def stepDetection(dataSet):
xs = []
ys = []
zs = []
timestamps = []
headings = []
for data in dataSet:
xs.append(data[1])
ys.append(data[2])
zs.append(data[3])
timestamps.append(data[0])
headings.append(data[4])
x,y,z,timestamps,headings = np.array(xs), np.array(ys),np.array(zs), np.array(timestamps), np.array(headings)
# Filter Params
order = 3
fs = 50.0 # sample rate, Hz
cutoff = 3.667 # desired cutoff frequency of the filter, Hz
lowPassX = lpf.butter_lowpass_filter(x,cutoff,fs,order)
peaks,troughs,average, headingMovedX = ajpb.adaptive_jerk_pace_buffer(lowPassX, timestamps, headings)
lowPassY = lpf.butter_lowpass_filter(y,cutoff,fs,order)
peaks,troughs,average, headingMovedY = ajpb.adaptive_jerk_pace_buffer(lowPassY, timestamps, headings)
lowPassZ = lpf.butter_lowpass_filter(z,cutoff,fs,order)
peaks,troughs,average, headingMovedZ = ajpb.adaptive_jerk_pace_buffer(lowPassZ, timestamps, headings)
print("X: ",headingMovedX)
print("Y: ",headingMovedY)
print("Z: ",headingMovedZ)
return headingMovedY
def __init__(self, address):
with np.load(address) as data:
self.time_stamps = data["time_stamps"]
# filter the data
self.yawData = data["angular_velocity"][2]
order = 1
fs = 100 # sample rate, Hz
cutoff = 10 # desired cutoff frequency of the filter, Hz
self.yawData = butter_lowpass_filter(self.yawData, cutoff, fs,
order)
self.currIndex = 0
import adaptiveJerkPaceBuffer as ajpb
import lowPassFilter as lpf
import math
import numpy as np
import json
f = open("accel.json", "r")
values = json.load(f)
f.close()
x = []
timestamps = []
for val in values:
timestamps.append(val[0])
x.append(val[3]) #Change the axis where values are used
timestamps = np.array(timestamps)
x = np.array(x)
# Filter Params
order = 3
fs = 50.0 # sample rate, Hz
cutoff = 3.667 # desired cutoff frequency of the filter, Hz
lowPassR = lpf.butter_lowpass_filter(x,cutoff,fs,order)
peaks,troughs,average = ajpb.adaptive_jerk_pace_buffer(lowPassR, timestamps)
print("No of steps: %d" %len(troughs))
