Detection of chaos in human fatigue mechanomyogarphy signals
暂无分享,去创建一个
[1] Vinzenz von Tscharner,et al. Time/frequency events of surface mechanomyographic signals resolved by nonlinearly scaled wavelets , 2008, Biomed. Signal Process. Control..
[2] Dario Farina,et al. Spectral moments of mechanomyographic signals recorded with accelerometer and microphone during sustained fatiguing contractions , 2006, Medical and Biological Engineering and Computing.
[3] M Paloheimo,et al. Quantitative surface electromyography (qEMG): applications in anaesthesiology and critical care. , 1990, Acta anaesthesiologica Scandinavica. Supplementum.
[4] Diks,et al. Efficient implementation of the gaussian kernel algorithm in estimating invariants and noise level from noisy time series data , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.
[5] Katsumi Mita,et al. Time-frequency analysis of mechanomyogram during sustained contractions with muscle fatigue , 2004 .
[6] K. Sakamoto,et al. Chaotic analysis of electromyography signal at low back and lower limb muscles during forward bending posture. , 2005, Electromyography and clinical neurophysiology.
[7] A. Veicsteinas,et al. Electromyogram and mechanomyogram changes in fresh and fatigued muscle during sustained contraction in men , 1998, European Journal of Applied Physiology and Occupational Physiology.
[8] C. Orizio. Muscle sound: bases for the introduction of a mechanomyographic signal in muscle studies. , 1993, Critical reviews in biomedical engineering.
[9] Alan Gerard Outten. Analysis of human muscle activity , 1996 .
[10] Mauricio Barahona,et al. Detection of nonlinear dynamics in short, noisy time series , 1996, Nature.
[11] Mauricio Barahona,et al. Titration of chaos with added noise , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[12] H Nieminen,et al. Evidence of deterministic chaos in the myoelectric signal. , 1996, Electromyography and clinical neurophysiology.