##Info about data and varibles gathered from the UCI website and work I've done to clean up, reshape and summarized ###Version 1.0

##The data The data contains the experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years old. 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, the team captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. 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 training data and test data were both split into three seperate file:subject_.txt(responding subject information),X_.txt(all the data) and Y_.txt(responding activity).

##For each record it is provided:

• Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration.
• Triaxial Angular velocity from the gyroscope.
• A 561-feature vector with time and frequency domain variables.
• Its activity label.
• An identifier of the subject who carried out the experiment.

##Feature Selection

The features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-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. 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.

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).

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

follwing are the shortname:t-time;body-body;acc-acceleration;mag-magnitide;XYZ-3 axis;

• tBodyAcc-XYZ (time body acceleration- X Y Z-axis)
• tGravityAcc-XYZ (time gravity acceleration- XYZ-axis)
• tBodyAccJerk-XYZ (time body accelreration jerk -XYZ-axis)
• tBodyGyro-XYZ (time body gyroscope XYZ-axis)
• tBodyGyroJerk-XYZ (... ...)
• tBodyAccMag
• tGravityAccMag
• tBodyAccJerkMag
• tBodyGyroMag
• tBodyGyroJerkMag
• fBodyAcc-XYZ
• fBodyAccJerk-XYZ
• fBodyGyro-XYZ
• fBodyAccMag
• fBodyAccJerkMag
• fBodyGyroMag
• fBodyGyroJerkMag

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.

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

• gravityMean
• tBodyAccMean
• tBodyAccJerkMean
• tBodyGyroMean
• tBodyGyroJerkMean

##Work outline to clean up the data. run_anaysis.R file contains more details about steps and code

• read all the data to R using read.table()
• match and combine the activity code file and activity file
• combine three seperate traning and test files into one horizontally using cbind()
• merge the training and test data vertically using rbind()
• select the data of interest using subseting skills, and idetify position using "find" function of text
• reshape the data and further summarize it,"reshape2" package melt and dcast function used