Datasets:

udayl
/

UCI_HAR

@@ -52,3 +52,16 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text

 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+csv_files/test.csv filter=lfs diff=lfs merge=lfs -text
+csv_files/train.csv filter=lfs diff=lfs merge=lfs -text
+test/X_test.txt filter=lfs diff=lfs merge=lfs -text
+train/Inertial[[:space:]]Signals/body_acc_x_train.txt filter=lfs diff=lfs merge=lfs -text
+train/Inertial[[:space:]]Signals/body_acc_y_train.txt filter=lfs diff=lfs merge=lfs -text
+train/Inertial[[:space:]]Signals/body_acc_z_train.txt filter=lfs diff=lfs merge=lfs -text
+train/Inertial[[:space:]]Signals/body_gyro_x_train.txt filter=lfs diff=lfs merge=lfs -text
+train/Inertial[[:space:]]Signals/body_gyro_y_train.txt filter=lfs diff=lfs merge=lfs -text
+train/Inertial[[:space:]]Signals/body_gyro_z_train.txt filter=lfs diff=lfs merge=lfs -text
+train/Inertial[[:space:]]Signals/total_acc_x_train.txt filter=lfs diff=lfs merge=lfs -text
+train/Inertial[[:space:]]Signals/total_acc_y_train.txt filter=lfs diff=lfs merge=lfs -text
+train/Inertial[[:space:]]Signals/total_acc_z_train.txt filter=lfs diff=lfs merge=lfs -text
+train/X_train.txt filter=lfs diff=lfs merge=lfs -text

README.txt ADDED Viewed

	@@ -0,0 +1,82 @@

+===================================================================================================
+Human Activity Recognition Using Smartphones Dataset
+Version 1.0
+===================================================================================================
+Jorge L. Reyes-Ortiz(1,2), Davide Anguita(1), Alessandro Ghio(1), Luca Oneto(1) and Xavier Parra(2)
+1 - Smartlab - Non-Linear Complex Systems Laboratory
+DITEN - Universit�  degli Studi di Genova, Genoa (I-16145), Italy.
+2 - CETpD - Technical Research Centre for Dependency Care and Autonomous Living
+Universitat Polit�cnica de Catalunya (BarcelonaTech). Vilanova i la Geltr� (08800), Spain
+activityrecognition '@' 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.
+- The units used for the accelerations (total and body) are 'g's (gravity of earth -> 9.80665 m/seg2).
+- The gyroscope units are rad/seg.
+- A video of the experiment including an example of the 6 recorded activities with one of the participants can be seen in the following link: http://www.youtube.com/watch?v=XOEN9W05_4A
+For more information about this dataset please 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. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.
+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.
+Other Related Publications:
+===========================
+[2] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra, Jorge L. Reyes-Ortiz.  Energy Efficient Smartphone-Based Activity Recognition using Fixed-Point Arithmetic. Journal of Universal Computer Science. Special Issue in Ambient Assisted Living: Home Care.   Volume 19, Issue 9. May 2013
+[3] 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. 4th International Workshop of Ambient Assited Living, IWAAL 2012, Vitoria-Gasteiz, Spain, December 3-5, 2012. Proceedings. Lecture Notes in Computer Science 2012, pp 216-223.
+[4] Jorge Luis Reyes-Ortiz, Alessandro Ghio, Xavier Parra-Llanas, Davide Anguita, Joan Cabestany, Andreu Catal�. Human Activity and Motion Disorder Recognition: Towards Smarter Interactive Cognitive Environments. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.
+==================================================================================================
+Jorge L. Reyes-Ortiz, Alessandro Ghio, Luca Oneto, Davide Anguita and Xavier Parra. November 2013.

