275 related articles for article (PubMed ID: 26887581)
1. Comparative study of wavelet denoising in myoelectric control applications.
Sharma T; Veer K
J Med Eng Technol; 2016; 40(3):80-6. PubMed ID: 26887581
[TBL] [Abstract][Full Text] [Related]
2. EMG classification using wavelet functions to determine muscle contraction.
Sharma T; Veer K
J Med Eng Technol; 2016; 40(3):99-105. PubMed ID: 26942656
[TBL] [Abstract][Full Text] [Related]
3. Surface electromyography signal denoising via EEMD and improved wavelet thresholds.
Sun Z; Xi X; Yuan C; Yang Y; Hua X
Math Biosci Eng; 2020 Oct; 17(6):6945-6962. PubMed ID: 33378883
[TBL] [Abstract][Full Text] [Related]
4. Extracting effective features of SEMG using continuous wavelet transform.
Kilby J; Hosseini HG
Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():1704-7. PubMed ID: 17946475
[TBL] [Abstract][Full Text] [Related]
5. A bi-dimensional index for the selective assessment of myoelectric manifestations of peripheral and central muscle fatigue.
Mesin L; Cescon C; Gazzoni M; Merletti R; Rainoldi A
J Electromyogr Kinesiol; 2009 Oct; 19(5):851-63. PubMed ID: 18824375
[TBL] [Abstract][Full Text] [Related]
6. Discrete wavelet transform analysis of surface electromyography for the fatigue assessment of neck and shoulder muscles.
Chowdhury SK; Nimbarte AD; Jaridi M; Creese RC
J Electromyogr Kinesiol; 2013 Oct; 23(5):995-1003. PubMed ID: 23787059
[TBL] [Abstract][Full Text] [Related]
7. Hybrid fusion of linear, non-linear and spectral models for the dynamic modeling of sEMG and skeletal muscle force: an application to upper extremity amputation.
Potluri C; Anugolu M; Schoen MP; Subbaram Naidu D; Urfer A; Chiu S
Comput Biol Med; 2013 Nov; 43(11):1815-26. PubMed ID: 24209927
[TBL] [Abstract][Full Text] [Related]
8. sEMG wavelet-based indices predicts muscle power loss during dynamic contractions.
González-Izal M; Rodríguez-Carreño I; Malanda A; Mallor-Giménez F; Navarro-Amézqueta I; Gorostiaga EM; Izquierdo M
J Electromyogr Kinesiol; 2010 Dec; 20(6):1097-106. PubMed ID: 20579906
[TBL] [Abstract][Full Text] [Related]
9. [Research on surface electromyographic signal decomposition based on the level of contraction force].
Deng H; Chen X; Yao B; Lou Z; Yang J
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2012 Dec; 29(6):1046-51, 1077. PubMed ID: 23469528
[TBL] [Abstract][Full Text] [Related]
10. EWT-IIT: a surface electromyography denoising method.
Xiao F
Med Biol Eng Comput; 2022 Dec; 60(12):3509-3523. PubMed ID: 36216989
[TBL] [Abstract][Full Text] [Related]
11. Muscle Co-Contraction Detection in the Time-Frequency Domain.
Di Nardo F; Morano M; Strazza A; Fioretti S
Sensors (Basel); 2022 Jun; 22(13):. PubMed ID: 35808382
[TBL] [Abstract][Full Text] [Related]
12. Estimation of handgrip force using frequency-band technique during fatiguing muscle contraction.
Soo Y; Sugi M; Yokoi H; Arai T; Nishino M; Kato R; Nakamura T; Ota J
J Electromyogr Kinesiol; 2010 Oct; 20(5):888-95. PubMed ID: 19837604
[TBL] [Abstract][Full Text] [Related]
13. Continuous wavelet transform in the evaluation of stretch reflex responses from surface EMG.
Leao RN; Burne JA
J Neurosci Methods; 2004 Feb; 133(1-2):115-25. PubMed ID: 14757352
[TBL] [Abstract][Full Text] [Related]
14. Optimized wavelets for blind separation of nonstationary surface myoelectric signals.
Farina D; Lucas MF; Doncarli C
IEEE Trans Biomed Eng; 2008 Jan; 55(1):78-86. PubMed ID: 18232349
[TBL] [Abstract][Full Text] [Related]
15. Time- and frequency-domain monitoring of the myoelectric signal during a long-duration, cyclic, force-varying, fatiguing hand-grip task.
Clancy EA; Bertolina MV; Merletti R; Farina D
J Electromyogr Kinesiol; 2008 Oct; 18(5):789-97. PubMed ID: 17434755
[TBL] [Abstract][Full Text] [Related]
16. A new modified wavelet-based ECG denoising.
Wang Z; Zhu J; Yan T; Yang L
Comput Assist Surg (Abingdon); 2019 Oct; 24(sup1):174-183. PubMed ID: 30689434
[No Abstract] [Full Text] [Related]
17. Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features.
Hari LM; Venugopal G; Ramakrishnan S
Proc Inst Mech Eng H; 2022 Feb; 236(2):208-217. PubMed ID: 34633247
[TBL] [Abstract][Full Text] [Related]
18. Denoising of HD-sEMG signals using canonical correlation analysis.
Al Harrach M; Boudaoud S; Hassan M; Ayachi FS; Gamet D; Grosset JF; Marin F
Med Biol Eng Comput; 2017 Mar; 55(3):375-388. PubMed ID: 27221811
[TBL] [Abstract][Full Text] [Related]
19. Analysis and classification of compressed EMG signals by wavelet transform via alternative neural networks algorithms.
Ozsert M; Yavuz O; Durak-Ata L
Comput Methods Biomech Biomed Engin; 2011 Jun; 14(6):521-5. PubMed ID: 20645198
[TBL] [Abstract][Full Text] [Related]
20. [Surface electromyogram denoising using adaptive wavelet thresholding].
Lou Z; Deng Hao ; Chen X; Yao B; Yang J
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2014 Aug; 31(4):723-8. PubMed ID: 25464776
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
