| title | output |
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CodeBook |
github_document |
DATA DICTIONARY - tidy_data.txt
The features selected for this data frame came from the transformation of the data in the "Human Activity Recognition Using Smartphones Dataset Version 1.0", which in turn have been taken from experiments carried out with a group of 30 volunteers within an age bracket of 19-48 years.
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
angle(): Angle between two 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
mean() : mean values of multiple measurements of the original variables. Type: Real number
std(): Standard deviation of multiple measurements of the original variables. Type: Real number
activity_id: Identifier, identifying the activity of each subject. Type: Integer Values: 1 : 6
activity_name: Descriptive name of each subject's activity. Type: Factor Values: WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING
subject_id : Identifier, identifying each subject. Type: Integer Values: 1 : 30