activity_labels.txt ADDED Viewed

	@@ -0,0 +1,6 @@

+1 WALKING
+2 WALKING_UPSTAIRS
+3 WALKING_DOWNSTAIRS
+4 SITTING
+5 STANDING
+6 LAYING

csv_files/test.csv ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:becb2ececea67d73a70fafa8d016c709d18d0645f0cacf6e1f2ff175148a0e5f
+size 19684670

csv_files/train.csv ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:c8fac75787e5bc0a2fdbd6fcc722a22bcadf50ed7f21aaadbb1602247ab296b8
+size 49052870

features.txt ADDED Viewed

	@@ -0,0 +1,561 @@

+1 tBodyAcc-mean()-X
+2 tBodyAcc-mean()-Y
+3 tBodyAcc-mean()-Z
+4 tBodyAcc-std()-X
+5 tBodyAcc-std()-Y
+6 tBodyAcc-std()-Z
+7 tBodyAcc-mad()-X
+8 tBodyAcc-mad()-Y
+9 tBodyAcc-mad()-Z
+10 tBodyAcc-max()-X
+11 tBodyAcc-max()-Y
+12 tBodyAcc-max()-Z
+13 tBodyAcc-min()-X
+14 tBodyAcc-min()-Y
+15 tBodyAcc-min()-Z
+16 tBodyAcc-sma()
+17 tBodyAcc-energy()-X
+18 tBodyAcc-energy()-Y
+19 tBodyAcc-energy()-Z
+20 tBodyAcc-iqr()-X
+21 tBodyAcc-iqr()-Y
+22 tBodyAcc-iqr()-Z
+23 tBodyAcc-entropy()-X
+24 tBodyAcc-entropy()-Y
+25 tBodyAcc-entropy()-Z
+26 tBodyAcc-arCoeff()-X,1
+27 tBodyAcc-arCoeff()-X,2
+28 tBodyAcc-arCoeff()-X,3
+29 tBodyAcc-arCoeff()-X,4
+30 tBodyAcc-arCoeff()-Y,1
+31 tBodyAcc-arCoeff()-Y,2
+32 tBodyAcc-arCoeff()-Y,3
+33 tBodyAcc-arCoeff()-Y,4
+34 tBodyAcc-arCoeff()-Z,1
+35 tBodyAcc-arCoeff()-Z,2
+36 tBodyAcc-arCoeff()-Z,3
+37 tBodyAcc-arCoeff()-Z,4
+38 tBodyAcc-correlation()-X,Y
+39 tBodyAcc-correlation()-X,Z
+40 tBodyAcc-correlation()-Y,Z
+41 tGravityAcc-mean()-X
+42 tGravityAcc-mean()-Y
+43 tGravityAcc-mean()-Z
+44 tGravityAcc-std()-X
+45 tGravityAcc-std()-Y
+46 tGravityAcc-std()-Z
+47 tGravityAcc-mad()-X
+48 tGravityAcc-mad()-Y
+49 tGravityAcc-mad()-Z
+50 tGravityAcc-max()-X
+51 tGravityAcc-max()-Y
+52 tGravityAcc-max()-Z
+53 tGravityAcc-min()-X
+54 tGravityAcc-min()-Y
+55 tGravityAcc-min()-Z
+56 tGravityAcc-sma()
+57 tGravityAcc-energy()-X
+58 tGravityAcc-energy()-Y
+59 tGravityAcc-energy()-Z
+60 tGravityAcc-iqr()-X
+61 tGravityAcc-iqr()-Y
+62 tGravityAcc-iqr()-Z
+63 tGravityAcc-entropy()-X
+64 tGravityAcc-entropy()-Y
+65 tGravityAcc-entropy()-Z
+66 tGravityAcc-arCoeff()-X,1
+67 tGravityAcc-arCoeff()-X,2
+68 tGravityAcc-arCoeff()-X,3
+69 tGravityAcc-arCoeff()-X,4
+70 tGravityAcc-arCoeff()-Y,1
+71 tGravityAcc-arCoeff()-Y,2
+72 tGravityAcc-arCoeff()-Y,3
+73 tGravityAcc-arCoeff()-Y,4
+74 tGravityAcc-arCoeff()-Z,1
