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Pubmed for Handhelds

PUBMED FOR HANDHELDS

Journal Abstract Search

277 related items for PubMed ID: 34283080

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  • 6. Spring-loaded inverted pendulum modeling improves neural network estimation of ground reaction forces.
    Kim B, Lim H, Park S.
    J Biomech; 2020 Dec 02; 113():110069. PubMed ID: 33142204
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  • 7. IMU-to-Segment Assignment and Orientation Alignment for the Lower Body Using Deep Learning.
    Zimmermann T, Taetz B, Bleser G.
    Sensors (Basel); 2018 Jan 19; 18(1):. PubMed ID: 29351262
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  • 8. 3D gait analysis in children using wearable sensors: feasibility of predicting joint kinematics and kinetics with personalized machine learning models and inertial measurement units.
    Mohammadi Moghadam S, Ortega Auriol P, Yeung T, Choisne J.
    Front Bioeng Biotechnol; 2024 Jan 19; 12():1372669. PubMed ID: 38572359
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  • 14. A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors.
    Liu K, Liu Y, Ji S, Gao C, Zhang S, Fu J.
    Sensors (Basel); 2023 Jun 26; 23(13):. PubMed ID: 37447755
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  • 17. Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor.
    Chow DHK, Tremblay L, Lam CY, Yeung AWY, Cheng WHW, Tse PTW.
    Sensors (Basel); 2021 Jul 06; 21(14):. PubMed ID: 34300372
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  • 18. A comparison of machine learning models' accuracy in predicting lower-limb joints' kinematics, kinetics, and muscle forces from wearable sensors.
    Moghadam SM, Yeung T, Choisne J.
    Sci Rep; 2023 Mar 28; 13(1):5046. PubMed ID: 36977706
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  • 19. Integrating an LSTM framework for predicting ankle joint biomechanics during gait using inertial sensors.
    Xiang L, Gu Y, Gao Z, Yu P, Shim V, Wang A, Fernandez J.
    Comput Biol Med; 2024 Mar 28; 170():108016. PubMed ID: 38277923
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