用于特征提取的Python FFT

我希望对人体加速度计数据进行特征提取,以用于活动识别。我的数据采样率是100Hz。

import numpy as np

def fft_transform (window_data):

    fft_data = []
    fft_freq = []
    power_spec = []

    for window in window_data:

        fft_window = np.fft.fft(window)
        fft_data.append(fft_window)

        freq  = np.fft.fftfreq(np.array(window).shape[-1], d=0.01)
        fft_freq.append(freq )

        fft_ps = np.abs(fft_window)**2
        power_spec.append(fft_ps)

    return fft_data, fft_freq, power_spec

这会产生如下输出:

fft_数据

array([  2.92394828e+01 +0.00000000e+00j,
        -6.00104665e-01 -7.57915977e+00j,
        -1.02677676e+01 -1.55806119e+00j,
        -7.17273995e-01 -6.64043705e+00j,
         3.45758079e+01 +3.60869421e+01j,
         etc..

array([  0.      ,   0.390625,   0.78125 ,   1.171875,   1.5625  , etc...

功率谱

array([  8.54947354e+02,   5.78037884e+01,   1.07854606e+02,
         4.46098863e+01,   2.49775388e+03,   etc...

import matplotlib.pyplot as plt
fig = plt.figure(figsize=(width, height))

fig1= fig.add_subplot(221)
fig2= fig.add_subplot(222)
fig1.plot(fst_freq, fst_ps)
fig2.plot(fst_freq, np.log10(fst_ps))
plt.show()

我正在寻找一些关于下一步提取特征的建议。谢谢