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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

167 related items for PubMed ID: 36201910

  • 1. Generative deep learning applied to biomechanics: A new augmentation technique for motion capture datasets.
    Bicer M, Phillips ATM, Melis A, McGregor AH, Modenese L.
    J Biomech; 2022 Nov; 144():111301. PubMed ID: 36201910
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  • 2. Real-time conversion of inertial measurement unit data to ankle joint angles using deep neural networks.
    Senanayake D, Halgamuge S, Ackland DC.
    J Biomech; 2021 Aug 26; 125():110552. PubMed ID: 34237661
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  • 4. The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions.
    Sharifi Renani M, Eustace AM, Myers CA, Clary CW.
    Sensors (Basel); 2021 Aug 31; 21(17):. PubMed ID: 34502766
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  • 5. Estimation of Lower Extremity Joint Moments and 3D Ground Reaction Forces Using IMU Sensors in Multiple Walking Conditions: A Deep Learning Approach.
    Hossain MSB, Guo Z, Choi H.
    IEEE J Biomed Health Inform; 2023 Jun 31; 27(6):2829-2840. PubMed ID: 37030855
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  • 8. Prediction of ground reaction forces during gait based on kinematics and a neural network model.
    Oh SE, Choi A, Mun JH.
    J Biomech; 2013 Sep 27; 46(14):2372-80. PubMed ID: 23962528
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  • 9. 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 27; 83():185-193. PubMed ID: 33161275
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  • 12. Estimating 3D L5/S1 moments and ground reaction forces during trunk bending using a full-body ambulatory inertial motion capture system.
    Faber GS, Chang CC, Kingma I, Dennerlein JT, van Dieën JH.
    J Biomech; 2016 Apr 11; 49(6):904-912. PubMed ID: 26795123
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  • 14. American society of biomechanics early career achievement award 2020: Toward portable and modular biomechanics labs: How video and IMU fusion will change gait analysis.
    Halilaj E, Shin S, Rapp E, Xiang D.
    J Biomech; 2021 Dec 02; 129():110650. PubMed ID: 34644610
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  • 15. Comparison of kinematic parameters of children gait obtained by inverse and direct models.
    Ziziene J, Daunoraviciene K, Juskeniene G, Raistenskis J.
    PLoS One; 2022 Dec 02; 17(6):e0270423. PubMed ID: 35749351
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  • 16. Data augmentation for enhancing EEG-based emotion recognition with deep generative models.
    Luo Y, Zhu LZ, Wan ZY, Lu BL.
    J Neural Eng; 2020 Oct 14; 17(5):056021. PubMed ID: 33052888
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  • 17. Generative adversarial networks with decoder-encoder output noises.
    Zhong G, Gao W, Liu Y, Yang Y, Wang DH, Huang K.
    Neural Netw; 2020 Jul 14; 127():19-28. PubMed ID: 32315932
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  • 18. Estimation of the ground reaction forces from a single video camera based on the spring-like center of mass dynamics of human walking.
    Jeong H, Park S.
    J Biomech; 2020 Dec 02; 113():110074. PubMed ID: 33176224
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  • 19. 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 02; 86():70-76. PubMed ID: 33711613
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  • 20. Prediction of joint moments from kinematics using machine learning in children with congenital talipes equino varus and typically developing peers.
    Kothurkar R, Gad M, Padate A, Rathod C, Bhaskar A, Lekurwale R, Rose J.
    J Orthop; 2024 Nov 02; 57():83-89. PubMed ID: 39006209
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