Literature DB >> 19329244

Performances of one-dimensional sonomyography and surface electromyography in tracking guided patterns of wrist extension.

Jing-Yi Guo1, Yong-Ping Zheng, Qing-Hua Huang, Xin Chen, Jun-Feng He, Helen Lai-Wa Chan.   

Abstract

Electromyography (EMG) and ultrasonography have been widely used for skeletal muscle assessment. Recently, it has been demonstrated that the muscle thickness change collected by ultrasound during contraction, namely sonomyography (SMG), can also be used for assessment of muscles and has the potential for prosthetic control. In this study, the performances of one-dimensional sonomyography (1D SMG) and surface EMG (SEMG) signal in tracking the guided patterns of wrist extension were evaluated and compared, and the potential of 1D SMG for skeletal muscle assessment and prosthetic control was investigated. Sixteen adult normal subjects including eight males and eight females participated in the experiment. The subject was instructed to perform the wrist extension under the guidance of displayed sinusoidal, square and triangular waveforms at movement rates of 20, 30, 50 cycles per min. SMG and SEMG root mean squares (RMS) were collected from the extensor carpi radialis, respectively, and their RMS errors in relation to the guiding signals were calculated and compared. It was found that the mean RMS tracking errors of SMG under different movement rates were 18.9% +/- 2.6% (mean+/-SD), 18.3% +/- 4.5%, and 17.0% +/- 3.4% for sinusoidal, square and triangular guiding waveforms, while the corresponding values for SEMG were 30.3% +/- 0.4%, 29.0% +/- 2.7% and 24.7% +/- 0.7%, respectively. Paired t test showed that the RMS errors of SMG tracking were significantly smaller than those of SEMG. Significant differences in RMS tracking errors of SMG among the three movement rates (p<0.01) for all the guiding waveforms were also observed using one-way analysis of variance (ANOVA). The results suggest that SMG signal, based on further improvement, has great potential to be an alternative method to SEMG to evaluate muscle function and control prostheses.

Mesh:

Year:  2009        PMID: 19329244     DOI: 10.1016/j.ultrasmedbio.2008.11.017

Source DB:  PubMed          Journal:  Ultrasound Med Biol        ISSN: 0301-5629            Impact factor:   2.998


  4 in total

1.  The analysis of surface EMG signals with the wavelet-based correlation dimension method.

Authors:  Gang Wang; Yanyan Zhang; Jue Wang
Journal:  Comput Math Methods Med       Date:  2014-04-27       Impact factor: 2.238

2.  The Reality of Myoelectric Prostheses: Understanding What Makes These Devices Difficult for Some Users to Control.

Authors:  Alix Chadwell; Laurence Kenney; Sibylle Thies; Adam Galpin; John Head
Journal:  Front Neurorobot       Date:  2016-08-22       Impact factor: 2.650

3.  Ultrasound Measurement of Skeletal Muscle Contractile Parameters Using Flexible and Wearable Single-Element Ultrasonic Sensor.

Authors:  Ibrahim AlMohimeed; Yuu Ono
Journal:  Sensors (Basel)       Date:  2020-06-27       Impact factor: 3.576

4.  Classifying Muscle States with One-Dimensional Radio-Frequency Signals from Single Element Ultrasound Transducers.

Authors:  Lukas Brausch; Holger Hewener; Paul Lukowicz
Journal:  Sensors (Basel)       Date:  2022-04-05       Impact factor: 3.576

  4 in total

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