Fezza HaiderGeorge Shaker

People and Fall Detection with Walabot

A system for detecting up to 5 stationary people simultaneously and determining if someone has fallen.

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People and Fall Detection with Walabot

Things used in this project

Hardware components



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TOF csv file

This file is needed for the Heatmap python script to work properly. The filename should be changed to 'TOF'



# --------------------------------------DATA COLLECTION-------------------------------------------
# |   File: DataCollection                                                                       |
# |   Type: .py (python)                                                                         |      
# |   Purpose: Subtracts an initially recorded background from newly collected data and saves it |
# |   into a csv file called 'raw_data'
# ------------------------------------------------------------------------------------------------

from __future__ import print_function
from sys import platform
from os import system
import WalabotAPI as wlbt
import matplotlib.pyplot as plt
from drawnow import drawnow
import numpy as np

# _______________________________________________________________________________________________________
#|                                            COLOR MATRIX                                               |
#| ______________________________________________________________________________________________________|

# The matrix with 40 different colors; this is to be used later when plotting data from the antenna
# pairs. THe size of this matrix is 40 because that correlates with the total number of available 
# antenna pairs.
"000000", "0000FF", "DC143C", "00FFFF", "008000", "0000FF", "ADD8E6", "F8F8FF", "F0FFF0", "6495ED",
"6A5ACD", "FAF0E6", "00008B", "B0E0E6", "2E8B57", "BDB76B", "FFFAFA", "A0522D", "0000CD", "4169E1",
"E0FFFF", "008000", "9370DB", "191970", "FFF8DC", "AFEEEE", "FFE4C4", "708090", "008B8B", "F0E68C",
"F5DEB3", "008080", "9932CC", "FA8072", "00BFFF", "663399", "8B0000", "4682B4", "DB7093", "778899"]

# _______________________________________________________________________________________________________
#|                                     INITIALIZING/CNNECTING WALABOT                                    |
#| ______________________________________________________________________________________________________|

# Load the python WalabotAPI into the program as 'wlbt' and initialize it

# Establish a connection between the Walabot and the computer

# Set sensor profile

# Set filtering to none

# ________________________________________________________________________________________________________
#|                                    GET ANTENNA PAIRS AND START WALABOT                                 |
#| _______________________________________________________________________________________________________|

# Get the list of antenna pairs that are available and store it in an array
pair = wlbt.GetAntennaPairs()

# Start the Walabot device

# ________________________________________________________________________________________________________
#|                                              LIVE-UPDATING GRAPH                                       |
#| _______________________________________________________________________________________________________|

# 'ant' stores the number of antenna pairs to be used for data collection
ant = 40

# This command creates a new csv file "raw_data.csv" if one does not exist in the program directory and in 
# the case it already does exist, it overwrites it
f = open("raw_data.csv", "w+")

# Initializing a zero-filled array, which is then updated with the collected data. The size of the array
# depends on the number of antenna pairs to be used.  
signal_list = [[0]]*ant 
new_signal_list = [[0]]*ant
background = []
summation = []

# Initializing the figure window for plotting
fig = plt.figure()
# The custom-made function to plot the data, depending on the number of antenna pairs chosen
# function for a lot of data (PROF_SENSOR PROFILE)

    # The for loop goes up to the size of 'ant', which is the number of antenna pairs, so that
    # the loop can plot data from every antenna pair used.
       # 'timeAxis' is a 1D array contining the time domain values for the obtained raw signals. 
       # 'new_signal_list' is a multidimensional array (the number of dimensions is equal to antenna
       #  pairs being used). Each element of the array refers to the obtained signal values for
       #  the correspoding antenna pair. For example, if the 'number' is 3, then the backscattered
       #  amplitudes btained from teh 3rd antenna pair will be plotted. 
       # 'COLORS' is used to change the line color for each antenna pair. 

def makeFig():
    for number in range(ant):
       plt.plot(timeAxis[::25], new_signal_list[number][::25], '#'+COLORS[number], linewidth=0.5)

# ________________________________________________________________________________________________________
#|                                                 CALIBRATION                                            |
#| _______________________________________________________________________________________________________|
# Scans the arena 10 times and takes the average of those scans for the background signals' frame

# Lets the user know calibration has begun

for i in range(10):
    wlbt. Trigger()

    for num in range(ant):
        targets = wlbt.GetSignal((pair[num]))

background = np.asarray(background)

for i in range(ant):
    summation.append(background[i] + background[i+ant] + background[i+(ant*2)] + background[i+(ant*3)] + background[i+(ant*4)] +
                    background[i+(ant*5)] + background[i+(ant*6)] + background[i+(ant*7)] + background[i+(ant*8)] + background[i+(ant*9)]) 

summation = np.asarray(summation)
average_background = summation/10

print("Calibration Complete")

# ________________________________________________________________________________________________________
#|                                           RAW SIGNALS' COLLECTION                                      |
#| _______________________________________________________________________________________________________|

# Using a 'try-and-except' here to allow user to stop the data collection whenever they want
# by using Ctrl+C
    j=1 # Counter variable for saving the figure

    # The infinite loop that runs until the user stops the program with keyboard interrupt.
    # This loop allows the Wlaabot to continuously scan the the arena that has been set.
    while True: 

        # Walabot API function used to initiate the scan 
        # The elements in the previously declared 'signal_list' are cleared. This is done so that 
        # every time this loop runs, the 'signal_list' is updated with the new values and doesn't
        # carry on the previous values. Having the previous values in the list would disrupt the 
        # plotting because the size of the 'signal_list' wouldn't match the 'timeAxis' in that case.
        del signal_list[0:ant]


