Here are the raw data for the project and A full description is available at the site where the data was obtained The raw data must be downloaded and extracted into the working directory of R. So the main directory "UCI HAR Dataset" must placed in the working directory after that runing "run_analysis.R" script will produce the tidy data.
OS: Windows 8 Pro Version 6.2 (Build 9200) R version 3.2.2 (2015-08-14) -- "Fire Safety" Platform: x86_64-w64-mingw32/x64 (64-bit) RStudio Version 0.99.484 Packages:
- utils Version: ‘3.2.2’
- base Version: ‘3.2.2’
- reshape Version: ‘0.8.5’ N.B. Package 1 & 2 included in default R installation
Human Activity Recognition Using Smartphones Dataset (Version 1.0) By: Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto. Smartlab - Non Linear Complex Systems Laboratory DITEN - Università degli Studi di Genova. Via Opera Pia 11A, I-16145, Genoa, Italy. activityrecognition@smartlab.ws www.smartlab.ws
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, they 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. See 'features_info.txt' for more details.
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.
- Features are normalized and bounded within [-1,1]
subject_ID: An identifier of the subject who carried out the experiment. activity: (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING)
tBodyAcc-mean()-X tBodyAcc-mean()-Y tBodyAcc-mean()-Z tBodyAcc-std()-X tBodyAcc-std()-Y tBodyAcc-std()-Z tGravityAcc-mean()-X tGravityAcc-mean()-Y tGravityAcc-mean()-Z tGravityAcc-std()-X tGravityAcc-std()-Y tGravityAcc-std()-Z tBodyAccJerk-mean()-X tBodyAccJerk-mean()-Y tBodyAccJerk-mean()-Z tBodyAccJerk-std()-X tBodyAccJerk-std()-Y tBodyAccJerk-std()-Z tBodyGyro-mean()-X tBodyGyro-mean()-Y tBodyGyro-mean()-Z tBodyGyro-std()-X tBodyGyro-std()-Y tBodyGyro-std()-Z tBodyGyroJerk-mean()-X tBodyGyroJerk-mean()-Y tBodyGyroJerk-mean()-Z tBodyGyroJerk-std()-X tBodyGyroJerk-std()-Y tBodyGyroJerk-std()-Z tBodyAccMag-mean() tBodyAccMag-std() tGravityAccMag-mean() tGravityAccMag-std() tBodyAccJerkMag-mean() tBodyAccJerkMag-std() tBodyGyroMag-mean() tBodyGyroMag-std() tBodyGyroJerkMag-mean() tBodyGyroJerkMag-std() fBodyAcc-mean()-X fBodyAcc-mean()-Y fBodyAcc-mean()-Z fBodyAcc-std()-X fBodyAcc-std()-Y fBodyAcc-std()-Z fBodyAccJerk-mean()-X fBodyAccJerk-mean()-Y fBodyAccJerk-mean()-Z fBodyAccJerk-std()-X fBodyAccJerk-std()-Y fBodyAccJerk-std()-Z fBodyGyro-mean()-X fBodyGyro-mean()-Y fBodyGyro-mean()-Z fBodyGyro-std()-X fBodyGyro-std()-Y fBodyGyro-std()-Z fBodyAccMag-mean() fBodyAccMag-std() fBodyBodyAccJerkMag-mean() fBodyBodyAccJerkMag-std() fBodyBodyGyroMag-mean() fBodyBodyGyroMag-std() fBodyBodyGyroJerkMag-mean() fBodyBodyGyroJerkMag-std()
N.B. for more details see the raw data set.