Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study
By S. Karlsson, Jun Yu, M. Akay. IEEE Transactions on Biomedical Engineering, 2000
366 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Cardiology and Cardiovascular Medicine
View Paper: https://www.semanticscholar.org/paper/b3e4afc4af53aa5043d540207ccf20e3c25f9d0d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
- François Hug
- Dario Farina
- S. Dorel
- Roger M. Enoka
- Roberto Merletti
- Vladimir Medved
- Mario Cifrek
- Sasa Ostojic
In Cardiology and Cardiovascular Medicine
- Alexandre Campeau-Lecours
- Mohammed R. Al-Mulla
- U. Côté-Allard
- S. Dorel
- Francisco Sepulveda
- Dario Farina
- Clément M. Gosselin
- Alexandre Drouin
Citations in the corpus
- The extraction of neural strategies from the surface EMG. (2004, methodology): “Characteristic spectral frequencies can be computed by the classic periodogram (57) and autoregressivebased approaches (66) or by advanced methods such as Cohen’s class time-frequency distributions (12) and wavelet analysis (43).”
- Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning (2018, positive): “To the best of the authors’ knowledge, this is the first time that CWTs are employed as features for the classification of sEMG-based hand gesture recognition (although they have been proposed for the analysis of myoelectric signals [8]).”
- Surface EMG based muscle fatigue evaluation in biomechanics. (2009, methodology): “Karlsson et al. (2000) compared the short-time Fourier transform, the Wigner–Ville distribution, the Choi–Williams distribution, and the continuous wavelet transform in order to analyze myoelectric signals during dynamic contractions by estimating time-dependent spectral moments.”
- A Review of Non-Invasive Techniques to Detect and Predict Localised Muscle Fatigue (2011, methodology): “[171] found that the CWT resulted in a more precise estimation of EMG signals when applying various time-scale methods to analyse sEMG signals.”
- Electromyographic analysis of pedaling: a review. (2009, methodology): “Characteristic spectral frequencies can be computed by a classic periodogram (Merletti and Lo Conte, 1997), or by advanced methods such as wavelet analysis (Karlsson et al., 2000).”
- Myoelectric control systems - A survey (2007, background): “[22,37], that during relatively low-level (20–30% MVC), and short-time contractions (20–40 s), it can be assumed to be wide-sense stationary.”