| title | author | date | output | ||||
|---|---|---|---|---|---|---|---|
Tidy Data Set Codebook |
Amber Sistla |
November 16, 2018 |
|
This purpose of this project was to subset Samsung data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was originally obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones Here are the data obtained for this project: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
The features selected for this tidy_data.txt database are all the mean and standard deviation features from the original data set that are then grouped by subject and activity and averaged.
##Study design and data processing
###Collection of the raw data
The raw data was collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones Here is the collected data for the project: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
####Files Included in the source data set
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'README.txt'
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'features_info.txt': Shows information about the variables used on the feature vector.
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'features.txt': List of all features.
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'activity_labels.txt': Links the class labels with their activity name.
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'train/X_train.txt': Training set.
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'train/y_train.txt': Training labels.
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'test/X_test.txt': Test set.
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'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.
####Acknowledgement of source data
[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.
##Creating the tidy datafile The units given are g's for the accelerometer and rad/sec for the gyro and g/sec and rad/sec/sec for the corresponding jerks.
###Guide to create/clean the tidy data file
- If the data does not already exist in the working directory, download the data.
- Merge the training and the test sets from to create one data set.
- Extract only the measurements on the mean and standard deviation for each measurement.
- Use descriptive activity names to name the activities in the data set
- Appropriately label the data set with descriptive variable names.
- From the data set in step 5, creates a second, independent tidy data set with the average of each variable for each activity and each subject. This is written to a file called tidyData.txt.
Return value: returns a tidy data set of class data frame To easily view the tidy data set, read the file in again and View it: td <- read.table(destFile, header=TRUE) View(td)
For more details, see the README.md file.
##Description of the variables in the tiny_data.txt file
The tidy data set containes 180 observations with 88 features each.
###Naming schema
These variables come from the accelerometer original data set gyroscope 3-axial raw signals TimeDomain_Acc_XYZDirection and TimeDomain_Gyro_XYZDirection. These time domain signals (prefix 'TimeDomain_' to denote time) were captured at a constant rate of 50 Hz. Then they were filtered using a median filter and a 3rd order low pass Butterworth filter with a corner frequency of 20 Hz to remove noise. Similarly, the acceleration signal was then separated into body and gravity acceleration signals (TimeDomain_BodyAcc_XYZ and TimeDomain_GravityAcc_XYZ) using another low pass Butterworth filter with a corner frequency of 0.3 Hz.
Subsequently, the body linear acceleration and angular velocity were derived in time to obtain Jerk signals (TimeDomain_BodyAccJerk_XYZDirection and TimeDomain_BodyGyroJerk_XYZDirection). Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (TimeDomain_BodyAccMag, TimeDomain_GravityAccMag, TimeDomain_BodyAccJerkMag, TimeDomain_BodyGyroMag, TimeDomain_BodyGyroJerkMag).
Finally a Fast Fourier Transform (FFT) was applied to some of these signals producing FrequencyDomain_BodyAcc_XYZDirection, FrequencyDomain_BodyAccJerk_XYZDirection, FrequencyDomain_BodyGyro_XYZDirection, FrequencyDomain_BodyAccJerkMag, FrequencyDomain_BodyGyroMag, FrequencyDomain_BodyGyroJerkMag. (Note the 'FrequencyDomain_' to indicate frequency domain signals).
These signals were used to estimate variables of the feature vector for each pattern:
'_XYZDirection' is used to denote 3-axial signals in the X, Y and Z directions.
