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 *

169 related articles for article (PubMed ID: 35460048)

  • 1. Deep reinforcement learning coupled with musculoskeletal modelling for a better understanding of elderly falls.
    Nowakowski K; El Kirat K; Dao TT
    Med Biol Eng Comput; 2022 Jun; 60(6):1745-1761. PubMed ID: 35460048
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Human locomotion with reinforcement learning using bioinspired reward reshaping strategies.
    Nowakowski K; Carvalho P; Six JB; Maillet Y; Nguyen AT; Seghiri I; M'Pemba L; Marcille T; Ngo ST; Dao TT
    Med Biol Eng Comput; 2021 Jan; 59(1):243-256. PubMed ID: 33417125
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Interpreting Musculoskeletal Models and Dynamic Simulations: Causes and Effects of Differences Between Models.
    Roelker SA; Caruthers EJ; Baker RK; Pelz NC; Chaudhari AMW; Siston RA
    Ann Biomed Eng; 2017 Nov; 45(11):2635-2647. PubMed ID: 28779473
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Contributions to the understanding of gait control.
    Simonsen EB
    Dan Med J; 2014 Apr; 61(4):B4823. PubMed ID: 24814597
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Development of a mathematical model for predicting electrically elicited quadriceps femoris muscle forces during isovelocity knee joint motion.
    Perumal R; Wexler AS; Binder-Macleod SA
    J Neuroeng Rehabil; 2008 Dec; 5():33. PubMed ID: 19077188
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Effects of Ankle Joint Motion on Pelvis-Hip Biomechanics and Muscle Activity Patterns of Healthy Individuals in Knee Immobilization Gait.
    Guan X; Kuai S; Song L; Liu W; Liu Y; Ji L; Wang R
    J Healthc Eng; 2019; 2019():3812407. PubMed ID: 31737239
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Muscle force estimation in clinical gait analysis using AnyBody and OpenSim.
    Trinler U; Schwameder H; Baker R; Alexander N
    J Biomech; 2019 Mar; 86():55-63. PubMed ID: 30739769
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Correlation between lower limb isometric strength and muscle structure with normal and challenged gait performance in older adults.
    Guadagnin EC; Priario LAA; Carpes FP; Vaz MA
    Gait Posture; 2019 Sep; 73():101-107. PubMed ID: 31319373
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Muscle force distribution during forward and backward locomotion.
    Błażkiewicz M
    Acta Bioeng Biomech; 2013; 15(3):3-9. PubMed ID: 24215105
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Strong relationship of muscle force and fall efficacy, but not of gait kinematics, with number of falls in the year after Total Hip Arthroplasty for osteoarthritis: An exploratory study.
    Lin X; Wu W; Weijer RHA; Prins MR; van Dieën JH; Bruijn SM; Meijer OG
    Clin Biomech (Bristol, Avon); 2022 Feb; 92():105551. PubMed ID: 34998081
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Increased use of stepping strategy in response to medio-lateral perturbations in the elderly relates to altered reactive tibialis anterior activity.
    Afschrift M; van Deursen R; De Groote F; Jonkers I
    Gait Posture; 2019 Feb; 68():575-582. PubMed ID: 30654320
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Hindlimb muscle function in relation to speed and gait: in vivo patterns of strain and activation in a hip and knee extensor of the rat (Rattus norvegicus).
    Gillis GB; Biewener AA
    J Exp Biol; 2001 Aug; 204(Pt 15):2717-31. PubMed ID: 11533122
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Age-related differences in muscle co-activation during locomotion and their relationship with gait speed: a pilot study.
    Lee HJ; Chang WH; Choi BO; Ryu GH; Kim YH
    BMC Geriatr; 2017 Jan; 17(1):44. PubMed ID: 28143609
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Swing phase mechanics of healthy young and elderly men.
    Mills PM; Barrett RS
    Hum Mov Sci; 2001 Nov; 20(4-5):427-46. PubMed ID: 11750671
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A comparison of optimisation methods and knee joint degrees of freedom on muscle force predictions during single-leg hop landings.
    Mokhtarzadeh H; Perraton L; Fok L; Muñoz MA; Clark R; Pivonka P; Bryant AL
    J Biomech; 2014 Sep; 47(12):2863-8. PubMed ID: 25129166
    [TBL] [Abstract][Full Text] [Related]  

  • 16. From deep learning to transfer learning for the prediction of skeletal muscle forces.
    Dao TT
    Med Biol Eng Comput; 2019 May; 57(5):1049-1058. PubMed ID: 30552553
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Deep Reinforcement Learning for Physics-Based Musculoskeletal Simulations of Healthy Subjects and Transfemoral Prostheses' Users During Normal Walking.
    De Vree L; Carloni R
    IEEE Trans Neural Syst Rehabil Eng; 2021; 29():607-618. PubMed ID: 33646954
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A three-dimensional biomechanical evaluation of quadriceps and hamstrings function using electrical stimulation.
    Hunter BV; Thelen DG; Dhaher YY
    IEEE Trans Neural Syst Rehabil Eng; 2009 Apr; 17(2):167-75. PubMed ID: 19193516
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation.
    Song S; Kidziński Ł; Peng XB; Ong C; Hicks J; Levine S; Atkeson CG; Delp SL
    J Neuroeng Rehabil; 2021 Aug; 18(1):126. PubMed ID: 34399772
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Gait characteristics of elderly people with a history of falls: a dynamic approach.
    Barak Y; Wagenaar RC; Holt KG
    Phys Ther; 2006 Nov; 86(11):1501-10. PubMed ID: 17079750
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.