Human_Activity_Learning.m

%% Human Activity Learning Using Mobile Phone Data
% Human activity sensor data contains observations derived from
% sensor measurements taken from smartphones worn by people while doing
% different activities (walking, lying, sitting etc). The goal of this 
% example is to provide a strategy to build a classifier that can 
% automatically identify the activity type given the sensor measurements. 
%
% Copyright (c) 2015, MathWorks, Inc.

%% Description of the Data
%  The dataset consists of accelerometer and gyroscope data captured at 
% 50Hz. The raw sensor data contain fixed-width sliding windows of 2.56 sec 
% (128 readings/window). The activities performed by the subject include:
% 'Walking', 'ClimbingStairs', 'Sitting', 'Standing',and 'Laying'
%%
% *How to get the data:*
% Execute |downloadSensorData| and follow the instructions to download the
% and extract the data from the source webpage. After the files have been
% extracted run |saveSensorDataAsMATFiles|. This will create two MAT files: 
% |rawSensorData_train|  and |rawSensorData_test| with the raw sensor data
%%
% # *total_acc_(x/y/z)_train :*  Raw accelerometer sensor data
% # *body_gyro_(x/y/z)_train :*  Raw gyroscope sensor data 
% # *trainActivity :*  Training data labels
% # *testActivity  :*  Test data labels

%%
% Reference:
% 
% |Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L.
%  Reyes-Ortiz. Human Activity Recognition on Smartphones using a
%  Multiclass Hardware-Friendly Support Vector Machine. International
%  Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz,
%  Spain. Dec 2012|

%% Download data from source
%  If you are running this script for the first time, make sure that you
%  execute these functions. 
%%
% * |downloadSensorData| : This function will download the dataset and
% extract its contents to a folder called: UCI HAR Dataset
% This folder must be present before you execute |saveSensorDataAsMATFiles|
if ~exist('UCI HAR Dataset','file')
    downloadSensorData;
end

%% Load data frome individual files and save as MAT file for reuse
%%
% * |saveSensorDataAsMATFiles| : This function will load the data from the individual
% source files and save the data in a single MAT file for easy accesss 
if ~exist('rawSensorData_train.mat','file') && ~exist('rawSensorData_test.mat','file')
    saveSensorDataAsMATFiles;
end

%% Load Training Data
load rawSensorData_train

%% Display data summary
plotRawSensorData(total_acc_x_train, total_acc_y_train, ...
    total_acc_z_train,trainActivity,1000)

%% Create Table variable
rawSensorDataTrain = table(...
    total_acc_x_train, total_acc_y_train, total_acc_z_train, ...
    body_gyro_x_train, body_gyro_y_train, body_gyro_z_train);

%% Pre-process Training Data: *Feature Extraction*
% Lets start with a simple preprocessing technique. Since the raw sensor 
% data contain fixed-width sliding windows of 2.56sec (128 readings/window) 
% lets start with a simple average feature for every 128 points

humanActivityData = varfun(@Wmean,rawSensorDataTrain);
humanActivityData.activity = trainActivity;

%% Train a model and assess its performance using Classification Learner
classificationLearner

%% Additional Feature Extraction

T_mean = varfun(@Wmean, rawSensorDataTrain);
T_stdv = varfun(@Wstd,rawSensorDataTrain);
T_pca  = varfun(@Wpca1,rawSensorDataTrain);

humanActivityData = [T_mean, T_stdv, T_pca];
humanActivityData.activity = trainActivity;

%% Use the new features to train a model and assess its performance 
classificationLearner
%%
% 
% <<classificationLearner.png>>
% 

%% Load Test Data
load rawSensorData_test

%% Visualize classifier performance on test data
%
% Step 1: Create a table
rawSensorDataTest = table(...
    total_acc_x_test, total_acc_y_test, total_acc_z_test, ...
    body_gyro_x_test, body_gyro_y_test, body_gyro_z_test);

% Step 2: Extract features from raw sensor data
T_mean = varfun(@Wmean, rawSensorDataTest);
T_stdv = varfun(@Wstd,rawSensorDataTest);
T_pca  = varfun(@Wpca1,rawSensorDataTest);

humanActivityData = [T_mean, T_stdv, T_pca];
humanActivityData.activity = testActivity;

% Step 3: Use trained model to predict activity on new sensor data
% Make sure that you've exported 'trainedClassifier' from
% ClassificationLearner
plotActivityResults(trainedClassifier,rawSensorDataTest,humanActivityData,0.1)

%%
% 
% <<PredictionResults.png>>
%