The tidy data set gathered all the mean and standard deviation of variables that were estimated from these signals in the original dataset:
Additionally, the tidy data set also used the original data set vectors that were obtained by averaging the signals in a signal window sample. These are used on the angle() variable:
gravityMean TimeDomain_BodyAccMean TimeDomain_BodyAccJerkMean TimeDomain_BodyGyroMean TimeDomain_BodyGyroJerkMean
More information can be found in the source data readme.txt, features_info.txt, and the original source data homepage: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
##Full List of Variables
| VARIABLE# | VARIABLE NAME | VARIABLE CLASS | UNIT OF MEASUREMENT | NOTES |
|---|---|---|---|---|
| 1 | Subject | int | NONE | Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30 |
| 2 | Activity | Factor w/ 6 levels "LAYING" "SITTING" "STANDING" "WALKING" "WALKING_DOWNSTAIRS" "WALKING_UPSTAIRS" | NONE | Indicates the activity the subject was doing |
| 3 | TimeDomain_BodyAcc_mean_XDirection | num | G-forces (g) | |
| 4 | TimeDomain_BodyAcc_mean_YDirection | num | G-forces (g) | |
| 5 | TimeDomain_BodyAcc_mean_ZDirection | num | G-forces (g) | |
| 6 | TimeDomain_BodyAcc_std_XDirection | num | G-forces (g) | |
| 7 | TimeDomain_BodyAcc_std_YDirection | num | G-forces (g) | |
| 8 | TimeDomain_BodyAcc_std_ZDirection | num | G-forces (g) | |
| 9 | TimeDomain_GravityAcc_mean_XDirection | num | G-forces (g) | |
| 10 | TimeDomain_GravityAcc_mean_YDirection | num | G-forces (g) | |
| 11 | TimeDomain_GravityAcc_mean_ZDirection | num | G-forces (g) | |
| 12 | TimeDomain_GravityAcc_std_XDirection | num | G-forces (g) | |
| 13 | TimeDomain_GravityAcc_std_YDirection | num | G-forces (g) | |
| 14 | TimeDomain_GravityAcc_std_ZDirection | num | G-forces (g) | |
| 15 | TimeDomain_BodyAccJerk_mean_XDirection | num | G-forces (g)/second | |
| 16 | TimeDomain_BodyAccJerk_mean_YDirection | num | G-forces (g)/second | |
| 17 | TimeDomain_BodyAccJerk_mean_ZDirection | num | G-forces (g)/second | |
| 18 | TimeDomain_BodyAccJerk_std_XDirection | num | G-forces (g)/second | |
| 19 | TimeDomain_BodyAccJerk_std_YDirection | num | G-forces (g)/second | |
| 20 | TimeDomain_BodyAccJerk_std_ZDirection | num | G-forces (g)/second | |
| 21 | TimeDomain_BodyGyro_mean_XDirection | num | G-forces (g) | |
| 22 | TimeDomain_BodyGyro_mean_YDirection | num | radians/second | |
| 23 | TimeDomain_BodyGyro_mean_ZDirection | num | radians/second | |
| 24 | TimeDomain_BodyGyro_std_XDirection | num | radians/second | |
| 25 | TimeDomain_BodyGyro_std_YDirection | num | radians/second | |
| 26 | TimeDomain_BodyGyro_std_ZDirection | num | radians/second | |
| 27 | TimeDomain_BodyGyroJerk_mean_XDirection | num | radians/secondSquared | |
| 28 | TimeDomain_BodyGyroJerk_mean_YDirection | num | radians/secondSquared | |
| 29 | TimeDomain_BodyGyroJerk_mean_ZDirection | num | radians/secondSquared | |
| 30 | TimeDomain_BodyGyroJerk_std_XDirection | num | radians/secondSquared | |
| 31 | TimeDomain_BodyGyroJerk_std_YDirection | num | radians/secondSquared | |
| 32 | TimeDomain_BodyGyroJerk_std_ZDirection | num | radians/secondSquared | |
| 33 | TimeDomain_BodyAccMag_mean | num | G-forces (g) | |
| 34 | TimeDomain_BodyAccMag_std | num | G-forces (g) | |
| 35 | TimeDomain_GravityAccMag_mean | num | G-forces (g) | |
| 36 | TimeDomain_GravityAccMag_std | num | G-forces (g) | |
| 37 | TimeDomain_BodyAccJerkMag_mean | num | G-forces (g)/second | |
| 38 | TimeDomain_BodyAccJerkMag_std | num | G-forces (g)/second | |
| 39 | TimeDomain_BodyGyroMag_mean | num | radians/second | |
| 40 | TimeDomain_BodyGyroMag_std | num | radians/second | |
| 41 | TimeDomain_BodyGyroJerkMag_mean | num | radians/secondSquared | |
| 42 | TimeDomain_BodyGyroJerkMag_std | num | radians/secondSquared | |
