0.05) between the data measured with the two systems. This means that the new system could streamline the movement and mobility of the body without affecting the experimental results. Through the real-time RMS, CI index, and green light indicator on the interface, the subjects can immediately know the situation of their muscles. The above results showed that EMG signals were not only able to be used to assess the degree of muscle strength by RMS and muscle fatigue by using median frequency, but also able to be used to observe the contribution mode of muscles through multi-channel EMG measurement.' > Airiti Library
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Reference ( 27 ) 〈TOP〉
  1. Brookham, R. L., Middlebrook, E. E., Grewal T., and Dickerson C. R., (2011). “The utility of an empirically derived co-activation ratio for muscle force prediction through optimization.” Journal of Biomechanics 44, 1582-1587
  2. Brookham, R. L., and Dickerson, C. R., (2013). “Empirical quantification of internal and external rotation muscular co-activation ratios in healthy shoulders.” Journal of Medical and Biological Engineering 50, 257-264.
  3. Criswell,E., (2011), Cram's Introduction to Surface Electromyography Second Edition, Sudbury: Jones and Bartlett, Part I
  4. Dowling, A., Barzilay, O., Lombrozo,Y., and Wolf, A., (2014). “An Adaptive Home-Use Robotic Rehabilitation System for the Upper Body.” IEEE Journal of Translational Engineering in Health and Medicine 02.
  5. Georgakis, A., Stergioulas, L. K., and Giakas G., (2003). “Fatigue Analysis of the Surface EMG Signal in Isometric Constant Force Contractions Using the Averaged Instantaneous Frequency.” IEEE Transactions on Biomedical Engineering 50, 262-265.
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