+75 tGravityAcc-arCoeff()-Z,2
+76 tGravityAcc-arCoeff()-Z,3
+77 tGravityAcc-arCoeff()-Z,4
+78 tGravityAcc-correlation()-X,Y
+79 tGravityAcc-correlation()-X,Z
+80 tGravityAcc-correlation()-Y,Z
+81 tBodyAccJerk-mean()-X
+82 tBodyAccJerk-mean()-Y
+83 tBodyAccJerk-mean()-Z
+84 tBodyAccJerk-std()-X
+85 tBodyAccJerk-std()-Y
+86 tBodyAccJerk-std()-Z
+87 tBodyAccJerk-mad()-X
+88 tBodyAccJerk-mad()-Y
+89 tBodyAccJerk-mad()-Z
+90 tBodyAccJerk-max()-X
+91 tBodyAccJerk-max()-Y
+92 tBodyAccJerk-max()-Z
+93 tBodyAccJerk-min()-X
+94 tBodyAccJerk-min()-Y
+95 tBodyAccJerk-min()-Z
+96 tBodyAccJerk-sma()
+97 tBodyAccJerk-energy()-X
+98 tBodyAccJerk-energy()-Y
+99 tBodyAccJerk-energy()-Z
+100 tBodyAccJerk-iqr()-X
+101 tBodyAccJerk-iqr()-Y
+102 tBodyAccJerk-iqr()-Z
+103 tBodyAccJerk-entropy()-X
+104 tBodyAccJerk-entropy()-Y
+105 tBodyAccJerk-entropy()-Z
+106 tBodyAccJerk-arCoeff()-X,1
+107 tBodyAccJerk-arCoeff()-X,2
+108 tBodyAccJerk-arCoeff()-X,3
+109 tBodyAccJerk-arCoeff()-X,4
+110 tBodyAccJerk-arCoeff()-Y,1
+111 tBodyAccJerk-arCoeff()-Y,2
+112 tBodyAccJerk-arCoeff()-Y,3
+113 tBodyAccJerk-arCoeff()-Y,4
+114 tBodyAccJerk-arCoeff()-Z,1
+115 tBodyAccJerk-arCoeff()-Z,2
+116 tBodyAccJerk-arCoeff()-Z,3
+117 tBodyAccJerk-arCoeff()-Z,4
+118 tBodyAccJerk-correlation()-X,Y
+119 tBodyAccJerk-correlation()-X,Z
+120 tBodyAccJerk-correlation()-Y,Z
+121 tBodyGyro-mean()-X
+122 tBodyGyro-mean()-Y
+123 tBodyGyro-mean()-Z
+124 tBodyGyro-std()-X
+125 tBodyGyro-std()-Y
+126 tBodyGyro-std()-Z
+127 tBodyGyro-mad()-X
+128 tBodyGyro-mad()-Y
+129 tBodyGyro-mad()-Z
+130 tBodyGyro-max()-X
+131 tBodyGyro-max()-Y
+132 tBodyGyro-max()-Z
+133 tBodyGyro-min()-X
+134 tBodyGyro-min()-Y
+135 tBodyGyro-min()-Z
+136 tBodyGyro-sma()
+137 tBodyGyro-energy()-X
+138 tBodyGyro-energy()-Y
+139 tBodyGyro-energy()-Z
+140 tBodyGyro-iqr()-X
+141 tBodyGyro-iqr()-Y
+142 tBodyGyro-iqr()-Z
+143 tBodyGyro-entropy()-X
+144 tBodyGyro-entropy()-Y
+145 tBodyGyro-entropy()-Z
+146 tBodyGyro-arCoeff()-X,1
+147 tBodyGyro-arCoeff()-X,2
+148 tBodyGyro-arCoeff()-X,3
+149 tBodyGyro-arCoeff()-X,4
+150 tBodyGyro-arCoeff()-Y,1
+151 tBodyGyro-arCoeff()-Y,2
+152 tBodyGyro-arCoeff()-Y,3
+153 tBodyGyro-arCoeff()-Y,4
+154 tBodyGyro-arCoeff()-Z,1
+155 tBodyGyro-arCoeff()-Z,2
+156 tBodyGyro-arCoeff()-Z,3
+157 tBodyGyro-arCoeff()-Z,4
+158 tBodyGyro-correlation()-X,Y
+159 tBodyGyro-correlation()-X,Z
