HAR

This study develops a CNN-LSTM model for Human Activity Recognition using accelerometer and gyroscope data. Achieving over 95% accuracy, it combines LSTM's temporal capabilities with CNN's spatial features.

Agenda

1. Analyzing the Data (EDA)

• Some Analysis on Data Set below:

• Here, first I perform EDA on Expert generated Data set. We try to understand the data then create some machine Learning model on top of it.

• Start with loading the feature.txt file then train data and test data and analysis these data.

• Total Data point and feature count in train and test data:

  train = pd.read_csv('UCI_HAR_dataset/csv_files/train.csv')
  test = pd.read_csv('UCI_HAR_dataset/csv_files/test.csv')
  print(train.shape, test.shape)

  Output: (7352, 564) (2947, 564)
  

• investigate participants activity durations: Since the dataset has been created in a scientific environment nearly equal preconditions for the participants can be assumed. Let us investigate their activity durations.

  sns.set_style('whitegrid')
  plt.rcParams['font.family'] = 'Dejavu Sans'
  plt.figure(figsize=(16,8))
  plt.title('Data provided by each user', fontsize=20)
  sns.countplot(x='subject',hue='ActivityName', data = train)
  plt.show()

  

  • Nearly all participants have more data for walking upstairs than downstairs. Assuming an equal number of up- and down-walks the participants need longer walking upstairs.
  • We know that we have six class classification so, we have the big problem is to know or check is there any imbalanced in the data. And after plotting above graph we can say data is balanced.
  • We have got almost same number of reading from all the subject.

• Next question is how many data points do I have per class level.

  • Data is almost balanced.
  • Although there are fluctuations in the label counts, the labels are quite equally distributed.
  • Assuming the participants had to walk the same number of stairs upwards as well as downwards and knowing the smartphones had a constant sampling rate, there should be the same amount of datapoints for walking upstairs and downstairs.
  • Disregarding the possibility of flawed data, the participants seem to walk roughly 10% faster downwards.

• Now time is to know Static and Dynamic Activities of Human:

  • In static activities (sit, stand, lie down) motion information will not be very useful.

  • In the dynamic activities (Walking, WalkingUpstairs,WalkingDownstairs) motion info will be significant.

  • Here we are using “tBodyAccMagmean” (tBody acceleration magnitude feature mean value) function to plot the graph for better understanding of Static and Dynamic Activities of Human.

  • We can see tbodyAccMagmean feature separate very well the Static and Dynamic Activities of Human.

• Now we plot the Static and Dynamic Activities of Human on Box plot for understanding:

  • If tAccMean is < -0.8 then the Activities are either Standing or Sitting or Laying.
  • If tAccMean is > -0.6 then the Activities are either Walking or WalkingDownstairs or WalkingUpstairs.
  • If tAccMean > 0.0 then the Activity is WalkingDownstairs.
  • We can classify 75% the Acitivity labels with some errors.

• Position of GravityAccelerationComponants also matters:

  

  • If angleX, gravityMean > 0 then Activity is Laying.
  • We can classify all datapoints belonging to Laying activity with just a single if else statement.

• Apply t-sne on the data to know how much the Activities are Separable? We know that we have 561-Dimension expert engineered feature now apply TSNE on these features to see how much these features are helpful.

  • We can clearly see the TSNE cluster, All the Activity are clean separate except "Standing" and "Sitting".

2. Machine Learning Models:

• Important Note as we discussed previous: I used the 561 expert engineered features and we will apply classical Machine Learning Model on top of it.
• The Machine Learning Model which I applied are:

a. Logistic Regression

• Logistic regression is a linear model for classification. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function. The logistic function is a sigmoid function, which takes any real input and outputs a value between 0 and 1, and hence is ideal for classification.

  When a model learns the training data too closely, it fails to fit new data or predict unseen observations reliably. This condition is called overfitting and is countered, in one of many ways, with ridge (L2) regularization. Ridge regularization penalizes model predictors if they are too big, thus enforcing them to be small. This reduces model variance and avoids overfitting.

• Hyperparameter Tuning: Cross-validation is a good technique to tune model parameters like regularization factor and the tolerance for stopping criteria (for determining when to stop training). Here, a validation set is held out from the training data for each run (called fold) while the model is trained on the remaining training data and then evaluated on the validation set. This is repeated for the total number of folds (say five or 10) and the parameters from the fold with the best evaluation score are used as the optimum parameters for the model.

b. Linear SVC

• The objective of a Linear SVC (Support Vector Classifier) is to fit to the data you provide, returning a "best fit" hyperplane that divides, or categorizes, your data. From there, after getting the hyperplane, you can then feed some features to your classifier to see what the "predicted" class is.

c. Kernal SVM

• SVM algorithms use a set of mathematical functions that are defined as the kernel. The function of kernel is to take data as input and transform it into the required form. Different SVM algorithms use different types of kernel functions. These functions can be different types.

