This tidy data corresponds to the mean and standard deviation measurements collected from Samsung Galaxy S II smartphone accelerometers. Each variable is summarized by average for each human activity.
| Data Set Characteristics: | Value |
|---|---|
| Size of data set: | 2,553 bytes |
| Number of instances: | 114 |
| Number of variables: | 19 |
| Number of activities: | 6 |
| Missing values: | No |
The raw data was extrated from: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
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 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.
This section presents all transformations necessary to achieve the tidy data in the folowing sequence:
- The raw data set was partitioned into training and test data. Then, both data sets were merged to create one data set.
- From the merged data set, only the variables which presents exactly "-mean()" or "-std()" patterns in the end of its names were extrated.
- Then, the numbers used to represent the activities were replaced by descriptive activity names in the new data set.
- The corresponding labels for each variable of the new dataset were added. Therefore, the data set now has header.
- From the new dataset, a independent second data set was created, where the data were grouped by activity. After, the average of each variable was calculated by group.
| Variables Description | |
|---|---|
| Variable name: | "tBodyAccMag-mean()" |
| Variable type: | numeric |
| Variable name: | "tBodyAccMag-std()" |
| Variable type: | numeric |
| Variable name: | "tGravityAccMag-mean()" |
| Variable type: | numeric |
| Variable name: | "tGravityAccMag-std()" |
| Variable type: | numeric |
| Variable name: | "tBodyAccJerkMag-mean()" |
| Variable type: | numeric |
| Variable name: | "tBodyAccJerkMag-std()" |
| Variable type: | numeric |
| Variable name: | "tBodyGyroMag-mean()" |
| Variable type: | numeric |
| Variable name: | "tBodyGyroMag-std()" |
| Variable type: | numeric |
| Variable name: | "tBodyGyroJerkMag-mean()" |
| Variable type: | numeric |
| Variable name: | "tBodyGyroJerkMag-std()" |
| Variable type: | numeric |
| Variable name: | "fBodyAccMag-mean()" |
| Variable type: | numeric |
| Variable name: | "fBodyAccMag-std()" |
| Variable type: | numeric |
| Variable name: | "fBodyBodyAccJerkMag-mean()" |
| Variable type: | numeric |
| Variable name: | "fBodyBodyAccJerkMag-std()" |
| Variable type: | numeric |
| Variable name: | "fBodyBodyGyroMag-mean()" |
| Variable type: | numeric |
| Variable name: | "fBodyBodyGyroMag-std()" |
| Variable type: | numeric |
| Variable name: | "fBodyBodyGyroJerkMag-mean()" |
| Variable type: | numeric |
| Variable name: | "fBodyBodyGyroJerkMag-std()" |
| Variable type: | numeric |
| Variable name: | "activity" |
| Variable type: | character |
| Comments: | description of human activities |
| Allowable values: | WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING |