Comparison of Different Time-Frequency Analyses Techniques Based on sEMG-Signals in Table Tennis: A Case Study

Q2 Computer Science
Bin Lin, S. Wong, A. Baca
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引用次数: 2

Abstract

Abstract The surface EMG signal in the action of dynamic contraction has more movement interference compared to sustained static contractions. In addition, the recruitment and de-recruitment of motor units causes a faster change in the surface EMG signal’s proprieties. Therefore, more complex techniques are required to extract information from the surface EMG signal. The standardized protocol for surface myoelectric signal measurement in table tennis was a case study in this research area. The Autoregressive method based on the Akaike Information Criterion, the Wavelet method based on intensity analysis, and the Hilbert-Huang transform method were used to estimate the muscle fatigue and non-fatigue condition. The result was that the Hilbert-Huang transform method was shown to be more reliable and accurate for studying the biceps brachii muscle in both conditions. However, the Wavelet method based on intensity analysis is more reliable and accurate for the pectoralis major muscle, deltoideus anterior muscle and deltoideus medialis muscle. The results suggest that different time-frequency analysis techniques influence different muscle analyses based on surface EMG signals in fatigue and non-fatigue conditions
基于表面肌电信号的乒乓球时频分析技术比较
摘要与持续的静态收缩相比,动态收缩动作中的表面肌电信号具有更多的运动干扰。此外,运动单位的募集和去募集会导致表面肌电信号的特性发生更快的变化。因此,需要更复杂的技术来从表面EMG信号中提取信息。乒乓球表面肌电信号测量的标准化协议是本研究领域的一个案例研究。采用基于Akaike信息准则的自回归方法、基于强度分析的小波方法和Hilbert-Huang变换方法来估计肌肉疲劳和非疲劳状态。结果表明,Hilbert-Huang变换方法在这两种条件下都能更可靠、更准确地研究肱二头肌。然而,基于强度分析的小波方法对胸大肌、三角肌前肌和三角肌内侧肌更可靠、更准确。结果表明,在疲劳和非疲劳条件下,不同的时频分析技术会影响基于表面肌电信号的不同肌肉分析
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来源期刊
International Journal of Computer Science in Sport
International Journal of Computer Science in Sport Computer Science-Computer Science (all)
CiteScore
2.20
自引率
0.00%
发文量
4
审稿时长
12 weeks
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