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. See 'features_info.txt' for more details.
- 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.
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'README.txt'
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'features_info.txt': Shows information about the variables used on the feature vector.
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'features.txt': List of all features.
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'activity_labels.txt': Links the class labels with their activity name.
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'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.
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'train/X_train.txt': Training set.
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'train/y_train.txt': Training labels.
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'test/subject_train.txt': Each row identifies the subject, who is identical to the equivalent subject number in subject_train.txt. However, the range is smaller (2, 4, 9, 10, 12, 13, 18, 20, 24).
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'test/X_test.txt': Test set.
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'test/y_test.txt': Test labels.
- Features are normalized and bounded within [-1,1].
- Each feature vector is a row on the text file.
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.
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
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
The complete list of variables of each feature vector is available in 'features.txt'
The labels below were obtained from activity_labels.txt
1 WALKING 2 WALKING_UPSTAIRS 3 WALKING_DOWNSTAIRS 4 SITTING 5 STANDING 6 LAYING
The purpose of this project is to prepare tidy data that can be used for later analysis. I designed this script according to project specifications and used the data.table, dplyr, and tidyr libraries to conduct this data cleaning.
Before I could begin to merge the data sets, I realized that each data set had a common set of variables (i.e. column names) that I needed to set. So, I read in those column names from the features.txt file provided. Then, I edited the variable names to appear more tidy (e.g. removing line numbers, cutting out white space, etc.). To finish this part of the process, I added a column to the start, "Subject", referring to the id numbers the subjects assigned to each volunteer. I also added an "Activity" column to the end to refer to what activity the subject was doing while measurements were recorded.
Afterwards, I made the train and test datasets in a similar manner. In both the test and train data sets, they possessed three separate text files that contained the following: subject IDs, X data, and y data. I simply loaded in each file as data tables, combined them into larger data tables, and named their columns according to the features in the first part of this section.
Lastly, I merged the datasets into one large dataset simplying by binding the rows of the train data to the rows of the test data. For the sake of future tidiness, I reordered the data by Subject and Activity.
I used the function "select" from the dplyr library to select the data. Besides the mandatory "Subject" and "Activity" columns, I also selected every column that featured the pattern "std" for standard deviation and "mean[^Freq]" for mean but not mean frequency.
I loaded in the activities featured in the activity_labels.txt file, and processed them into a list of character vectors. Using the fact that each activity corresponded to an assigned number from 1-6, I simply used the mutate function to redefine the the Activity column and used the assigned numbers as indices that corresponded to their activity label. As an example, "WALKING" was represented with a number 1, and its index within the list of labels was 1. Therefore, I simply called list[activity number] to get "WALKING".
This was done earlier in the process when I initially named each column according to their measurement names.
This portion of the process was more difficult than I initially expected, but I nonetheless got it done. In order to ensure I would properly find the means of each measurement according to their subject id and activity, I split the data by those two attributes, which generated a 180 element long list of data frames (e.g. first element of the list was Subject 1's "WALKING" measurements, second element was Subject 1's "WALKING_UPSTAIRS" data, etc.) Then, I looped through each dataframe in this list, applied the colMeans function (as well as other string/label tidying), and merged the data into one mega data table. After the work was done, I saved all of this as the second data set.