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Journal Abstract Search

293 related items for PubMed ID: 33711613

  • 1. A novel dataset and deep learning-based approach for marker-less motion capture during gait.
    Vafadar S, Skalli W, Bonnet-Lebrun A, Khalifé M, Renaudin M, Hamza A, Gajny L.
    Gait Posture; 2021 May; 86():70-76. PubMed ID: 33711613
    [Abstract] [Full Text] [Related]

  • 2. Assessment of a novel deep learning-based marker-less motion capture system for gait study.
    Vafadar S, Skalli W, Bonnet-Lebrun A, Assi A, Gajny L.
    Gait Posture; 2022 May; 94():138-143. PubMed ID: 35306382
    [Abstract] [Full Text] [Related]

  • 3. Comparing the accuracy of open-source pose estimation methods for measuring gait kinematics.
    Washabaugh EP, Shanmugam TA, Ranganathan R, Krishnan C.
    Gait Posture; 2022 Sep; 97():188-195. PubMed ID: 35988434
    [Abstract] [Full Text] [Related]

  • 4. A Deep Neural Network-based method for estimation of 3D lifting motions.
    Mehrizi R, Peng X, Xu X, Zhang S, Li K.
    J Biomech; 2019 Feb 14; 84():87-93. PubMed ID: 30587377
    [Abstract] [Full Text] [Related]

  • 5. The accuracy of several pose estimation methods for 3D joint centre localisation.
    Needham L, Evans M, Cosker DP, Wade L, McGuigan PM, Bilzon JL, Colyer SL.
    Sci Rep; 2021 Oct 19; 11(1):20673. PubMed ID: 34667207
    [Abstract] [Full Text] [Related]

  • 6. Two-dimensional video-based analysis of human gait using pose estimation.
    Stenum J, Rossi C, Roemmich RT.
    PLoS Comput Biol; 2021 Apr 19; 17(4):e1008935. PubMed ID: 33891585
    [Abstract] [Full Text] [Related]

  • 7. Concurrent assessment of gait kinematics using marker-based and markerless motion capture.
    Kanko RM, Laende EK, Davis EM, Selbie WS, Deluzio KJ.
    J Biomech; 2021 Oct 11; 127():110665. PubMed ID: 34380101
    [Abstract] [Full Text] [Related]

  • 8. Test Platform for Developing New Optical Position Tracking Technology towards Improved Head Motion Correction in Magnetic Resonance Imaging.
    Silic M, Tam F, Graham SJ.
    Sensors (Basel); 2024 Jun 08; 24(12):. PubMed ID: 38931521
    [Abstract] [Full Text] [Related]

  • 9. Assessment of deep learning pose estimates for sports collision tracking.
    Blythman R, Saxena M, Tierney GJ, Richter C, Smolic A, Simms C.
    J Sports Sci; 2022 Sep 08; 40(17):1885-1900. PubMed ID: 36093680
    [Abstract] [Full Text] [Related]

  • 10. Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system.
    Kanko RM, Laende EK, Strutzenberger G, Brown M, Selbie WS, DePaul V, Scott SH, Deluzio KJ.
    J Biomech; 2021 Jun 09; 122():110414. PubMed ID: 33915475
    [Abstract] [Full Text] [Related]

  • 11. Applications and limitations of current markerless motion capture methods for clinical gait biomechanics.
    Wade L, Needham L, McGuigan P, Bilzon J.
    PeerJ; 2022 Jun 09; 10():e12995. PubMed ID: 35237469
    [Abstract] [Full Text] [Related]

  • 12. Dual-channel cascade pose estimation network trained on infrared thermal image and groundtruth annotation for real-time gait measurement.
    Zhu Y, Lu W, Zhang R, Wang R, Robbins D.
    Med Image Anal; 2022 Jul 09; 79():102435. PubMed ID: 35398606
    [Abstract] [Full Text] [Related]

  • 13. Applications of markerless motion capture in gait recognition.
    Sandau M.
    Dan Med J; 2016 Mar 09; 63(3):. PubMed ID: 26931198
    [Abstract] [Full Text] [Related]

  • 14. A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors.
    Guimarães V, Sousa I, Correia MV.
    Sensors (Basel); 2021 Nov 12; 21(22):. PubMed ID: 34833590
    [Abstract] [Full Text] [Related]

  • 15. Lower body kinematics estimation from wearable sensors for walking and running: A deep learning approach.
    Hernandez V, Dadkhah D, Babakeshizadeh V, Kulić D.
    Gait Posture; 2021 Jan 12; 83():185-193. PubMed ID: 33161275
    [Abstract] [Full Text] [Related]

  • 16. A comparison of three-dimensional kinematics between markerless and marker-based motion capture in overground gait.
    Ripic Z, Nienhuis M, Signorile JF, Best TM, Jacobs KA, Eltoukhy M.
    J Biomech; 2023 Oct 12; 159():111793. PubMed ID: 37725886
    [Abstract] [Full Text] [Related]

  • 17. Detection, segmentation, and 3D pose estimation of surgical tools using convolutional neural networks and algebraic geometry.
    Hasan MK, Calvet L, Rabbani N, Bartoli A.
    Med Image Anal; 2021 May 12; 70():101994. PubMed ID: 33611053
    [Abstract] [Full Text] [Related]

  • 18. Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.
    Kolaghassi R, Al-Hares MK, Marcelli G, Sirlantzis K.
    Sensors (Basel); 2022 Apr 13; 22(8):. PubMed ID: 35458954
    [Abstract] [Full Text] [Related]

  • 19. Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model.
    Aoyagi Y, Yamada S, Ueda S, Iseki C, Kondo T, Mori K, Kobayashi Y, Fukami T, Hoshimaru M, Ishikawa M, Ohta Y.
    Sensors (Basel); 2022 Jul 14; 22(14):. PubMed ID: 35890959
    [Abstract] [Full Text] [Related]

  • 20. Enabling Gait Analysis in the Telemedicine Practice through Portable and Accurate 3D Human Pose Estimation.
    Martini E, Boldo M, Aldegheri S, Valè N, Filippetti M, Smania N, Bertucco M, Picelli A, Bombieri N.
    Comput Methods Programs Biomed; 2022 Oct 14; 225():107016. PubMed ID: 35907374
    [Abstract] [Full Text] [Related]

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