MATLAB Examples

Human Activity Classification based on Smartphone Sensor Signals

Classify using supervised learning (FULL script)

Contents

Supervised Learning

In this section we will apply a supervised learning approach (outputs are known) and iterate using different Supervised Learning algorithms

Clear all variables that are not relevant & load pre-saved variables

Clear nonrelevant variables

clear; clc

% Load set of feature vectors (feat) and cell array of feature names
% (featlabels)
load('Data\Prepared_iPhone_32\BufferFeatures60.mat')

% Load buffered signals (here only using known activity IDs for buffers)
load('Data\Prepared_iPhone_32\BufferedAccelerations.mat')

Data preparation

Activities = categorical(actid,(1:numel(actnames)),actnames);

% Cross Validation
cv = cvpartition(length(actid),'holdout',0.1); % 10% size for Test

% Training set
Xtrain = feat(training(cv),:);
Ytrain = Activities(training(cv));

% Test set
Xtest = feat(test(cv),:);
Ytest = Activities(test(cv));

Dataset_train = [array2table(Xtrain) table(Ytrain)];
Dataset_test = array2table(Xtest);
Dataset_train.Properties.VariableNames = [featlabels' 'Activities'];
Dataset_test.Properties.VariableNames = featlabels;

% View some stats on some features
grpstats(Dataset_train,'Activities','mean','Datavars',featlabels(1:6))
ans = 

                Activities    GroupCount    mean_TotalAccXMean    mean_TotalAccYMean    mean_TotalAccZMean    mean_BodyAccXRMS    mean_BodyAccYRMS    mean_BodyAccZRMS
                __________    __________    __________________    __________________    __________________    ________________    ________________    ________________

    Sitting     Sitting       5260           -3.2669              -3.1347                  7.4826             0.044261            0.044248            0.033736        
    Standing    Standing      5589           0.30591              -4.4346               0.0045702              0.06581            0.038985            0.062751        
    Walking     Walking       4872          0.014923              -1.3015                 0.58783               2.3559              3.3679              2.7939        
    Running     Running       3555           0.56194              -10.157                  1.4732               5.4044              11.397              5.9691        
    Dancing     Dancing       2392           0.56227              -9.5047                  1.7717               3.7561              12.895               4.415        

Statistical Feature Importance (parametric)

The complexity of the model and its likelihood to overfit can be reduced by reducing the number of features included. A metric like a paired t test can be used to identify the top features that help separate classes. We can then investigate later whether a model trained using only these features can perform comparably with a model trained on the entire dataset

[featIdx, pair] = featureScore(feat, actid, 10);
disp('Identified Discriminative features (using paired t-test):');
for i = 1:size(pair,1)
    fprintf('%d and %d: ', pair(i,1), pair(i,2));
    fprintf('%s, ', featlabels{featIdx(i,:)});
    fprintf('\n');
end

