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Muscle Co-Contraction Detection in the Time-Frequency Domain
Francesco Di Nardo1, Martina Morano1, Annachiara Strazza1
1Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 60131 Ancona, Italy.
Sensors (Basel, Switzerland)
|July 9, 2022
Summary
This study introduces a new time-frequency domain method using continuous wavelet transform (CWT) for detecting muscle co-contraction. The CWT approach accurately identifies co-contraction timing and frequency, improving upon existing time-domain methods for motion control analysis.
Area of Science:
- Biomechanics
- Signal Processing
- Neuroscience
Background:
- Muscle co-contraction is crucial for effective motion control.
- Current detection methods are limited to the time domain.
- A novel time-frequency domain approach is proposed.
Purpose of the Study:
- To introduce and validate a new method for muscle co-contraction detection.
- To characterize co-contraction activity in the time-frequency domain.
- To improve the accuracy and scope of co-contraction analysis.
Main Methods:
- Utilized continuous wavelet transform (CWT) for time-frequency analysis.
- Applied CWT-based cross-energy localization (CWT coscalogram) to surface electromyographic (sEMG) signals.
- Implemented CWT-based denoising and validated against a double-threshold statistical algorithm.
Main Results:
- The CWT approach accurately predicted co-contraction timing in simulated and real sEMG data.
- The method showed minimal sensitivity to signal-to-noise ratio (SNR) variations.
- Revealed significant inter-subject and intra-stride variability in co-contraction frequency content.
Conclusions:
- The CWT method offers a significant advancement over existing time-domain techniques.
- Provides novel frequency information for muscle co-contraction.
- Demonstrates robustness and physiological relevance, supporting clinical applications.
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