+160 tBodyGyro-correlation()-Y,Z
+161 tBodyGyroJerk-mean()-X
+162 tBodyGyroJerk-mean()-Y
+163 tBodyGyroJerk-mean()-Z
+164 tBodyGyroJerk-std()-X
+165 tBodyGyroJerk-std()-Y
+166 tBodyGyroJerk-std()-Z
+167 tBodyGyroJerk-mad()-X
+168 tBodyGyroJerk-mad()-Y
+169 tBodyGyroJerk-mad()-Z
+170 tBodyGyroJerk-max()-X
+171 tBodyGyroJerk-max()-Y
+172 tBodyGyroJerk-max()-Z
+173 tBodyGyroJerk-min()-X
+174 tBodyGyroJerk-min()-Y
+175 tBodyGyroJerk-min()-Z
+176 tBodyGyroJerk-sma()
+177 tBodyGyroJerk-energy()-X
+178 tBodyGyroJerk-energy()-Y
+179 tBodyGyroJerk-energy()-Z
+180 tBodyGyroJerk-iqr()-X
+181 tBodyGyroJerk-iqr()-Y
+182 tBodyGyroJerk-iqr()-Z
+183 tBodyGyroJerk-entropy()-X
+184 tBodyGyroJerk-entropy()-Y
+185 tBodyGyroJerk-entropy()-Z
+186 tBodyGyroJerk-arCoeff()-X,1
+187 tBodyGyroJerk-arCoeff()-X,2
+188 tBodyGyroJerk-arCoeff()-X,3
+189 tBodyGyroJerk-arCoeff()-X,4
+190 tBodyGyroJerk-arCoeff()-Y,1
+191 tBodyGyroJerk-arCoeff()-Y,2
+192 tBodyGyroJerk-arCoeff()-Y,3
+193 tBodyGyroJerk-arCoeff()-Y,4
+194 tBodyGyroJerk-arCoeff()-Z,1
+195 tBodyGyroJerk-arCoeff()-Z,2
+196 tBodyGyroJerk-arCoeff()-Z,3
+197 tBodyGyroJerk-arCoeff()-Z,4
+198 tBodyGyroJerk-correlation()-X,Y
+199 tBodyGyroJerk-correlation()-X,Z
+200 tBodyGyroJerk-correlation()-Y,Z
+201 tBodyAccMag-mean()
+202 tBodyAccMag-std()
+203 tBodyAccMag-mad()
+204 tBodyAccMag-max()
+205 tBodyAccMag-min()
+206 tBodyAccMag-sma()
+207 tBodyAccMag-energy()
+208 tBodyAccMag-iqr()
+209 tBodyAccMag-entropy()
+210 tBodyAccMag-arCoeff()1
+211 tBodyAccMag-arCoeff()2
+212 tBodyAccMag-arCoeff()3
+213 tBodyAccMag-arCoeff()4
+214 tGravityAccMag-mean()
+215 tGravityAccMag-std()
+216 tGravityAccMag-mad()
+217 tGravityAccMag-max()
+218 tGravityAccMag-min()
+219 tGravityAccMag-sma()
+220 tGravityAccMag-energy()
+221 tGravityAccMag-iqr()
+222 tGravityAccMag-entropy()
+223 tGravityAccMag-arCoeff()1
+224 tGravityAccMag-arCoeff()2
+225 tGravityAccMag-arCoeff()3
+226 tGravityAccMag-arCoeff()4
+227 tBodyAccJerkMag-mean()
+228 tBodyAccJerkMag-std()
+229 tBodyAccJerkMag-mad()
+230 tBodyAccJerkMag-max()
+231 tBodyAccJerkMag-min()
+232 tBodyAccJerkMag-sma()
+233 tBodyAccJerkMag-energy()
+234 tBodyAccJerkMag-iqr()
+235 tBodyAccJerkMag-entropy()
+236 tBodyAccJerkMag-arCoeff()1
+237 tBodyAccJerkMag-arCoeff()2
+238 tBodyAccJerkMag-arCoeff()3
+239 tBodyAccJerkMag-arCoeff()4
+240 tBodyGyroMag-mean()