% Build digraph showcasing the 5 most important features which dissociate activities
[connections,nodeOutmap,nodeFeatmap] = build_connections(pair,featIdx);
ImportantFeat = cellstr(categorical(unique(featIdx),(1:numel(featlabels)),featlabels));
G = table([connections(:,1) connections(:,2)],ones(size(connections,1),1),repmat({'t-test Interaction'},size(connections,1),1),...
    'VariableNames',{'EndNodes' 'Weight' 'Code'});
G = graph(G,table([actnames';ImportantFeat],'VariableNames',{'Name'}));
colormap hsv
nColors = degree(G);
nSizes = 6*sqrt(nColors-min(nColors)+0.2);
plot(G,'MarkerSize',nSizes,'NodeCData',nColors,'EdgeAlpha',0.1,'Layout','force');
set(gca,'XColor','w','YColor','w'); box off
Identified Discriminative features (using paired t-test):
1 and 2: TotalAccZMean, TotalAccXMean, BodyAccZSpectPos2, BodyAccZSpectPos3, BodyAccZSpectPos4, BodyAccZSpectPos5, BodyAccZSpectPos1, BodyAccZSpectPos6, BodyAccZCovFirstPos, BodyAccXSpectPos2, 
1 and 3: BodyAccZRMS, BodyAccXRMS, BodyAccYRMS, TotalAccZMean, BodyAccZPowerBand1, BodyAccZCovZeroValue, BodyAccXCovZeroValue, BodyAccXPowerBand2, BodyAccZPowerBand2, BodyAccYCovZeroValue, 
1 and 4: BodyAccYRMS, BodyAccXRMS, BodyAccZRMS, BodyAccYCovZeroValue, BodyAccYPowerBand2, BodyAccXCovZeroValue, TotalAccYMean, BodyAccXPowerBand2, TotalAccZMean, BodyAccZCovZeroValue, 
1 and 5: BodyAccYRMS, BodyAccXRMS, BodyAccYPowerBand2, BodyAccYCovZeroValue, BodyAccZRMS, BodyAccYCovFirstValue, BodyAccXPowerBand2, TotalAccZMean, TotalAccYMean, BodyAccXCovZeroValue, 
2 and 3: BodyAccZRMS, BodyAccXRMS, BodyAccYRMS, BodyAccZCovZeroValue, BodyAccZPowerBand1, BodyAccXCovZeroValue, BodyAccXPowerBand2, BodyAccZPowerBand2, BodyAccYCovZeroValue, BodyAccYPowerBand2, 
2 and 4: BodyAccYRMS, BodyAccXRMS, BodyAccZRMS, BodyAccYCovZeroValue, BodyAccYPowerBand2, BodyAccXCovZeroValue, BodyAccXPowerBand2, BodyAccZCovZeroValue, BodyAccZPowerBand2, BodyAccYCovFirstValue, 
2 and 5: BodyAccYRMS, BodyAccXRMS, BodyAccYPowerBand2, BodyAccYCovZeroValue, BodyAccYCovFirstValue, BodyAccZRMS, BodyAccXPowerBand2, BodyAccXCovZeroValue, BodyAccXSpectVal6, BodyAccYSpectVal6, 
3 and 4: BodyAccYRMS, BodyAccXRMS, BodyAccYPowerBand2, BodyAccYCovZeroValue, BodyAccXCovZeroValue, BodyAccZRMS, BodyAccXPowerBand2, BodyAccYCovFirstValue, BodyAccZPowerBand2, BodyAccZCovZeroValue, 
3 and 5: BodyAccYRMS, BodyAccYPowerBand2, BodyAccYCovZeroValue, BodyAccYCovFirstValue, BodyAccYSpectVal6, BodyAccXRMS, TotalAccZMean, BodyAccXCovZeroValue, BodyAccXPowerBand2, BodyAccYSpectVal3, 
4 and 5: BodyAccXRMS, BodyAccYCovFirstPos, BodyAccXPowerBand2, BodyAccXCovZeroValue, BodyAccYCovFirstValue, BodyAccZRMS, BodyAccXSpectVal6, BodyAccXSpectVal3, BodyAccXSpectVal4, BodyAccXSpectVal5, 

Train Classification Tree

tic
myTree = fitctree(Dataset_train,'Activities','SplitCriterion', 'gdi', ...
    'MaxNumSplits', 20, 'Surrogate', 'off');
toc

% Predict on Training & Test sets
Y_CT_train = predict(myTree,Dataset_train);
Y_CT_test = predict(myTree,Dataset_test);
view(myTree,'mode','graph')

% Calculate confusion matrices using prediction results
C_CT = confusionmat(Ytest,Y_CT_test);
C_CT_train = confusionmat(Ytrain,Y_CT_train);
Elapsed time is 2.761747 seconds.

Train Multiclass SVM

templateSVM = templateSVM('KernelFunction', 'linear', 'PolynomialOrder', [], ...
    'KernelScale', 'auto', 'BoxConstraint', 1, 'Standardize', true);
tic
mySVM = fitcecoc(Dataset_train,'Activities','Learners', templateSVM, ...
    'Coding', 'onevsall');
toc

Y_SVM_train = predict(mySVM,Dataset_train);
Y_SVM_test = predict(mySVM,Dataset_test);
C_SVM = confusionmat(Ytest,Y_SVM_test);
C_SVM_train = confusionmat(Ytrain,Y_SVM_train);
Elapsed time is 47.138819 seconds.

