BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

148 related articles for article (PubMed ID: 32854288)

  • 1. Recognition and Repetition Counting for Local Muscular Endurance Exercises in Exercise-Based Rehabilitation: A Comparative Study Using Artificial Intelligence Models.
    Prabhu G; O'Connor NE; Moran K
    Sensors (Basel); 2020 Aug; 20(17):. PubMed ID: 32854288
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Deep Learning for Sensor-Based Rehabilitation Exercise Recognition and Evaluation.
    Zhu ZA; Lu YC; You CH; Chiang CK
    Sensors (Basel); 2019 Feb; 19(4):. PubMed ID: 30791648
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Assessing Physical Rehabilitation Exercises using Graph Convolutional Network with Self-supervised regularization.
    Du C; Graham S; Depp C; Nguyen T
    Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():281-285. PubMed ID: 34891291
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Recognition and Repetition Counting for ComplexPhysical Exercises with Deep Learning.
    Soro A; Brunner G; Tanner S; Wattenhofer R
    Sensors (Basel); 2019 Feb; 19(3):. PubMed ID: 30744158
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Enhancing automated lower limb rehabilitation exercise task recognition through multi-sensor data fusion in tele-rehabilitation.
    Ettefagh A; Roshan Fekr A
    Biomed Eng Online; 2024 Mar; 23(1):35. PubMed ID: 38504279
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Graph Convolutional Networks for Assessment of Physical Rehabilitation Exercises.
    Deb S; Islam MF; Rahman S; Rahman S
    IEEE Trans Neural Syst Rehabil Eng; 2022; 30():410-419. PubMed ID: 35139022
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Deep learning model for classifying shoulder pain rehabilitation exercises using IMU sensor.
    Lee K; Kim JH; Hong H; Jeong Y; Ryu H; Kim H; Lee SU
    J Neuroeng Rehabil; 2024 Mar; 21(1):42. PubMed ID: 38539223
    [TBL] [Abstract][Full Text] [Related]  

  • 8. White blood cells detection and classification based on regional convolutional neural networks.
    Kutlu H; Avci E; Özyurt F
    Med Hypotheses; 2020 Feb; 135():109472. PubMed ID: 31760248
    [TBL] [Abstract][Full Text] [Related]  

  • 9. AIoT-Enabled Rehabilitation Recognition System-Exemplified by Hybrid Lower-Limb Exercises.
    Lai YC; Kan YC; Lin YC; Lin HC
    Sensors (Basel); 2021 Jul; 21(14):. PubMed ID: 34300501
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Computer Vision Based on a Modular Neural Network for Automatic Assessment of Physical Therapy Rehabilitation Activities.
    Francisco JA; Rodrigues PS
    IEEE Trans Neural Syst Rehabil Eng; 2023; 31():2174-2183. PubMed ID: 36459598
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Analysis of the role and robustness of artificial intelligence in commodity image recognition under deep learning neural network.
    Chen R; Wang M; Lai Y
    PLoS One; 2020; 15(7):e0235783. PubMed ID: 32634167
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Automatic Assessment of Functional Movement Screening Exercises with Deep Learning Architectures.
    Spilz A; Munz M
    Sensors (Basel); 2022 Dec; 23(1):. PubMed ID: 36616604
    [TBL] [Abstract][Full Text] [Related]  

  • 13. CNN Multi-Position Wearable Sensor Human Activity Recognition Used in Basketball Training.
    Tang B; Guan W
    Comput Intell Neurosci; 2022; 2022():9918143. PubMed ID: 36172312
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Real-Time Sensor-Based Human Activity Recognition for eFitness and eHealth Platforms.
    Czekaj Ł; Kowalewski M; Domaszewicz J; Kitłowski R; Szwoch M; Duch W
    Sensors (Basel); 2024 Jun; 24(12):. PubMed ID: 38931675
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Development and Clinical Evaluation of a Web-Based Upper Limb Home Rehabilitation System Using a Smartwatch and Machine Learning Model for Chronic Stroke Survivors: Prospective Comparative Study.
    Chae SH; Kim Y; Lee KS; Park HS
    JMIR Mhealth Uhealth; 2020 Jul; 8(7):e17216. PubMed ID: 32480361
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Sensor-Based Human Activity Recognition Using Adaptive Class Hierarchy.
    Kondo K; Hasegawa T
    Sensors (Basel); 2021 Nov; 21(22):. PubMed ID: 34833819
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset.
    Espinosa R; Ponce H; Gutiérrez S; Martínez-Villaseñor L; Brieva J; Moya-Albor E
    Comput Biol Med; 2019 Dec; 115():103520. PubMed ID: 31698242
    [TBL] [Abstract][Full Text] [Related]  

  • 18. fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations.
    Vu H; Kim HC; Jung M; Lee JH
    Neuroimage; 2020 Dec; 223():117328. PubMed ID: 32896633
    [TBL] [Abstract][Full Text] [Related]  

  • 19. An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques.
    Nasri N; Orts-Escolano S; Cazorla M
    Sensors (Basel); 2020 Nov; 20(22):. PubMed ID: 33198083
    [TBL] [Abstract][Full Text] [Related]  

  • 20. A deep learning framework for automatic detection of arbitrarily shaped fiducial markers in intrafraction fluoroscopic images.
    Mylonas A; Keall PJ; Booth JT; Shieh CC; Eade T; Poulsen PR; Nguyen DT
    Med Phys; 2019 May; 46(5):2286-2297. PubMed ID: 30929254
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.