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首页> 外文会议>2015 IEEE Student Symposium in Biomedical Engineering amp; Sciences >Performance of DWT and SWT in muscle fatigue detection
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Performance of DWT and SWT in muscle fatigue detection

机译：DWT和SWT在肌肉疲劳检测中的性能

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Ability of wavelet transform in accessing time and frequency information at the same time make it widely used in analyzing bio-signals like electromyography (EMG). Discrete wavelet transforms (DWT) and stationary wavelet transform (SWT) are examples of analysis based on wavelet. Both analyses are based on decomposition technique and splitting signals into few frequency band. The different is DWT will down sample resolution into half at each decomposition level, while SWT is not. This paper is investigating both analyses in its ability on de-noising process of EMG using the same properties. The signals will be decomposed into five level of decomposition using `db20', and de-noised using the same threshold setting. The performance will be evaluated based on its signals to noise ratio and muscle fatigue detection. Results show that de-noising process through SWT give better signals to ratio. Inability in DWT removed 20Hz corner frequency in several reading lead to misinterpretation in fatigue detection.
机译：小波变换同时访问时间和频率信息的能力使其广泛用于分析肌电信号（EMG）等生物信号。离散小波变换（DWT）和平稳小波变换（SWT）是基于小波的分析示例。两种分析均基于分解技术，并将信号分成几个频段。不同之处在于DWT将在每个分解级别将样本分辨率降低到一半，而SWT则不会。本文正在研究使用相同属性对EMG进行去噪处理的能力的两种分析。使用“ db20”将信号分解为五个分解级别，并使用相同的阈值设置将其去噪。将根据其信噪比和肌肉疲劳检测来评估性能。结果表明，通过SWT进行的去噪过程可提供更好的信噪比。 DWT的无能消除了多个读数中20Hz的转折频率，导致疲劳检测中的误解。

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