Jayraj Desai
Published © GPL3+

Distinguish walking and running using Machine Learning

Gather data and develop algorithm to distinguish walking and running.

IntermediateFull instructions provided10 hours7,785
Distinguish walking and running using Machine Learning

Things used in this project

Hardware components

Arduino UNO
Arduino UNO
×1
Analog Accelerometer: ADXL335
Adafruit Analog Accelerometer: ADXL335
×1
micro SD card
×1
Male Header 40 Position 1 Row (0.1")
Male Header 40 Position 1 Row (0.1")
×1
AA Batteries
AA Batteries
×1
4xAA battery holder
4xAA battery holder
×1
USB-A to B Cable
USB-A to B Cable
×1
styrofoam
×1
Tape
×1
MicroSD Module (Generic)
×1

Software apps and online services

Arduino IDE
Arduino IDE
Octave

Story

Read more

Schematics

fritzing schematic

this is how connections are to be done.

Code

Arduino code

Arduino
#include <Wire.h>
#include <SD.h>

#define ADXL_module (0x53)

int count=0;                  //variable counts the number of readings written in log file

//change this number to change the number of desired reading
unsigned long measurement_to_take=360000;   //Number of desired readings

byte data[6];
char stringToPrint[200];
const int chipSelect = 10;

long randNumber;
char FileName[40];

void setup(){
  
  int existance_check;
  
  Serial.begin(9600);      //starting serial interface
   while (!Serial) {
    ; // wait for serial port to connect. Needed for Leonardo only
  }
  
  Serial.print("Initializing SD card...");
  // make sure that the default chip select pin is set to
  // output, even if you don't use it:
  pinMode(10, OUTPUT);
  
  // see if the card is present and can be initialized:
  if (!SD.begin(chipSelect)) {
    Serial.println("Card failed, or not present");
    // don't do anything more:
    return;
  }
  Serial.println("card initialized.");
  
//  existance_check=SD.exists("datalog.txt");
//  if(existance_check == 1)
//  {
//    SD.remove("datalog.txt");
//    Serial.println("Old datalog file removed");
//  }
  
  Wire.begin();            //starting I2C interface
//following code resets all bits of POWER_CTL register of ADXL345
  Wire.beginTransmission(ADXL_module);
  Wire.write(0x2D);
  Wire.write(0);
  Wire.endTransmission();

// Following code is to enable measurement 
  Wire.beginTransmission(ADXL_module);
  Wire.write(0x2D);
  Wire.write(8);
  Wire.endTransmission();
  
  //Following code sets data format to max resolution and +-8g
  Wire.beginTransmission(ADXL_module);
  Wire.write(0x31);
  Wire.write(0b00001010);
  Wire.endTransmission();
  
  //Following code sets the sampling rate of ADXL345
  Wire.beginTransmission(ADXL_module);
  Wire.write(0x2C);
  Wire.write(0b00001100);
  Wire.endTransmission();
  
  randomSeed(analogRead(0));
  randNumber = random(3000);
  Serial.println(randNumber);
  sprintf(FileName, "data%d.txt", randNumber);
}

void loop(){
  
  int xyzregister = 0x32;        //starting address of data register 
  int x,y,z;
  
  Wire.beginTransmission(ADXL_module);
  Wire.write(xyzregister);
  Wire.endTransmission();
  
  Wire.beginTransmission(ADXL_module);
  Wire.requestFrom(ADXL_module, 6);
  
  int i =0;
  while(Wire.available()){    //Wire.availabel() returns the number of bytes available for retrival with read()
    data[i] = Wire.read();    //Wire.read() reads a byte that was transmitted from a slave device to a master 
                              //after a call to Wire.requestFrom()
    i++;
  }
  Wire.endTransmission();
  
  x = (((int)data[1]) << 8) | data[0];      //As ADXL345 is 13 bit accelerometre, datais sent as 16 bits(2 byte)
  y = (((int)data[3]) << 8) | data[2];      //which we received in above while loop as individual bytes, using these
  z = (((int)data[5]) << 8) | data[4];      //3 statements we are properly formating the data.
  
  sprintf(stringToPrint, "%d,%d,%d,%ld", x,y,z,millis());
  