Train k-Nearest Neighbor

tic
myKNN = fitcknn(Dataset_train,'Activities','Distance', 'Hamming','Exponent',[],...
    'NumNeighbors', 10, 'DistanceWeight', 'Inverse', 'Standardize', false);
toc

Y_KNN_train = predict(myKNN,Dataset_train);
Y_KNN_test = predict(myKNN,Dataset_test);

C_KNN = confusionmat(Ytest,Y_KNN_test);
C_KNN_train = confusionmat(Ytrain,Y_KNN_train);
Elapsed time is 0.146296 seconds.

Set-Up & Train Naive Bayes

tic
myNB = fitcnb(Dataset_train,'Activities','Distribution','normal');
toc

% Predict on Training & Test sets
Y_NB_train = predict(myNB,Dataset_train);
Y_NB_test = predict(myNB,Dataset_test);

C_NB = confusionmat(Ytest,Y_NB_test);
C_NB_train = confusionmat(Ytrain,Y_NB_train);
Elapsed time is 43.948930 seconds.

Set-Up & Train neural network

Create Dummy Variable Groups

Ytrain_bingrps = dummyvar(Ytrain);

% Reset random number generators (for repetability)
rng default

% Initialize a Neural Network with 15 nodes in hidden layer
net = patternnet(15);
net.divideParam.trainRatio = 90/100;
net.divideParam.valRatio = 5/100;
net.divideParam.testRatio = 5/100;
% net.trainFcn = 'trainbr';  % Bayesian Regularization backpropagation.

p = gcp('nocreate');
if isempty(p)
    p = parpool('local');
end

% For details about customizing the training function refer to the
% web(fullfile(docroot, 'nnet/ug/choose-a-multilayer-neural-network-training-function.html'))
tic
net = train(net, Xtrain', Ytrain_bingrps','UseParallel','always');
toc

outputs_train = net(Xtrain');

% Predict on Training set
[~, Y_NN_train] = max(outputs_train,[],1);
Y_NN_train = categorical(Y_NN_train,1:length(actnames),actnames)';

% Predict on Test set
outputs_test = net(Xtest');
[~, Y_NN_test] = max(outputs_test,[],1);
Y_NN_test = categorical(Y_NN_test,1:length(actnames),actnames)';

C_NN = confusionmat(Ytest,Y_NN_test);
C_NN_train = confusionmat(Ytrain,Y_NN_train);
Starting parallel pool (parpool) using the 'local' profile ... connected to 4 workers.
Elapsed time is 17.427224 seconds.

Train network on reduced feature set (Optionnal)

netRed = patternnet(15); % Initialize network
netRed.divideParam.trainRatio = 90/100;
netRed.divideParam.valRatio = 5/100;
netRed.divideParam.testRatio = 5/100;

redF = unique(featIdx(:));
XtrainRed = Xtrain(:,redF);
XtestRed = Xtest(:,redF);

tic
netRed = train(netRed, XtrainRed', Ytrain_bingrps','UseParallel','always');
toc

outputs_train_red = netRed(XtrainRed');
[~, Y_NN_train_red] = max(outputs_train_red,[],1);
Y_NN_train_red = categorical(Y_NN_train_red,1:length(actnames),actnames)';

outputs_test_red = netRed(XtestRed');
[~, Y_NN_test_red] = max(outputs_test_red,[],1);
Y_NN_test_red = categorical(Y_NN_test_red,1:length(actnames),actnames)';

C_NN_red = confusionmat(Ytest,Y_NN_test_red);
C_NN_red_train = confusionmat(Ytrain,Y_NN_train_red);
Elapsed time is 11.243019 seconds.

