These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

131 related articles for article (PubMed ID: 36406950)

  • 1. A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals.
    Zhang B; Lan X; Wang G; Pang Z; Zhang X; Sun Z
    Front Neurorobot; 2022; 16():1047325. PubMed ID: 36406950
    [TBL] [Abstract][Full Text] [Related]  

  • 2. SVM-Based Classification of sEMG Signals for Upper-Limb Self-Rehabilitation Training.
    Cai S; Chen Y; Huang S; Wu Y; Zheng H; Li X; Xie L
    Front Neurorobot; 2019; 13():31. PubMed ID: 31214010
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Fusion Learning for sEMG Recognition of Multiple Upper-Limb Rehabilitation Movements.
    Zhong T; Li D; Wang J; Xu J; An Z; Zhu Y
    Sensors (Basel); 2021 Aug; 21(16):. PubMed ID: 34450825
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots.
    Yang S; Garg NP; Gao R; Yuan M; Noronha B; Ang WT; Accoto D
    Sensors (Basel); 2023 Mar; 23(6):. PubMed ID: 36991709
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Research on the method of identifying upper and lower limb coordinated movement intentions based on surface EMG signals.
    Feng Y; Yu L; Dong F; Zhong M; Pop AA; Tang M; Vladareanu L
    Front Bioeng Biotechnol; 2023; 11():1349372. PubMed ID: 38268935
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Shoulder muscle activation pattern recognition based on sEMG and machine learning algorithms.
    Jiang Y; Chen C; Zhang X; Chen C; Zhou Y; Ni G; Muh S; Lemos S
    Comput Methods Programs Biomed; 2020 Dec; 197():105721. PubMed ID: 32882593
    [TBL] [Abstract][Full Text] [Related]  

  • 7. fNIRS-Based Upper Limb Motion Intention Recognition Using an Artificial Neural Network for Transhumeral Amputees.
    Sattar NY; Kausar Z; Usama SA; Farooq U; Shah MF; Muhammad S; Khan R; Badran M
    Sensors (Basel); 2022 Jan; 22(3):. PubMed ID: 35161473
    [TBL] [Abstract][Full Text] [Related]  

  • 8. MCR-ALS-based muscle synergy extraction method combined with LSTM neural network for motion intention detection.
    Zhao D; Ma Y; Meng J; Hu Y; Hong M; Zhang J; Zuo G; Lv X; Liu Y; Shi C
    Front Neurorobot; 2023; 17():1174710. PubMed ID: 37334170
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Recognition of Upper Limb Action Intention Based on IMU.
    Cui JW; Li ZG; Du H; Yan BY; Lu PD
    Sensors (Basel); 2022 Mar; 22(5):. PubMed ID: 35271101
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A New Projected Active Set Conjugate Gradient Approach for Taylor-Type Model Predictive Control: Application to Lower Limb Rehabilitation Robots With Passive and Active Rehabilitation.
    Shi T; Tian Y; Sun Z; Zhang B; Pang Z; Yu J; Zhang X
    Front Neurorobot; 2020; 14():559048. PubMed ID: 33343324
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Review of adaptive control for stroke lower limb exoskeleton rehabilitation robot based on motion intention recognition.
    Su D; Hu Z; Wu J; Shang P; Luo Z
    Front Neurorobot; 2023; 17():1186175. PubMed ID: 37465413
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Real-Time Evaluation of the Signal Processing of sEMG Used in Limb Exoskeleton Rehabilitation System.
    Gao B; Wei C; Ma H; Yang S; Ma X; Zhang S
    Appl Bionics Biomech; 2018; 2018():1391032. PubMed ID: 30405746
    [TBL] [Abstract][Full Text] [Related]  

  • 13. A Real-Time Control Method for Upper Limb Exoskeleton Based on Active Torque Prediction Model.
    Li S; Zhang L; Meng Q; Yu H
    Bioengineering (Basel); 2023 Dec; 10(12):. PubMed ID: 38136032
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Upper Limb Movement Classification Via Electromyographic Signals and an Enhanced Probabilistic Network.
    Burns A; Adeli H; Buford JA
    J Med Syst; 2020 Aug; 44(10):176. PubMed ID: 32829419
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Review on electromyography based intention for upper limb control using pattern recognition for human-machine interaction.
    Asghar A; Jawaid Khan S; Azim F; Shakeel CS; Hussain A; Niazi IK
    Proc Inst Mech Eng H; 2022 May; 236(5):628-645. PubMed ID: 35118907
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Design and verification of a human-robot interaction system for upper limb exoskeleton rehabilitation.
    Wendong W; Hanhao L; Menghan X; Yang C; Xiaoqing Y; Xing M; Bing Z
    Med Eng Phys; 2020 May; 79():19-25. PubMed ID: 32205023
    [TBL] [Abstract][Full Text] [Related]  

  • 17. An EMG-Based Control for an Upper-Limb Power-Assist Exoskeleton Robot.
    Kiguchi K; Hayashi Y
    IEEE Trans Syst Man Cybern B Cybern; 2012 Aug; 42(4):1064-71. PubMed ID: 22334026
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Design and Control of an Upper Limb Bionic Exoskeleton Rehabilitation Device Based on Tensegrity Structure.
    Ni P; Sun J; Dong J
    Appl Bionics Biomech; 2024; 2024():5905225. PubMed ID: 39239384
    [TBL] [Abstract][Full Text] [Related]  

  • 19. sEMG-Based Motion Recognition of Upper Limb Rehabilitation Using the Improved Yolo-v4 Algorithm.
    Bu D; Guo S; Li H
    Life (Basel); 2022 Jan; 12(1):. PubMed ID: 35054457
    [TBL] [Abstract][Full Text] [Related]  

  • 20. MCSNet: Channel Synergy-Based Human-Exoskeleton Interface With Surface Electromyogram.
    Shi K; Huang R; Peng Z; Mu F; Yang X
    Front Neurosci; 2021; 15():704603. PubMed ID: 34867145
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 7.