$\require{mediawiki-texvc}$

연합인증

연합인증 가입 기관의 연구자들은 소속기관의 인증정보(ID와 암호)를 이용해 다른 대학, 연구기관, 서비스 공급자의 다양한 온라인 자원과 연구 데이터를 이용할 수 있습니다.

이는 여행자가 자국에서 발행 받은 여권으로 세계 각국을 자유롭게 여행할 수 있는 것과 같습니다.

연합인증으로 이용이 가능한 서비스는 NTIS, DataON, Edison, Kafe, Webinar 등이 있습니다.

한번의 인증절차만으로 연합인증 가입 서비스에 추가 로그인 없이 이용이 가능합니다.

다만, 연합인증을 위해서는 최초 1회만 인증 절차가 필요합니다. (회원이 아닐 경우 회원 가입이 필요합니다.)

연합인증 절차는 다음과 같습니다.

최초이용시에는
ScienceON에 로그인 → 연합인증 서비스 접속 → 로그인 (본인 확인 또는 회원가입) → 서비스 이용

그 이후에는
ScienceON 로그인 → 연합인증 서비스 접속 → 서비스 이용

연합인증을 활용하시면 KISTI가 제공하는 다양한 서비스를 편리하게 이용하실 수 있습니다.

Prediction of human gait activities using wearable sensors

Proceedings 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)

Abstract AI-Helper 아이콘AI-Helper

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...

참고문헌 (32)

  1. 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.

  2. 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.

  3. IEEE Syst J Huo W 1 10 3 2014 

  4. Biomed Eng Online Windrich M 5 15 3 2016 

  5. 2010 IEEE/RSJ International conference intelligent robots and systems Unal R 343 

  6. 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.

  7. 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-.

  8. 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.

  9. 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.

  10. J Rehabil Assist Technol Eng Orendurff MS 3 2016 

  11. 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-.

  12. 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-.

  13. Adv Intell Syst Comput Gupta A 1155 2020 

  14. 10.48084/etasr.2952 

  15. 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.

  16. 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.

  17. Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS Woodward RB 6405 

  18. Proceedings of the IEEE RAS and EMBS international conference on biomedical robotics and biomechatronics Martinez-Hernandez U 897 

  19. 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.

  20. 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.

  21. 10.1007/978-3-642-29148-7_32 

  22. 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.

  23. 10.1007/978-3-642-22170-5_54 

  24. 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.

  25. 10.1007/978-3-319-65172-9_51 

  26. 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.

  27. 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.

  28. 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.

  29. 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-.

  30. 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-.

  31. 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.

  32. 10.1109/ITCE48509.2020.9047780 

섹션별 컨텐츠 바로가기

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

AI-Helper 아이콘
AI-Helper
안녕하세요, AI-Helper입니다. 좌측 "선택된 텍스트"에서 텍스트를 선택하여 요약, 번역, 용어설명을 실행하세요.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.

선택된 텍스트

맨위로