d. Decision Tree

• Decision trees is a hierarchical model also known as classification and regression trees. They have the property of predicting response from data. The attributes of the decision trees are mapped into nodes. The edges of the tree represent the possible output values. Each branch of the tree represents a classification rule, from the root to the leaf node.
• This method has been used for several tasks in the field of pattern recognition and machine learning as a predictive model. The main goal is to predict the next value given several input variable.

e. Random Forest Classifier

• Random Forest is an outfit of unpruned demand or descends like bootstrapping algorithm with various decision trees. Each tree depends upon the estimations of the vector picked unpredictably and independently. Random Forest reliably gives an immense improvement than the single tree classifier. Each tree is fabricated using the algorithm.

f. Gradient Boosted

• Gradient boosting is an AI method for relapse and order issues, which creates an expectation model as a group of powerless forecast models, normally choice trees. The goal of any directed learning algorithm is to characterize a misfortune work and limit it. Gradient boosting machines are in light of a ensemble of choice trees where numerous weak learner trees are utilized in mix as a group to give preferred forecasts over singular trees. Boost has unrivalled regularization and better treatment of missing qualities and also much improved proficiency.
• NOTE: I am trying to run the "GradientBoostingClassifier()" with "GridSearchCV", but my system is not supported this pice of code.

3. Deep Learning Models:

• Now, I created LSTM based Deep learning Model on the Raw time series Data.
• HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same sequence, we can use LSTM (long short-term memory) model of the Recurrent Neural Network (RNN) to recognize various activities of humans like standing, climbing upstairs and downstairs etc.
• LSTM model is a type of recurrent neural network capable of learning order dependence in sequence prediction problems. This model is used as this helps in remembering values over arbitrary intervals.
• I applied LSTM as follows:
  • 1-Layer of LSTM
  • 2-Layer of LSTM with more hyperparameter tuning

4. Results & Conclusion

• For below table we are comparing all the ML model Accuracy score.

Model Name	Features	Hyperparameter Tuning	Accuracy Score
Logistic Regression	Expert generated Feature	Done	95.83%
Linear SVC	Expert generated Feature	Done	96.47%
RBF SVM classifier	Expert generated Feature	Done	96.27%
Decision Tree	Expert generated Feature	Done	86.46%
Random Forest	Expert generated Feature	Done	92.06%

• We can choose Linear SVC or rbf SVM classifier or Logistic Regression as our best model while applying ML Classical Model.

• For Below table we are comparing Deep Learning LSTM Model.

Model Name	Features	Hyperparameter Tuning	crossentropy	Accuracy Value
LSTM With 1_Layer(neurons:32)	Raw time series Data	Done	0.47	0.90%
LSTM With 2_Layer(neurons:48, neurons:32)	Raw time series Data	Done	0.39	0.90%
LSTM With 2_Layer(neurons:64, neurons:48)	Raw time series Data	Done	0.27	0.91%

• When we talking about LSTM Model, here with LSTM we are using simple RAW data(in ML model we are using Single engineered data made by an expert), but we can see the result without any FE data, LSTM perform very-very well and got highest 91% accuracy with 2_layer LSTM with hyperparameter Tuning and also when we are increasing LSTM layer and Hyperparameter Tuning the cross-entropy value is decreasing and Accuracy is increasing.

Technical Aspect

This project is divided into four part:

1. I have done EDA in first part.
2. Created Classical Machine Learning Prediction Models on top of expert generated features in second part.
3. Created LSTM based Deep learning Model on top of Raw time series Data in third part.
4. Machine Learning and Deep Learning Model Comparison and conclusion in fourth part.

Installation

The Code is written in Python 3.7. If you don't have Python installed you can find it here. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip.

• all the code for the article is available with this REPOSITORIES..

  How To

  • Install Required Libraries

    pip3 install pandas
    pip3 install numpy
    pip3 install scikit-learn
    pip3 install matplotlib
    pip3 install keras

Quick overview of the dataset

• Accelerometer and Gyroscope readings are taken from 30 volunteers (referred as subjects) while performing the following 6 Activities.

  1. Walking
  2. WalkingUpstairs
  3. WalkingDownstairs
  4. Standing
  5. Sitting
  6. Lying.

• Readings are divided into a window of 2.56 seconds with 50% overlapping.

• Accelerometer readings are divided into gravity acceleration and body acceleration readings, which has x,y and z components each.

• Gyroscope readings are the measure of angular velocities which has x,y and z components.

• Jerk signals are calculated for BodyAcceleration readings.

• Fourier Transforms are made on the above time readings to obtain frequency readings.

• Now, on all the base signal readings., mean, max, mad, sma, arcoefficient, engerybands,entropy etc., are calculated for each window.

• We get a feature vector of 561 features and these features are given in the dataset.

• Each window of readings is a datapoint of 561 features.