Name
Abstract | ||
|---|---|---|
The aim of this paper is to estimate muscle fatigue by using wavelet analysis method in SEMG signal analysis. A signal acquisition system is designed and forearm muscle fatigue experiments under static and dynamic contractions are performed. The wavelet analysis method is proposed to group the wavelet coefficients of SEMG signal into high frequency-band (100Hz-350Hz) and low frequency-band (13-22Hz). The amplitude of SEMG signal is determined by calculating the root mean square, the amplitude of high frequency is correlated to the force level and the amplitude of low frequency band which is correlated to the muscle fatigue shows an upward trend. Then correlation coefficients between RMS of low frequency band and MF, RMS of low frequency band and MDF in static contraction as well the first time-varying parameter in dynamic contraction are calculated. Results demonstrate that the wavelet analysis method is an effective analysis tool in muscle fatigue evaluation and it lays a foundation for studying at the muscle fatigue in a variety of muscle contraction modes. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ROBIO.2010.5723504 | ROBIO |
Keywords | Field | DocType |
correlation coefficient,wavelet analysis,surface electromyography,medical signal detection,static contraction,wavelet transforms,muscle fatigue estimation,fatigue feature,signal acquisition,forearm semg signal analysis,rms,feature extraction,muscle fatigue,correlation,electromyography,dynamic contraction,correlation methods,low frequency,time frequency analysis,high frequency,dynamics,force,root mean square,signal analysis | Muscle contraction,Electromyography,Time–frequency analysis,Root mean square,Engineering,Acoustics,Muscle fatigue,Amplitude,Wavelet,Wavelet transform | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4244-9319-7 |
Citations | PageRank | References |
3 | 0.62 | 1 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Baikun Wan | 1 | 104 | 16.90 |
| Lifeng Xu | 2 | 3 | 0.62 |
| Yue Ren | 3 | 3 | 0.96 |
| Lu Wang | 4 | 144 | 32.99 |
| Shuang Qiu | 5 | 32 | 7.78 |
| Xiaojia Liu | 6 | 3 | 0.62 |
| Xiuyun Liu | 7 | 3 | 1.64 |
| Hongzhi Qi | 8 | 49 | 20.61 |
| Dong Ming | 9 | 105 | 51.47 |
| Weijie Wang | 10 | 5 | 2.01 |