+241 tBodyGyroMag-std()
+242 tBodyGyroMag-mad()
+243 tBodyGyroMag-max()
+244 tBodyGyroMag-min()
+245 tBodyGyroMag-sma()
+246 tBodyGyroMag-energy()
+247 tBodyGyroMag-iqr()
+248 tBodyGyroMag-entropy()
+249 tBodyGyroMag-arCoeff()1
+250 tBodyGyroMag-arCoeff()2
+251 tBodyGyroMag-arCoeff()3
+252 tBodyGyroMag-arCoeff()4
+253 tBodyGyroJerkMag-mean()
+254 tBodyGyroJerkMag-std()
+255 tBodyGyroJerkMag-mad()
+256 tBodyGyroJerkMag-max()
+257 tBodyGyroJerkMag-min()
+258 tBodyGyroJerkMag-sma()
+259 tBodyGyroJerkMag-energy()
+260 tBodyGyroJerkMag-iqr()
+261 tBodyGyroJerkMag-entropy()
+262 tBodyGyroJerkMag-arCoeff()1
+263 tBodyGyroJerkMag-arCoeff()2
+264 tBodyGyroJerkMag-arCoeff()3
+265 tBodyGyroJerkMag-arCoeff()4
+266 fBodyAcc-mean()-X
+267 fBodyAcc-mean()-Y
+268 fBodyAcc-mean()-Z
+269 fBodyAcc-std()-X
+270 fBodyAcc-std()-Y
+271 fBodyAcc-std()-Z
+272 fBodyAcc-mad()-X
+273 fBodyAcc-mad()-Y
+274 fBodyAcc-mad()-Z
+275 fBodyAcc-max()-X
+276 fBodyAcc-max()-Y
+277 fBodyAcc-max()-Z
+278 fBodyAcc-min()-X
+279 fBodyAcc-min()-Y
+280 fBodyAcc-min()-Z
+281 fBodyAcc-sma()
+282 fBodyAcc-energy()-X
+283 fBodyAcc-energy()-Y
+284 fBodyAcc-energy()-Z
+285 fBodyAcc-iqr()-X
+286 fBodyAcc-iqr()-Y
+287 fBodyAcc-iqr()-Z
+288 fBodyAcc-entropy()-X
+289 fBodyAcc-entropy()-Y
+290 fBodyAcc-entropy()-Z
+291 fBodyAcc-maxInds-X
+292 fBodyAcc-maxInds-Y
+293 fBodyAcc-maxInds-Z
+294 fBodyAcc-meanFreq()-X
+295 fBodyAcc-meanFreq()-Y
+296 fBodyAcc-meanFreq()-Z
+297 fBodyAcc-skewness()-X
+298 fBodyAcc-kurtosis()-X
+299 fBodyAcc-skewness()-Y
+300 fBodyAcc-kurtosis()-Y
+301 fBodyAcc-skewness()-Z
+302 fBodyAcc-kurtosis()-Z
+303 fBodyAcc-bandsEnergy()-1,8
+304 fBodyAcc-bandsEnergy()-9,16
+305 fBodyAcc-bandsEnergy()-17,24
+306 fBodyAcc-bandsEnergy()-25,32
+307 fBodyAcc-bandsEnergy()-33,40
+308 fBodyAcc-bandsEnergy()-41,48
+309 fBodyAcc-bandsEnergy()-49,56
+310 fBodyAcc-bandsEnergy()-57,64
+311 fBodyAcc-bandsEnergy()-1,16
+312 fBodyAcc-bandsEnergy()-17,32
+313 fBodyAcc-bandsEnergy()-33,48
+314 fBodyAcc-bandsEnergy()-49,64
+315 fBodyAcc-bandsEnergy()-1,24
+316 fBodyAcc-bandsEnergy()-25,48
+317 fBodyAcc-bandsEnergy()-1,8
+318 fBodyAcc-bandsEnergy()-9,16
+319 fBodyAcc-bandsEnergy()-17,24
+320 fBodyAcc-bandsEnergy()-25,32
+321 fBodyAcc-bandsEnergy()-33,40
+322 fBodyAcc-bandsEnergy()-41,48
+323 fBodyAcc-bandsEnergy()-49,56
+324 fBodyAcc-bandsEnergy()-57,64
+325 fBodyAcc-bandsEnergy()-1,16
