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首页> 外文会议>IEEE Student Symposium in Biomedical Engineering 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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摘要

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：小波变换在访问时间和频率信息时的能力同时使其广泛用于分析电拍摄（EMG）等生物信号。离散小波变换（DWT）和静止小波变换（SWT）是基于小波的分析示例。两种分析都基于分解技术和将信号分成几个频带。不同的是DWT将在每个分解水平下将样本分辨率降到一半，而SWT则不是。本文在使用相同性质的情况下研究其能够在其EMG的去噪过程中分析。信号将使用“DB20”分解为五个分解，并使用相同的阈值设置去噪。将基于其对噪声比和肌肉疲劳检测的信号进行评估。结果表明，通过SWT的去噪过程会使比率更好。在几个读数中，DWT无法移除20Hz的角频率，导致疲劳检测中的误解。

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