Human Activity Recognition Using Smartphones Data Set (mean and standard deviation only)
Abstract: Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors.
Data Set Information: 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.
Attribute Information: For each record in the dataset it is provided:
- Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration(mean and standard deviation only).
- Triaxial Angular velocity from the gyroscope(mean and standard deviation only).
- A 81-feature vector with the average of each variable for each activity and each subject(180 groups totally).
- An identifier of the subject who carried out the experiment(the 1st column).
- Its activity label(the 2nd column).
The variable names are as follows:
[1] "subjectname" "activityname" "timebodyacc.mean.x"
[4] "timebodyacc.mean.y" "timebodyacc.mean.z" "timegravityacc.mean.x"
[7] "timegravityacc.mean.y" "timegravityacc.mean.z" "timebodyaccjerk.mean.x"
[10] "timebodyaccjerk.mean.y" "timebodyaccjerk.mean.z" "timebodygyro.mean.x"
[13] "timebodygyro.mean.y" "timebodygyro.mean.z" "timebodygyrojerk.mean.x"
[16] "timebodygyrojerk.mean.y" "timebodygyrojerk.mean.z" "timebodyaccmag.mean"
[19] "timegravityaccmag.mean" "timebodyaccjerkmag.mean" "timebodygyromag.mean"
[22] "timebodygyrojerkmag.mean" "freqbodyacc.mean.x" "freqbodyacc.mean.y"
[25] "freqbodyacc.mean.z" "freqbodyacc.meanfreq.x" "freqbodyacc.meanfreq.y"
[28] "freqbodyacc.meanfreq.z" "freqbodyaccjerk.mean.x" "freqbodyaccjerk.mean.y"
[31] "freqbodyaccjerk.mean.z" "freqbodyaccjerk.meanfreq.x" "freqbodyaccjerk.meanfreq.y"
[34] "freqbodyaccjerk.meanfreq.z" "freqbodygyro.mean.x" "freqbodygyro.mean.y"
[37] "freqbodygyro.mean.z" "freqbodygyro.meanfreq.x" "freqbodygyro.meanfreq.y"
[40] "freqbodygyro.meanfreq.z" "freqbodyaccmag.mean" "freqbodyaccmag.meanfreq"
[43] "freqbodybodyaccjerkmag.mean" "freqbodybodyaccjerkmag.meanfreq" "freqbodybodygyromag.mean"
[46] "freqbodybodygyromag.meanfreq" "freqbodybodygyrojerkmag.mean" "freqbodybodygyrojerkmag.meanfreq"
[49] "timebodyacc.std.x" "timebodyacc.std.y" "timebodyacc.std.z"
[52] "timegravityacc.std.x" "timegravityacc.std.y" "timegravityacc.std.z"
[55] "timebodyaccjerk.std.x" "timebodyaccjerk.std.y" "timebodyaccjerk.std.z"
[58] "timebodygyro.std.x" "timebodygyro.std.y" "timebodygyro.std.z"
[61] "timebodygyrojerk.std.x" "timebodygyrojerk.std.y" "timebodygyrojerk.std.z"
[64] "timebodyaccmag.std" "timegravityaccmag.std" "timebodyaccjerkmag.std"
[67] "timebodygyromag.std" "timebodygyrojerkmag.std" "freqbodyacc.std.x"
[70] "freqbodyacc.std.y" "freqbodyacc.std.z" "freqbodyaccjerk.std.x"
[73] "freqbodyaccjerk.std.y" "freqbodyaccjerk.std.z" "freqbodygyro.std.x"
[76] "freqbodygyro.std.y" "freqbodygyro.std.z" "freqbodyaccmag.std"
[79] "freqbodybodyaccjerkmag.std" "freqbodybodygyromag.std" "freqbodybodygyrojerkmag.std"