/human_activity_recognition_with_smartphone

Primary LanguageJupyter Notebook

Human Activity Recognition

In this notebook, we are trying to predict the Activity of a user. As you can it is a Muliclassification Problem. This notebook is to build a model that can predict whether a person is Laying, Standing , Sitting, Walking, Walking_upstairs, or Walking_downstairs

Initially, the information in this dataset is the measurements from the accelerometer, gyroscope, magnetometer, and GPS of the smartphone.

Data Information

From the website:

http://archive.ics.uci.edu/ml/datasets/Smartphone-Based+Recognition+of+Human+Activities+and+Postural+Transitions

The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data.

The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain.

Let's talk about the features (columns)

We see, there are 563 individual features(columns).

  1. The features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ . These time domain signals (prefix 't' to denote time) were captured at a constant rate of 50 Hz. Then they were filtered using a median filter and a 3rd order low pass Butterworth filter with a corner frequency of 20 Hz to remove noise.

  2. Similarly, the acceleration signal was then separated into body and gravity acceleration signals (tBodyAcc-XYZ and tGravityAcc-XYZ) using another low pass Butterworth filter with a corner frequency of 0.3 Hz.

  3. Subsequently, the body linear acceleration and angular velocity were derived in time to obtain Jerk signals (tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ) . Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag)

jerk is the rate at which an object's acceleration changes with respect to time

  1. Finally a Fast Fourier Transform (FFT) was applied to some of these signals producing

fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag.

(Note the 'f' to indicate frequency domain signals).

These signals were used to estimate variables of the feature vector for each pattern:

  1. '-XYZ' is used to denote 3-axial signals in the X, Y and Z directions.

    • tBodyAcc-XYZ
    • tGravityAcc-XYZ
    • tBodyAccJerk-XYZ
    • tBodyGyro-XYZ
    • tBodyGyroJerk-XYZ
    • tBodyAccMag
    • tGravityAccMag
    • tBodyAccJerkMag
    • tBodyGyroMag
    • tBodyGyroJerkMag
    • fBodyAcc-XYZ
    • fBodyAccJerk-XYZ
    • fBodyGyro-XYZ
    • fBodyAccMag
    • fBodyAccJerkMag
    • fBodyGyroMag
    • fBodyGyroJerkMag`

  2. The set of variables that were estimated from these signals are:

    • mean(): Mean value
    • std(): Standard deviation
    • mad(): Median absolute deviation
    • max(): Largest value in array
    • min(): Smallest value in array
    • sma(): Signal magnitude area
    • energy(): Energy measure. Sum of the squares divided by the number of values.
    • iqr(): Interquartile range
    • entropy(): Signal entropy
    • arCoeff(): Autorregresion coefficients with Burg order equal to 4
    • correlation(): correlation coefficient between two signals
    • maxInds(): index of the frequency component with largest magnitude
    • meanFreq(): Weighted average of the frequency components to obtain a mean frequency
    • skewness(): skewness of the frequency domain signal
    • kurtosis(): kurtosis of the frequency domain signal
    • bandsEnergy(): Energy of a frequency interval within the 64 bins of the FFT of each window.
    • angle(): Angle between to vectors.

  3. Additional vectors obtained by averaging the signals in a signal window sample. These are used on the angle() variable:

    gravityMean tBodyAccMean tBodyAccJerkMean tBodyGyroMean tBodyGyroJerkMean

That's too much information.

What's our Plan?

Outline

  • 1. Read Dataset

  • 2. Datset Cleaning

    • 2.1 Outliers
    • 2.2 Filling null values
    • 2.3 Check for data imbalance
    • 2.4 Correcting some feature names

  • 3. Exploratory Data Analysis

  • 4. Data Preprocessing

    • 4.1 Encoding categorical variables
    • 4.2 Normalization
    • 4.3 Split Training and testing

  • 5. Models, Hyperparameter Tuning and Cross Validation

    • 5.1 Logistic Regression
    • 5.2 Naive Bayes
    • 5.3 K-Nearest Neighbor
    • 5.4 Decision Tree
    • 5.5 Random Forest
    • 5.5 Support Vector Machine