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中文摘要：
将小波变换用于处理人体行走时产生的加速度信号.利用离散小波变换的多尺度、多分辨率特性对原始加速度信号进行尺度分解,在对小波基以及分解尺度进行合理选取后准确地从加速度信号中提取出隐藏的步态节律.与利用阈值法直接对原始加速度信号提取峰值的算法比较后发现：利用小波分解得到与步态节律相关的特征尺度后再进行峰值检测能显著地提高信号峰值的检出率;即使当原始信号存在较严重的噪声干扰时,该方法也能保证所提取出的步态序列的准确性.这对于步态序列的后续分析具有至关重要的意义.研究表明,离散小波变换是一种有效的提取步态节律的方法,这种方法对于其他生理信号特征节律的提取具有重要的参考价值.
英文摘要：
The wavelet transform was applied to process the accelerometer signals derived from human walking. The accelerometer signals were first decomposed at different levels utilizing the multi-scale and multi-resolution characteristics of the discrete wavelet transform. After the determination of both the mother wavelet and the optimal decomposition level, human gait series can thus be extracted from the eigen scale of the accelerometer signal. Compared with the method that detects peak values directly from accelerometer signals by thresholding,the wavelet transform gives higher detection rate of peak values on the eigen scale of the accelerometer signals. Even when the accelerometer signals are exposed to serious noise,experimental results still demonstrate that the wavelet approach can guarantee the precision of the extracted gait series,which is of vital importance for the subsequent analyses. It can be concluded that wavelet transform is an effective tool for the extraction of gait rhythmicity. The wavelet transform will be helpful in identifying the characteristics of other physiological signals.
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