BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

204 related articles for article (PubMed ID: 20703933)

  • 1. Muscle fatigue detection in EMG using time-frequency methods, ICA and neural networks.
    Subasi A; Kiymik MK
    J Med Syst; 2010 Aug; 34(4):777-85. PubMed ID: 20703933
    [TBL] [Abstract][Full Text] [Related]  

  • 2. An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise.
    Liu SH; Lin CB; Chen Y; Chen W; Huang TS; Hsu CY
    Sensors (Basel); 2019 Jul; 19(14):. PubMed ID: 31337107
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Detection of surface electromyography recording time interval without muscle fatigue effect for biceps brachii muscle during maximum voluntary contraction.
    Soylu AR; Arpinar-Avsar P
    J Electromyogr Kinesiol; 2010 Aug; 20(4):773-6. PubMed ID: 20211568
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Analysis of progression of fatigue conditions in biceps brachii muscles using surface electromyography signals and complexity based features.
    Karthick PA; Makaram N; Ramakrishnan S
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():3276-9. PubMed ID: 25570690
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms.
    Karthick PA; Ghosh DM; Ramakrishnan S
    Comput Methods Programs Biomed; 2018 Feb; 154():45-56. PubMed ID: 29249346
    [TBL] [Abstract][Full Text] [Related]  

  • 6. The comparison of wavelet- and Fourier-based electromyographic indices of back muscle fatigue during dynamic contractions: validity and reliability results.
    da Silva RA; Larivière C; Arsenault AB; Nadeau S; Plamondon A
    Electromyogr Clin Neurophysiol; 2008; 48(3-4):147-62. PubMed ID: 18551835
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Order Frequency Spectral Correlation Based Cyclo-nonstationary Analysis of Surface EMG Signals in Biceps Brachii Muscles.
    Jero SE; Ramakrishnan S
    Annu Int Conf IEEE Eng Med Biol Soc; 2019 Jul; 2019():7165-7168. PubMed ID: 31947487
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A new EMG frequency-based fatigue threshold test.
    Hendrix CR; Housh TJ; Johnson GO; Mielke M; Camic CL; Zuniga JM; Schmidt RJ
    J Neurosci Methods; 2009 Jun; 181(1):45-51. PubMed ID: 19394361
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Characterization of surface electromyography signals of biceps brachii muscle in fatigue using symbolic motif features.
    Makaram N; Swaminathan R
    Proc Inst Mech Eng H; 2020 Jun; 234(6):570-577. PubMed ID: 32181725
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Wavelet analysis of surface electromyography to determine muscle fatigue.
    Kumar DK; Pah ND; Bradley A
    IEEE Trans Neural Syst Rehabil Eng; 2003 Dec; 11(4):400-6. PubMed ID: 14960116
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Comparison of Fourier and wavelet transform procedures for examining the mechanomyographic and electromyographic frequency domain responses during fatiguing isokinetic muscle actions of the biceps brachii.
    Beck TW; Housh TJ; Johnson GO; Weir JP; Cramer JT; Coburn JW; Malek MH
    J Electromyogr Kinesiol; 2005 Apr; 15(2):190-9. PubMed ID: 15664148
    [TBL] [Abstract][Full Text] [Related]  

  • 12. The relationship between EMG median frequency and low frequency band amplitude changes at different levels of muscle capacity.
    Allison GT; Fujiwara T
    Clin Biomech (Bristol, Avon); 2002 Jul; 17(6):464-9. PubMed ID: 12135548
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Assessment of muscle load and fatigue with the usage of frequency and time-frequency analysis of the EMG signal.
    Bartuzi P; Roman-Liu D
    Acta Bioeng Biomech; 2014; 16(2):31-9. PubMed ID: 25088376
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Fatigue compensation during FES using surface EMG.
    Winslow J; Jacobs PL; Tepavac D
    J Electromyogr Kinesiol; 2003 Dec; 13(6):555-68. PubMed ID: 14573370
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A power spectral study of surface EMG of muscles subjected to non-repetitive task.
    Mehrotra R; Sahay KB
    Electromyogr Clin Neurophysiol; 1994; 34(5):265-74. PubMed ID: 7956875
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Local muscle endurance is associated with fatigue-based changes in electromyographic spectral properties, but not with conduction velocity.
    Beck TW; Ye X; Wages NP
    J Electromyogr Kinesiol; 2015 Jun; 25(3):451-6. PubMed ID: 25744086
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Assessment of average muscle fiber conduction velocity from surface EMG signals during fatiguing dynamic contractions.
    Farina D; Pozzo M; Merlo E; Bottin A; Merletti R
    IEEE Trans Biomed Eng; 2004 Aug; 51(8):1383-93. PubMed ID: 15311823
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Muscle fatigue assessment through electrodermal activity analysis during isometric contraction.
    Greco A; Guidi A; Felici F; Leo A; Ricciardi E; Bianchi M; Bicchi A; Citi L; Valenza G; Scilingo EP
    Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():398-401. PubMed ID: 29059894
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Multiscale feature based analysis of surface EMG signals under fatigue and non-fatigue conditions.
    Navaneethakrishna M; Ramakrishnan S;
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():4627-30. PubMed ID: 25571023
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Determination of muscle fatigue using dynamically embedded signals.
    Slack PS; Ma XH
    Proc Inst Mech Eng H; 2008 Jan; 222(1):41-50. PubMed ID: 18335717
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.