  //Following If segment writes data to the log file.
  if(count <= measurement_to_take)
  {
    File dataFile = SD.open(FileName,FILE_WRITE);
    // if the file is available, write to it:
    if (dataFile) 
    {
      dataFile.println(stringToPrint);
      dataFile.close();
      //print to serial monitor too
      Serial.println(stringToPrint);
      count++;
    }  
    // if the file isn't open, pop up an error:
    else {
      Serial.println("error opening datalog.txt");
    } 
  }
  
  // add delay to change the rate of data collection
 // delay(10);    DATA_RATE of ADXL345 is set to 100Hz hence no need of delay.
}

sample data

Plain text
6.277902,1873.131579,0
2.022879,2008.083333,0
5.859375,2022.960000,0
5.998884,1881.806452,0
6.138393,1766.375000,0
2.371652,364.054054,1
2.371625,399.545064,1
2.511161,391.741007,1
2.371652,341.769231,1

data_running_1

Plain text
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...

This file has been truncated, please download it to see its full contents.

Octave visualize file

MATLAB
clear; close all; clc

fprintf('Reading data ...\n');
%change the path to point to your file.
data = load('~/AStudy/Hobby/Machine_learning_approch/DATA_and_octave_scripts/DATA/Running_13bit_8g/running3.TXT');
Fs=(1/0.014);
nfft =1024;
X=data(:,1);
starting=1;
ending=size(X,1);
X_fft = fft(X(starting:ending).-mean(X(starting:ending)),nfft); 
Y=data(:,2);
Y_fft = fft(Y(starting:ending).-mean(Y(starting:ending)),nfft); 
Z=data(:,3);
Z_fft = fft(Z(starting:ending).-mean(Z(starting:ending)),nfft); 
figure (1);
subplot(3,1,1);
plot(X);
ylabel('X-data');
subplot(3,1,2);
plot(Y);
ylabel('Y-data');
subplot(3,1,3);
plot(Z);
ylabel('Z-data');

f_scale = (0:nfft/2)*Fs/nfft;

figure(2);
X_fft_abs= abs(X_fft.^2);
X_fft_abs = X_fft_abs(1:1+(nfft/2));
[X_max,X_freq_max_index]=max(X_fft_abs);
X_max_freq = f_scale(X_freq_max_index);
plot(f_scale,X_fft_abs);
ylabel('X-fft-data');

%figure(3);
Y_fft_abs= abs(Y_fft.^2);
Y_fft_abs = Y_fft_abs(1:1+(nfft/2));
[Y_max,Y_freq_max_index]=max(Y_fft_abs);
Y_max_freq = f_scale(Y_freq_max_index);
%plot(f_scale,Y_fft_abs);
%ylabel('Y-fft-data');

%figure(4);
Z_fft_abs= abs(Z_fft.^2);
Z_fft_abs = Z_fft_abs(1:1+(nfft/2));
[Z_max,Z_freq_max_index]=max(Z_fft_abs);
Z_max_freq = f_scale(Z_freq_max_index);
%plot(f_scale,Z_fft_abs);
%ylabel('Z-fft-data');

fprintf('Dominant_freq.: %f Hz \n',X_max_freq);

[peaksdata,index] = findpeaks(abs(X),'DoubleSided');
fprintf('peak average is.: %f \n',mean(peaksdata));

other octave codes

MATLAB
function [J, grad] = costFunction(theta, X, y)
%COSTFUNCTION Compute cost and gradient for logistic regression
%   J = COSTFUNCTION(theta, X, y) computes the cost of using theta as the
%   parameter for logistic regression and the gradient of the cost
%   w.r.t. to the parameters.

% Initialize some useful values
m = length(y); % number of training examples

% You need to return the following variables correctly 
J = 0;
grad = zeros(size(theta));

% ====================== YOUR CODE HERE ======================
% Instructions: Compute the cost of a particular choice of theta.
%               You should set J to the cost.
%               Compute the partial derivatives and set grad to the partial
%               derivatives of the cost w.r.t. each parameter in theta
%
% Note: grad should have the same dimensions as theta
%

J = -(1/m) * (y' * (log(sigmoid(theta'*X')))' + (1-y)' * (log(1- sigmoid(theta'*X')))'); 

grad = (1/m) * ((sigmoid(theta'*X') - y') * X);


% =============================================================

end

other octave codes

MATLAB
clear; close all; clc

data = load('ML_data.txt');
X = data(:,[1,2]);
y = data(:,3);

figure;
hold on;

pos = find(y == 1);
neg = find(y == 0);

plot(X(pos, 1),X(pos, 2), 'k+','LineWidth', 2,'MarkerSize', 7);
plot(X(neg, 1),X(neg, 2), 'ko','MarkerFaceColor','y','MarkerSize', 7);

