silva, Clarence W., Fu, Chenglong. Unsupervised Cross-Subject Adaptation for Predicting Human Locomotion Intent. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, vol.28, no.3, 646-657.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.