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
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))
crossings = pat.peak_accel_threshold(r, timestamps, 10.5) # print "Peak Acceleration Threshold Steps:", len(crossings)/2 pat_sum = pat_sum + len(crossings)/2 # Peak Jerk Threshold jumps, zeroes = sjt.step_jerk_threshold(r, timestamps) # print "Step Jerk Threshold Steps:", len(jumps) pjt_sum = pjt_sum + len(jumps) # Adaptive Step Jerk Threshold jumps, avgs = asjt.adaptive_step_jerk_threshold(r, timestamps) # print "Adaptive Step Jerk Threshold Steps:", len(jumps) # print "Final Step Jerk Average:", avgs[-1][1] apjt_sum = apjt_sum + len(jumps) peaks, troughs, avgs = ajpb.adaptive_jerk_pace_buffer(r, timestamps) # print "Adaptive Step Jerk Pace Buffer Steps:", len(troughs) ajpb_sum = ajpb_sum + len(peaks) #Android Steps a_steps = android_steps(trial) # print "Android Steps:", a_steps andr_sum = andr_sum + a_steps trial_count = trial_count + 1 print "Peak Acceleration Average:", pat_sum/trial_count, pat_sum, trial_count print "Step Jerk Average:", pjt_sum/trial_count, pjt_sum, trial_count print "Adaptive Step Jerk Average:",apjt_sum/trial_count, apjt_sum, trial_count print "Adaptive Jerk Pace Buffer Average:",ajpb_sum/trial_count, ajpb_sum, trial_count print "Android Steps:", andr_sum/trial_count, andr_sum, trial_count
crossings = pat.peak_accel_threshold(r, timestamps, 10.5)
# print "Peak Acceleration Threshold Steps:", len(crossings)/2
pat_sum = pat_sum + len(crossings) / 2
# Peak Jerk Threshold
jumps, zeroes = sjt.step_jerk_threshold(r, timestamps)
# print "Step Jerk Threshold Steps:", len(jumps)
pjt_sum = pjt_sum + len(jumps)
# Adaptive Step Jerk Threshold
jumps, avgs = asjt.adaptive_step_jerk_threshold(r, timestamps)
# print "Adaptive Step Jerk Threshold Steps:", len(jumps)
# print "Final Step Jerk Average:", avgs[-1][1]
apjt_sum = apjt_sum + len(jumps)
peaks, troughs, avgs = ajpb.adaptive_jerk_pace_buffer(
r, timestamps)
# print "Adaptive Step Jerk Pace Buffer Steps:", len(troughs)
ajpb_sum = ajpb_sum + len(peaks)
#Android Steps
a_steps = android_steps(trial)
# print "Android Steps:", a_steps
andr_sum = andr_sum + a_steps
trial_count = trial_count + 1
print "Peak Acceleration Average:", pat_sum / trial_count, pat_sum, trial_count
print "Step Jerk Average:", pjt_sum / trial_count, pjt_sum, trial_count
print "Adaptive Step Jerk Average:", apjt_sum / trial_count, apjt_sum, trial_count
print "Adaptive Jerk Pace Buffer Average:", ajpb_sum / trial_count, ajpb_sum, trial_count
print "Android Steps:", andr_sum / trial_count, andr_sum, trial_count
