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 *

314 related articles for article (PubMed ID: 31141885)

  • 21. Detection of Near Falls Using Wearable Devices: A Systematic Review.
    Pang I; Okubo Y; Sturnieks D; Lord SR; Brodie MA
    J Geriatr Phys Ther; 2019; 42(1):48-56. PubMed ID: 29384813
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets.
    Aziz O; Klenk J; Schwickert L; Chiari L; Becker C; Park EJ; Mori G; Robinovitch SN
    PLoS One; 2017; 12(7):e0180318. PubMed ID: 28678808
    [TBL] [Abstract][Full Text] [Related]  

  • 23. On the Comparison of Wearable Sensor Data Fusion to a Single Sensor Machine Learning Technique in Fall Detection.
    Tsinganos P; Skodras A
    Sensors (Basel); 2018 Feb; 18(2):. PubMed ID: 29443923
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Recent trends in wearable device used to detect freezing of gait and falls in people with Parkinson's disease: A systematic review.
    Huang T; Li M; Huang J
    Front Aging Neurosci; 2023; 15():1119956. PubMed ID: 36875701
    [TBL] [Abstract][Full Text] [Related]  

  • 25. A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors.
    Yu X; Jang J; Xiong S
    Front Aging Neurosci; 2021; 13():692865. PubMed ID: 34335231
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Identifying balance impairments in people with Parkinson's disease using video and wearable sensors.
    Stack E; Agarwal V; King R; Burnett M; Tahavori F; Janko B; Harwin W; Ashburn A; Kunkel D
    Gait Posture; 2018 May; 62():321-326. PubMed ID: 29614464
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning.
    Kiprijanovska I; Gjoreski H; Gams M
    Sensors (Basel); 2020 Sep; 20(18):. PubMed ID: 32961750
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults.
    Scheurer S; Koch J; Kucera M; Bryn H; Bärtschi M; Meerstetter T; Nef T; Urwyler P
    Sensors (Basel); 2019 Mar; 19(6):. PubMed ID: 30889925
    [TBL] [Abstract][Full Text] [Related]  

  • 29. A cross-dataset deep learning-based classifier for people fall detection and identification.
    Delgado-Escaño R; Castro FM; Cózar JR; Marín-Jiménez MJ; Guil N; Casilari E
    Comput Methods Programs Biomed; 2020 Feb; 184():105265. PubMed ID: 31881399
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Covariance matrix based fall detection from multiple wearable sensors.
    Boutellaa E; Kerdjidj O; Ghanem K
    J Biomed Inform; 2019 Jun; 94():103189. PubMed ID: 31029654
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model.
    Hsieh CY; Liu KC; Huang CN; Chu WC; Chan CT
    Sensors (Basel); 2017 Feb; 17(2):. PubMed ID: 28208694
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: a machine learning approach.
    Bourke AK; Klenk J; Schwickert L; Aminian K; Ihlen EA; Mellone S; Helbostad JL; Chiari L; Becker C
    Annu Int Conf IEEE Eng Med Biol Soc; 2016 Aug; 2016():3712-3715. PubMed ID: 28269098
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Use of Average Vertical Velocity and Difference in Altitude for Improving Automatic Fall Detection from Trunk Based Inertial and Barometric Pressure Measurements.
    Musngi MM; Aziz O; Zihajehzadeh S; Nazareth GC; Tae CG; Park EJ
    Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():5146-5149. PubMed ID: 30441498
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Fall detection and fall risk assessment in older person using wearable sensors: A systematic review.
    Bet P; Castro PC; Ponti MA
    Int J Med Inform; 2019 Oct; 130():103946. PubMed ID: 31450081
    [TBL] [Abstract][Full Text] [Related]  

  • 35. AnkFall-Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks.
    Luna-Perejón F; Muñoz-Saavedra L; Civit-Masot J; Civit A; Domínguez-Morales M
    Sensors (Basel); 2021 Mar; 21(5):. PubMed ID: 33800347
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Innovative Head-Mounted System Based on Inertial Sensors and Magnetometer for Detecting Falling Movements.
    Lin CL; Chiu WC; Chu TC; Ho YH; Chen FH; Hsu CC; Hsieh PH; Chen CH; Lin CK; Sung PS; Chen PT
    Sensors (Basel); 2020 Oct; 20(20):. PubMed ID: 33053827
    [TBL] [Abstract][Full Text] [Related]  

  • 37. A wearable system for pre-impact fall detection.
    Nyan MN; Tay FE; Murugasu E
    J Biomech; 2008 Dec; 41(16):3475-81. PubMed ID: 18996529
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Real-Life/Real-Time Elderly Fall Detection with a Triaxial Accelerometer.
    Sucerquia A; López JD; Vargas-Bonilla JF
    Sensors (Basel); 2018 Apr; 18(4):. PubMed ID: 29621156
    [TBL] [Abstract][Full Text] [Related]  

  • 39. An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors.
    Mohammad Z; Anwary AR; Mridha MF; Shovon MSH; Vassallo M
    Sensors (Basel); 2023 May; 23(10):. PubMed ID: 37430686
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Hardware/Software Co-design of Fractal Features based Fall Detection System.
    Tahir A; Morison G; Skelton DA; Gibson RM
    Sensors (Basel); 2020 Apr; 20(8):. PubMed ID: 32325712
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 16.