        # The for loop goes up to the number of antenna pairs used. This loop allows the Walabot
        # to get the raw signals from each one of the selected number of antenna pairs, for every 
        # scan. 
            # 'GetSignal' from WalabotAPI which returns the time domain values and the returned signal
            # amplitudes. The data from this function is stored in 'targets' (2D array). The first array 
            # within 'targets' has the returned signal amplitudes and thus, those values are appended to 
            # 'signal_list'. The second array in 'targets' contains the time domain values and thus, is 
            # assigned to the 'timeAxis'
        for num in range(ant):
            targets = wlbt.GetSignal((pair[num]))
            timeAxis = targets[1]

   		# background frame subtracted  
        new_signal_list = signal_list-average_background

        # Loop for writing the collected data to a csv file. 
        for i in range(len(new_signal_list[0])):
            for k in range(ant):
        # The builtin function which updates the figure, with the plots from the previously defined
        # function

        # Saves the graphs from each scan of the Walabot (optional)
        # plt.savefig("frame"+str(j)+".png")



except KeyboardInterrupt:

wlbt.Stop()  # stops Walabot when finished scanning
wlbt.Disconnect()  # stops communication with Walabot


# ---------------------------------------------HEATMAP-------------------------------------------
# |   File: Heatmap                                                                              |
# |   Type: .py (python)                                                                         |      
# |   Purpose: Reads data from 'raw_data.csv' and 'TOF.csv' to plot ellipse-based heatmaps for   | 
# |   signal intensity at different locations, relative to Walabot.                              |
# ------------------------------------------------------------------------------------------------

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# _______________________________________________________________________________________________________
#|                                          REQUIRED PARAMETERS                                          |
#| ______________________________________________________________________________________________________|

# The user is prompted to enter the following parameters so that the program can identify which data frame
# and antenna pair to use, along with the range for the heatmap. Note that the range here is set by number 
# of points, NOT by the distance from Walabot. 
frameNumber =  int((input("Enter frame number: ")))
antennaPairInput = int(input("Specify antenna pair: "))
upperlimit = int(input("Enter the upper limit range (number of points): "))
lowerlimit = int(input("Enter the lower limit range (number of points): "))

# Based on user input, the starting value of range for reading rows from 'raw_data.csv' is calculated
# Since python indexing is zero-based, the user input needs to be subtracted by 1 
rowStartRange = (((frameNumber - 1) * 8192) + 1)
rowEndRange = (rowStartRange) + 8191
antennaPair = (antennaPairInput - 1)

# _______________________________________________________________________________________________________
#|                                            IMPORTING DATA                                             |
#| ______________________________________________________________________________________________________|

pd.set_option('precision', 18) # precision of values read from the csv file

# 'TOF.csv' is a one-column file and doesn't require a certain range of data to be read, hence, loadtxt
# from numpy library is used to import the data as a numpy array into 'roundtrip'. User needs to set 
# file path to 'TOF.csv' file
roundtrip = np.loadtxt(r'C:\Users\...\TOF.csv',dtype=float,delimiter=',',skiprows=42,usecols=(0,))

# 'raw_data.csv' is a large file (size varies depending on how much data is collected), so instead of 
# importing all the data from it, only a certain column (based on user specified antenna pair) from a 
# frame (also user input based) is imported into the variable 'power'. The absolute values of the signals
# is taken 
io = pd.read_csv('raw_data.csv', sep=",", header=None)
power = abs(io.ix[(rowStartRange + 40):rowEndRange,antennaPair].as_matrix())

# NOTE: The first 40 values from each frame are disregarded because they correspond to a distance (~5cm)  that  
# Walabot cannot detect with its long-range sensing profile. 

# _______________________________________________________________________________________________________
#|                                        PLOTTING ELLIPSES' HEATMAP                                     |
#| ______________________________________________________________________________________________________|

# Based on the upper and lower range limits, the max and min of the obtained signals is determined. These
# values are used to set the max and min intensity values for the heatmap
z_min = min(power[lowerlimit:upperlimit-1])
z_max = max(power[lowerlimit:upperlimit-1])

# 'theta' is used to convert the points on an ellipse into cartesian coordinates. Only one half of the ellipse
# is considered because the field-of-interest is in front of Walabot
theta = np.linspace(0,np.pi,225)

# Initializing empty numpy arrays, which will be updated in the for loop
majoraxisradius = np.empty(upperlimit)
minoraxisradius = np.empty(upperlimit)

# for loop used to make plot every ellipse in cartesian coordinates
for i in range(lowerlimit, upperlimit,10):

	majoraxisradius[i] = (roundtrip[i])/2
	minoraxisradius[i] = (np.sqrt((roundtrip[i])**2 - (0.0735**2)))/2 

	x = majoraxisradius[i] * np.cos(theta) 
	# 225 points for horizontal axis, for EACH ellipse 

	y = minoraxisradius[i] * np.sin(theta) 
	# 225 corresponding points for vertical axis, for EACH ellipse 

	z = np.full(225, power[i])
	# Setting the signal intensity value for the 'x' and 'y'  

	x1 = x.reshape(15,15)
	y1 = y.reshape(15,15)
	z1 = z.reshape(15,15)

	plt.pcolor(x1,y1,z1, cmap='jet', vmin=z_min, vmax=z_max) 
	# function in matplotlib for plotiing heatmaps

plt.colorbar() # adds the colorbar on the side of the heatmap
plt.show() # shows the colorbar


Fezza Haider

Fezza Haider

2 projects • 1 follower
Engineering Student, University of Waterloo
George Shaker

George Shaker

0 projects • 1 follower
Adj. Assistant Prof. @ University of Waterloo, ON, Canada