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 *

253 related articles for article (PubMed ID: 29952759)

  • 1. Shoulder physiotherapy exercise recognition: machine learning the inertial signals from a smartwatch.
    Burns DM; Leung N; Hardisty M; Whyne CM; Henry P; McLachlin S
    Physiol Meas; 2018 Jul; 39(7):075007. PubMed ID: 29952759
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Adherence monitoring of rehabilitation exercise with inertial sensors: A clinical validation study.
    Bavan L; Surmacz K; Beard D; Mellon S; Rees J
    Gait Posture; 2019 May; 70():211-217. PubMed ID: 30903993
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Adherence Tracking With Smart Watches for Shoulder Physiotherapy in Rotator Cuff Pathology: Protocol for a Longitudinal Cohort Study.
    Burns D; Razmjou H; Shaw J; Richards R; McLachlin S; Hardisty M; Henry P; Whyne C
    JMIR Res Protoc; 2020 Jul; 9(7):e17841. PubMed ID: 32623366
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Physiotherapy Exercise Classification with Single-Camera Pose Detection and Machine Learning.
    Arrowsmith C; Burns D; Mak T; Hardisty M; Whyne C
    Sensors (Basel); 2022 Dec; 23(1):. PubMed ID: 36616961
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Evaluation of at-home physiotherapy.
    Boyer P; Burns D; Whyne C
    Bone Joint Res; 2023 Mar; 12(3):165-177. PubMed ID: 37051835
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Detection of Low Back Physiotherapy Exercises With Inertial Sensors and Machine Learning: Algorithm Development and Validation.
    Alfakir A; Arrowsmith C; Burns D; Razmjou H; Hardisty M; Whyne C
    JMIR Rehabil Assist Technol; 2022 Aug; 9(3):e38689. PubMed ID: 35998014
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Machine-learning models for shoulder rehabilitation exercises classification using a wearable system.
    Sassi M; Carnevale A; Mancuso M; Schena E; Pecchia L; Longo UG
    Knee Surg Sports Traumatol Arthrosc; 2024 Aug; ():. PubMed ID: 39154254
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Adherence Patterns and Dose Response of Physiotherapy for Rotator Cuff Pathology: Longitudinal Cohort Study.
    Burns D; Boyer P; Razmjou H; Richards R; Whyne C
    JMIR Rehabil Assist Technol; 2021 Mar; 8(1):e21374. PubMed ID: 33704076
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Wearable Motion Sensor Device to Facilitate Rehabilitation in Patients With Shoulder Adhesive Capsulitis: Pilot Study to Assess Feasibility.
    Chen YP; Lin CY; Tsai MJ; Chuang TY; Lee OK
    J Med Internet Res; 2020 Jul; 22(7):e17032. PubMed ID: 32457026
    [TBL] [Abstract][Full Text] [Related]  

  • 10. [Comparison of the results of supervised physiotherapy program and home-based exercise program in patients treated with arthroscopic-assisted mini-open rotator cuff repair].
    Büker N; Kitiş A; Akkaya S; Akkaya N
    Eklem Hastalik Cerrahisi; 2011 Dec; 22(3):134-9. PubMed ID: 22085347
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms.
    Cai S; Li G; Zhang X; Huang S; Zheng H; Ma K; Xie L
    J Neuroeng Rehabil; 2019 Nov; 16(1):131. PubMed ID: 31684970
    [TBL] [Abstract][Full Text] [Related]  

  • 12. 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]  

  • 13. Comparison of machine learning techniques to predict all-cause mortality using fitness data: the Henry ford exercIse testing (FIT) project.
    Sakr S; Elshawi R; Ahmed AM; Qureshi WT; Brawner CA; Keteyian SJ; Blaha MJ; Al-Mallah MH
    BMC Med Inform Decis Mak; 2017 Dec; 17(1):174. PubMed ID: 29258510
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Machine learning algorithms can classify outdoor terrain types during running using accelerometry data.
    Dixon PC; Schütte KH; Vanwanseele B; Jacobs JV; Dennerlein JT; Schiffman JM; Fournier PA; Hu B
    Gait Posture; 2019 Oct; 74():176-181. PubMed ID: 31539798
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Out-of-Distribution Detection of Human Activity Recognition with Smartwatch Inertial Sensors.
    Boyer P; Burns D; Whyne C
    Sensors (Basel); 2021 Mar; 21(5):. PubMed ID: 33804317
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. Deep learning-based detection and classification of geographic atrophy using a deep convolutional neural network classifier.
    Treder M; Lauermann JL; Eter N
    Graefes Arch Clin Exp Ophthalmol; 2018 Nov; 256(11):2053-2060. PubMed ID: 30091055
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data.
    Saccà V; Sarica A; Novellino F; Barone S; Tallarico T; Filippelli E; Granata A; Chiriaco C; Bruno Bossio R; Valentino P; Quattrone A
    Brain Imaging Behav; 2019 Aug; 13(4):1103-1114. PubMed ID: 29992392
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Single-Cell Phenotype Classification Using Deep Convolutional Neural Networks.
    Dürr O; Sick B
    J Biomol Screen; 2016 Oct; 21(9):998-1003. PubMed ID: 26950929
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Medhere: A Smartwatch-based Medication Adherence Monitoring System using Machine Learning and Distributed Computing.
    Ma J; Ovalle A; Woodbridge DM
    Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():4945-4948. PubMed ID: 30441452
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 13.