% Put some labels 
hold on;
% Labels and Legend
xlabel('Dominant frequency in Hz');
ylabel('peak average');

% Specified in plot order
legend('Walking', 'Running');
hold off;

[m, n] = size(X);

X = [ones(m,1) X];

initial_theta = zeros(n+1,1);

[cost, grad] = costFunction(initial_theta,X,y);
fprintf('Cost at initial theta (zeros): %f\n', cost);
fprintf('Gradient at initial theta (zeros): \n');
fprintf(' %f \n', grad);

options = optimset('GradObj', 'on', 'MaxIter', 400);
[theta, cost] = ...
	fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);

fprintf('Cost at theta found by fminunc: %f\n', cost);
fprintf('theta: \n');
fprintf(' %f \n', theta);

plotDecisionBoundary(theta, X, y);
% Put some labels 
hold on;
% Labels and Legend
xlabel('Dominant frequency in Hz');
ylabel('peak average');

% Specified in plot order
legend('Walking', 'Running');
hold off;

other octave codes

MATLAB
function plotData(X, y)
%PLOTDATA Plots the data points X and y into a new figure 
%   PLOTDATA(x,y) plots the data points with + for the positive examples
%   and o for the negative examples. X is assumed to be a Mx2 matrix.

% Create New Figure
figure; hold on;

% ====================== YOUR CODE HERE ======================
% Instructions: Plot the positive and negative examples on a
%               2D plot, using the option 'k+' for the positive
%               examples and 'ko' for the negative examples.
%

% Find Indices of Positive and Negative Examples
pos = find(y == 1); 
neg = find(y == 0);

plot(X(pos, 1), X(pos, 2), 'k+','LineWidth', 2, ...
'MarkerSize', 7);

plot(X(neg, 1), X(neg, 2), 'ko','MarkerFaceColor', 'y', ...
'MarkerSize', 7);

% =========================================================================



hold off;

end

other octave codes

MATLAB
function plotDecisionBoundary(theta, X, y)
%PLOTDECISIONBOUNDARY Plots the data points X and y into a new figure with
%the decision boundary defined by theta
%   PLOTDECISIONBOUNDARY(theta, X,y) plots the data points with + for the 
%   positive examples and o for the negative examples. X is assumed to be 
%   a either 
%   1) Mx3 matrix, where the first column is an all-ones column for the 
%      intercept.
%   2) MxN, N>3 matrix, where the first column is all-ones

% Plot Data
plotData(X(:,2:3), y);
hold on

if size(X, 2) <= 3
    % Only need 2 points to define a line, so choose two endpoints
    plot_x = [min(X(:,2))-2,  max(X(:,2))+2];

    % Calculate the decision boundary line
    plot_y = (-1./theta(3)).*(theta(2).*plot_x + theta(1));

    % Plot, and adjust axes for better viewing
    plot(plot_x, plot_y)
    
    % Legend, specific for the exercise
    legend('Admitted', 'Not admitted', 'Decision Boundary')
    axis([2, 8, 0, 2200])
else
    % Here is the grid range
    u = linspace(-1, 1.5, 50);
    v = linspace(-1, 1.5, 50);

    z = zeros(length(u), length(v));
    % Evaluate z = theta*x over the grid
    for i = 1:length(u)
        for j = 1:length(v)
            z(i,j) = mapFeature(u(i), v(j))*theta;
        end
    end
    z = z'; % important to transpose z before calling contour

    % Plot z = 0
    % Notice you need to specify the range [0, 0]
    contour(u, v, z, [0, 0], 'LineWidth', 2)
end
hold off

end

other octave codes

MATLAB
function g = sigmoid(z)
%SIGMOID Compute sigmoid functoon
%   J = SIGMOID(z) computes the sigmoid of z.

% You need to return the following variables correctly 
g = zeros(size(z));

% ====================== YOUR CODE HERE ======================
% Instructions: Compute the sigmoid of each value of z (z can be a matrix,
%               vector or scalar).

g = 1./(1 + e.^(-z));

% =============================================================

end

Credits

Jayraj Desai

Jayraj Desai

10 projects • 70 followers
Just a Hobbyist, trying things

Comments