def process(self):
# Grab only the new samples into a NumPy array
x = np.array(self.__l1[-self.__new_samples:])
# Filter the signal (detrend, LP, MA, etc…)
# ...
# ...
# ...
# Store the filtered data
ma = filt.moving_average(x, 20) # Compute Moving Average
dt = filt.detrend(ma) # Detrend the Signal
lp = filt.filter(self.__b, self.__a, dt) # Low-pass Filter Signal
grad = filt.gradient(lp) # Compute the gradient
x = filt.moving_average(
grad, 20) # Compute the moving average of the gradient
self.__filtered.add(x.tolist())
# Count the number of peaks in the filtered data
count, peaks = filt.count_peaks(x, self.__thresh_low,
self.__thresh_high)
# Update the step count and reset the new sample count
self.__steps += count
self.__new_samples = 0
# Return the step count, peak locations, and filtered data
return self.__steps, peaks, np.array(self.__filtered)
def process(self):
# Grab only the new samples into a NumPy array
x = np.array(self.__l1[-self.__new_samples:])
# Filter the signal (detrend, LP, MA, etc…)
x = filt.detrend(x)
# x = filt.filter(self.__b, self.__a, x)
x, self.__xi, self.__yi = filt.filter_ic(self.__b, self.__a, x,
self.__xi, self.__yi)
x = filt.gradient(x)
x = filt.moving_average(x, 25)
# Store the filtered data
self.__filtered.add(x.tolist())
# Count the number of peaks in the filtered data
count, peaks = filt.count_peaks(x, self.__thresh_low,
self.__thresh_high)
# Update the step count and reset the new sample count
self.__steps += count
self.__new_samples = 0
# Return the step count, peak locations, and filtered data
return self.__steps, peaks, np.array(self.__filtered)
def process(self):
# Grab only the new samples into a NumPy array
x = np.array(self.__ppg[-self.__new_samples:])
# Filter the signal (feel free to customize!)
x = filt.detrend(x, 25)
x = filt.moving_average(x, 5)
x = filt.gradient(x)
x = filt.normalize(x)
# Store the filtered data
self.__filtered.add(x.tolist())
# Find the peaks in the filtered data
_, peaks = filt.count_peaks(x, self.__thresh, 1)
# Update the step count and reset the new sample count
self.__hr = self.compute_heart_rate(peaks)
self.__new_samples = 0
# Return the heart rate, peak locations, and filtered data
return self.__hr, peaks, np.array(self.__filtered)
def load_data(filename):
return np.genfromtxt(filename, delimiter=",")
# Load the data as a 500x4 ndarray
data = load_data("./data/offline_data.csv")
t = data[:, 0]
t = (t - t[0]) / 1e3
ax = data[:, 1]
ay = data[:, 2]
az = data[:, 3]
l1 = filt.l1_norm(ax, ay, az) # Compute the L1-Norm
print(len(l1))
ma = filt.moving_average(l1, 20) # Compute Moving Average
dt = filt.detrend(ma) # Detrend the Signal
freqs, power = filt.psd(l1, len(l1), 50) # Power Spectral Density
bl, al = filt.create_filter(3, 1, "lowpass", fs) # Low-pass Filter Design
lp = filt.filter(bl, al, dt) # Low-pass Filter Signal
grad = filt.gradient(lp) # Compute the gradient
grad_avg = filt.moving_average(
grad, 20) # Compute the moving average of the gradient
count, peaks = filt.count_peaks(grad_avg, t_low,
t_high) # Find & Count the Peaks
# Plot the results
plt.plot(t, grad_avg)
import ECE16Lib.DSP as filt
from matplotlib import pyplot as plt
import numpy as np
# Load the data as a 500x2 ndarray and extract the 2 arrays
data = np.genfromtxt("./data/ramsink_01_13.csv", delimiter=",")
t = data[:, 0]
t = (t - t[0]) / 1e3
ppg = data[:, 1]
# Detrend the signal and plot the result
dt = filt.detrend(ppg, 25)
plt.subplot(211)
plt.title("Detrend")
plt.plot(t, ppg)
plt.subplot(212)
plt.plot(t, dt)
plt.show()
ma = filt.moving_average(dt, 5)
plt.subplot(211)
plt.title("Moving Average")
plt.plot(t, ppg)
plt.subplot(212)
plt.plot(t, ma)
plt.show()
grad = filt.gradient(ma)
def process(x): x = filt.detrend(x, 25) x = filt.moving_average(x, 5) x = filt.gradient(x) return filt.normalize(x)