Compare Models

The ultimate prediction performance can be represented visually in a number of different ways. Below we present the confusion matrix. The confusion matrix is a square matrix that summarizes the cumulative prediction results for all couplings between actual and predicted classes, respectively & the overall misclassification rates on both test & training sets

% Overall misclassification rates
perfTest(1) = 100 - sum(sum(diag(C_CT)))/sum(sum(C_CT))*100;
perfTest(2) = 100 - sum(sum(diag(C_SVM)))/sum(sum(C_SVM))*100;
perfTest(3) = 100 - sum(sum(diag(C_KNN)))/sum(sum(C_KNN))*100;
perfTest(4) = 100 - sum(sum(diag(C_NB)))/sum(sum(C_NB))*100;
perfTest(5) = 100 - sum(sum(diag(C_NN)))/sum(sum(C_NN))*100;
perfTest(6) = 100 - sum(sum(diag(C_NN_red)))/sum(sum(C_NN_red))*100;

perfTrain(1) = 100 - sum(sum(diag(C_CT_train)))/sum(sum(C_CT_train))*100;
perfTrain(2) = 100 - sum(sum(diag(C_SVM_train)))/sum(sum(C_SVM_train))*100;
perfTrain(3) = 100 - sum(sum(diag(C_KNN_train)))/sum(sum(C_KNN_train))*100;
perfTrain(4) = 100 - sum(sum(diag(C_NB_train)))/sum(sum(C_NB_train))*100;
perfTrain(5) = 100 - sum(sum(diag(C_NN_train)))/sum(sum(C_NN_train))*100;
perfTrain(6) = 100 - sum(sum(diag(C_NN_red_train)))/sum(sum(C_NN_red_train))*100;

figure
bar([perfTrain;perfTest]'); set(gca,'XTickLabel', {'Trees', 'SVM', 'kNN', 'Naive Bayes','NN','Reduced NN'});
legend('Training','Test')
ylabel('Misclassification Rates(%)'); title('Comparison of models on training & test set');

% Confusion Matrices on Test sets
figure
subplot(2,3,1);
heatmap(C_CT*100./repmat(sum(C_CT,2),1,size(C_CT,2)),actnames,actnames,'%10.1f%%', 'TickAngle',60,'TickFontSize',10,'colormap','lines','ShowAllTicks',true);
title(['\bf Classification Tree (' num2str(perfTest(1)) '%)']);
ylabel('Predicted Classes');
subplot(2,3,2);
heatmap(C_SVM*100./repmat(sum(C_SVM,2),1,size(C_SVM,2)),actnames,actnames,'%10.1f%%', 'TickAngle',60,'TickFontSize',10,'colormap','lines','ShowAllTicks',true);
title(['\bf Multiclass SVM (' num2str(perfTest(2)) '%)']);
subplot(2,3,3);
heatmap(C_NN*100./repmat(sum(C_NN,2),1,size(C_NN,2)),actnames,actnames,'%10.1f%%', 'TickAngle',60,'TickFontSize',10,'colormap','lines','ShowAllTicks',true);
title(['\bf Neural Networks (' num2str(perfTest(5)) '%)']);
subplot(2,3,4);
heatmap(C_NB*100./repmat(sum(C_NB,2),1,size(C_NB,2)),actnames,actnames,'%10.1f%%', 'TickAngle',60,'TickFontSize',10,'colormap','lines','ShowAllTicks',true);
title(['\bf Naive Bayes (' num2str(perfTest(4)) '%)']);
ylabel('Predicted Classes');
subplot(2,3,5);
heatmap(C_KNN*100./repmat(sum(C_KNN,2),1,size(C_KNN,2)),actnames,actnames,'%10.1f%%', 'TickAngle',60,'TickFontSize',10,'colormap','lines','ShowAllTicks',true);
title(['\bf K-Nearest Neighbour (' num2str(perfTest(3)) '%)']);
subplot(2,3,6);
heatmap(C_NN_red*100./repmat(sum(C_NN_red,2),1,size(C_NB,2)),actnames,actnames,'%10.1f%%', 'TickAngle',60,'TickFontSize',10,'colormap','lines','ShowAllTicks',true);
title(['\bf Reduced Neural Networks (' num2str(perfTest(6)) '%)']);

Save models

save Data\Prepared_iPhone_32\TrainedNetwork net netRed mySVM myTree myKNN myNB