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CodeBook.md
Lauren Fitch
Sunday, March 22, 2015
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This data comes from http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones and represents accelerometer data from Samsung smartphones.

Variables

  1. subject: the subject in the experiment
  2. activity: the activity the subject was performing when the measurement was taken: WALKING WALKING_UPSTAIRS WALKING_DOWNSTAIRS SITTING STANDING LAYING

Variables 3-81 represent the mean ofeach variable grouped by subject and activity. Full explanation of each variable can be found in the original documentation below.

  1. tBodyAccmeanX
  2. tBodyAccmeanY
  3. tBodyAccmeanZ
  4. tBodyAccstdX
  5. tBodyAccstdY
  6. tBodyAccstdZ
  7. tGravityAccmeanX
  8. tGravityAccmeanY
  9. tGravityAccmeanZ
  10. tGravityAccstdX
  11. tGravityAccstdY
  12. tGravityAccstdZ
  13. tBodyAccJerkmeanX
  14. tBodyAccJerkmeanY
  15. tBodyAccJerkmeanZ
  16. tBodyAccJerkstdX
  17. tBodyAccJerkstdY
  18. tBodyAccJerkstdZ
  19. tBodyGyromeanX
  20. tBodyGyromeanY
  21. tBodyGyromeanZ
  22. tBodyGyrostdX
  23. tBodyGyrostdY
  24. tBodyGyrostdZ
  25. tBodyGyroJerkmeanX
  26. tBodyGyroJerkmeanY
  27. tBodyGyroJerkmeanZ
  28. tBodyGyroJerkstdX
  29. tBodyGyroJerkstdY
  30. tBodyGyroJerkstdZ
  31. tBodyAccMagmean
  32. tBodyAccMagstd
  33. tGravityAccMagmean
  34. tGravityAccMagstd
  35. tBodyAccJerkMagmean
  36. tBodyAccJerkMagstd
  37. tBodyGyroMagmean
  38. tBodyGyroMagstd
  39. tBodyGyroJerkMagmean
  40. tBodyGyroJerkMagstd
  41. fBodyAccmeanX
  42. fBodyAccmeanY
  43. fBodyAccmeanZ
  44. fBodyAccstdX
  45. fBodyAccstdY
  46. fBodyAccstdZ
  47. fBodyAccmeanFreqX
  48. fBodyAccmeanFreqY
  49. fBodyAccmeanFreqZ
  50. fBodyAccJerkmeanX
  51. fBodyAccJerkmeanY
  52. fBodyAccJerkmeanZ
  53. fBodyAccJerkstdX
  54. fBodyAccJerkstdY
  55. fBodyAccJerkstdZ
  56. fBodyAccJerkmeanFreqX
  57. fBodyAccJerkmeanFreqY
  58. fBodyAccJerkmeanFreqZ
  59. fBodyGyromeanX
  60. fBodyGyromeanY
  61. fBodyGyromeanZ
  62. fBodyGyrostdX
  63. fBodyGyrostdY
  64. fBodyGyrostdZ
  65. fBodyGyromeanFreqX
  66. fBodyGyromeanFreqY
  67. fBodyGyromeanFreqZ
  68. fBodyAccMagmean
  69. fBodyAccMagstd
  70. fBodyAccMagmeanFreq
  71. fBodyBodyAccJerkMagmean
  72. fBodyBodyAccJerkMagstd
  73. fBodyBodyAccJerkMagmeanFreq
  74. fBodyBodyGyroMagmean
  75. fBodyBodyGyroMagstd
  76. fBodyBodyGyroMagmeanFreq
  77. fBodyBodyGyroJerkMagmean
  78. fBodyBodyGyroJerkMagstd
  79. fBodyBodyGyroJerkMagmeanFreq

The following is a reproduction of the original README file submitted with the data:

================================================================== Human Activity Recognition Using Smartphones Dataset Version 1.0

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, 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.

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.
  • Its activity label.
  • An identifier of the subject who carried out the experiment.

The dataset includes the following files:

  • 'README.txt'

  • 'features_info.txt': Shows information about the variables used on the feature vector.

  • 'features.txt': List of all features.

  • 'activity_labels.txt': Links the class labels with their activity name.

  • 'train/X_train.txt': Training set.

  • 'train/y_train.txt': Training labels.

  • 'test/X_test.txt': Test set.

  • 'test/y_test.txt': Test labels.

The following files are available for the train and test data. Their descriptions are equivalent.

  • 'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.

  • 'train/Inertial Signals/total_acc_x_train.txt': The acceleration signal from the smartphone accelerometer X axis in standard gravity units 'g'. Every row shows a 128 element vector. The same description applies for the 'total_acc_x_train.txt' and 'total_acc_z_train.txt' files for the Y and Z axis.

  • 'train/Inertial Signals/body_acc_x_train.txt': The body acceleration signal obtained by subtracting the gravity from the total acceleration.

  • 'train/Inertial Signals/body_gyro_x_train.txt': The angular velocity vector measured by the gyroscope for each window sample. The units are radians/second.

Notes:

  • Features are normalized and bounded within [-1,1].
  • Each feature vector is a row on the text file.

For more information about this dataset contact: activityrecognition@smartlab.ws

License:

Use of this dataset in publications must be acknowledged by referencing the following publication [1]

[1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012

This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited.

Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita. November 2012.