| 43 | FrequencyDomain_BodyAcc_mean_XDirection | num | G-forces (g) | |
| 44 | FrequencyDomain_BodyAcc_mean_YDirection | num | G-forces (g) | |
| 45 | FrequencyDomain_BodyAcc_mean_ZDirection | num | G-forces (g) | |
| 46 | FrequencyDomain_BodyAcc_std_XDirection | num | G-forces (g) | |
| 47 | FrequencyDomain_BodyAcc_std_YDirection | num | G-forces (g) | |
| 48 | FrequencyDomain_BodyAcc_std_ZDirection | num | G-forces (g) | |
| 49 | FrequencyDomain_BodyAcc_meanFreq_XDirection | num | G-forces (g) | |
| 50 | FrequencyDomain_BodyAcc_meanFreq_YDirection | num | G-forces (g) | |
| 51 | FrequencyDomain_BodyAcc_meanFreq_ZDirection | num | G-forces (g) | |
| 52 | FrequencyDomain_BodyAccJerk_mean_XDirection | num | G-forces (g)/second | |
| 53 | FrequencyDomain_BodyAccJerk_mean_YDirection | num | G-forces (g)/second | |
| 54 | FrequencyDomain_BodyAccJerk_mean_ZDirection | num | G-forces (g)/second | |
| 55 | FrequencyDomain_BodyAccJerk_std_XDirection | num | G-forces (g)/second | |
| 56 | FrequencyDomain_BodyAccJerk_std_YDirection | num | G-forces (g)/second | |
| 57 | FrequencyDomain_BodyAccJerk_std_ZDirection | num | G-forces (g)/second | |
| 58 | FrequencyDomain_BodyAccJerk_meanFreq_XDirection | num | G-forces (g)/second | |
| 59 | FrequencyDomain_BodyAccJerk_meanFreq_YDirection | num | G-forces (g)/second | |
| 60 | FrequencyDomain_BodyAccJerk_meanFreq_ZDirection | num | G-forces (g)/second | |
| 61 | FrequencyDomain_BodyGyro_mean_XDirection | num | radians/second | |
| 62 | FrequencyDomain_BodyGyro_mean_YDirection | num | radians/second | |
| 63 | FrequencyDomain_BodyGyro_mean_ZDirection | num | radians/second | |
| 64 | FrequencyDomain_BodyGyro_std_XDirection | num | radians/second | |
| 65 | FrequencyDomain_BodyGyro_std_YDirection | num | radians/second | |
| 66 | FrequencyDomain_BodyGyro_std_ZDirection | num | radians/second | |
| 67 | FrequencyDomain_BodyGyro_meanFreq_XDirection | num | radians/second | |
| 68 | FrequencyDomain_BodyGyro_meanFreq_YDirection | num | radians/second | |
| 69 | FrequencyDomain_BodyGyro_meanFreq_ZDirection | num | radians/second | |
| 70 | FrequencyDomain_BodyAccMag_mean | num | radians/second | |
| 71 | FrequencyDomain_BodyAccMag_std | num | G-forces (g) | |
| 72 | FrequencyDomain_BodyAccMag_meanFreq | num | G-forces (g) | |
| 73 | FrequencyDomain_BodyBodyAccJerkMag_mean | num | G-forces (g)/second | |
| 74 | FrequencyDomain_BodyBodyAccJerkMag_std | num | G-forces (g)/second | |
| 75 | FrequencyDomain_BodyBodyAccJerkMag_meanFreq | num | G-forces (g)/second | |
| 76 | FrequencyDomain_BodyBodyGyroMag_mean | num | radians/second | |
| 77 | FrequencyDomain_BodyBodyGyroMag_std | num | radians/second | |
| 78 | FrequencyDomain_BodyBodyGyroMag_meanFreq | num | radians/second | |
| 79 | FrequencyDomain_BodyBodyGyroJerkMag_mean | num | radians/secondSquared | |
| 80 | FrequencyDomain_BodyBodyGyroJerkMag_std | num | radians/secondSquared | |
| 81 | FrequencyDomain_BodyBodyGyroJerkMag_meanFreq | num | radians/secondSquared | |
| 82 | angle_TimeDomain_BodyAccMean_gravity | num | G-forces (g) | |
| 83 | angle_TimeDomain_BodyAccJerkMean__gravityMean | num | G-forces (g)/second | |
| 84 | angle_TimeDomain_BodyGyroMean_gravityMean | num | radians/second | |
| 85 | angle_TimeDomain_BodyGyroJerkMean_gravityMean | num | radians/secondSquared | |
| 86 | angle_XDirection_gravityMean | num | G-forces (g) | |
| 87 | angle_YDirection_gravityMean | num | G-forces (g) | |
| 88 | angle_ZDirection_gravityMean | num | G-forces (g) | |
##Sources The raw data was collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained: http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones Here is the collected data set for the project: https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip ###Acknowledgement [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.