+326 fBodyAcc-bandsEnergy()-17,32
+327 fBodyAcc-bandsEnergy()-33,48
+328 fBodyAcc-bandsEnergy()-49,64
+329 fBodyAcc-bandsEnergy()-1,24
+330 fBodyAcc-bandsEnergy()-25,48
+331 fBodyAcc-bandsEnergy()-1,8
+332 fBodyAcc-bandsEnergy()-9,16
+333 fBodyAcc-bandsEnergy()-17,24
+334 fBodyAcc-bandsEnergy()-25,32
+335 fBodyAcc-bandsEnergy()-33,40
+336 fBodyAcc-bandsEnergy()-41,48
+337 fBodyAcc-bandsEnergy()-49,56
+338 fBodyAcc-bandsEnergy()-57,64
+339 fBodyAcc-bandsEnergy()-1,16
+340 fBodyAcc-bandsEnergy()-17,32
+341 fBodyAcc-bandsEnergy()-33,48
+342 fBodyAcc-bandsEnergy()-49,64
+343 fBodyAcc-bandsEnergy()-1,24
+344 fBodyAcc-bandsEnergy()-25,48
+345 fBodyAccJerk-mean()-X
+346 fBodyAccJerk-mean()-Y
+347 fBodyAccJerk-mean()-Z
+348 fBodyAccJerk-std()-X
+349 fBodyAccJerk-std()-Y
+350 fBodyAccJerk-std()-Z
+351 fBodyAccJerk-mad()-X
+352 fBodyAccJerk-mad()-Y
+353 fBodyAccJerk-mad()-Z
+354 fBodyAccJerk-max()-X
+355 fBodyAccJerk-max()-Y
+356 fBodyAccJerk-max()-Z
+357 fBodyAccJerk-min()-X
+358 fBodyAccJerk-min()-Y
+359 fBodyAccJerk-min()-Z
+360 fBodyAccJerk-sma()
+361 fBodyAccJerk-energy()-X
+362 fBodyAccJerk-energy()-Y
+363 fBodyAccJerk-energy()-Z
+364 fBodyAccJerk-iqr()-X
+365 fBodyAccJerk-iqr()-Y
+366 fBodyAccJerk-iqr()-Z
+367 fBodyAccJerk-entropy()-X
+368 fBodyAccJerk-entropy()-Y
+369 fBodyAccJerk-entropy()-Z
+370 fBodyAccJerk-maxInds-X
+371 fBodyAccJerk-maxInds-Y
+372 fBodyAccJerk-maxInds-Z
+373 fBodyAccJerk-meanFreq()-X
+374 fBodyAccJerk-meanFreq()-Y
+375 fBodyAccJerk-meanFreq()-Z
+376 fBodyAccJerk-skewness()-X
+377 fBodyAccJerk-kurtosis()-X
+378 fBodyAccJerk-skewness()-Y
+379 fBodyAccJerk-kurtosis()-Y
+380 fBodyAccJerk-skewness()-Z
+381 fBodyAccJerk-kurtosis()-Z
+382 fBodyAccJerk-bandsEnergy()-1,8
+383 fBodyAccJerk-bandsEnergy()-9,16
+384 fBodyAccJerk-bandsEnergy()-17,24
+385 fBodyAccJerk-bandsEnergy()-25,32
+386 fBodyAccJerk-bandsEnergy()-33,40
+387 fBodyAccJerk-bandsEnergy()-41,48
+388 fBodyAccJerk-bandsEnergy()-49,56
+389 fBodyAccJerk-bandsEnergy()-57,64
+390 fBodyAccJerk-bandsEnergy()-1,16
+391 fBodyAccJerk-bandsEnergy()-17,32
+392 fBodyAccJerk-bandsEnergy()-33,48
+393 fBodyAccJerk-bandsEnergy()-49,64
+394 fBodyAccJerk-bandsEnergy()-1,24
+395 fBodyAccJerk-bandsEnergy()-25,48
+396 fBodyAccJerk-bandsEnergy()-1,8
+397 fBodyAccJerk-bandsEnergy()-9,16
+398 fBodyAccJerk-bandsEnergy()-17,24
+399 fBodyAccJerk-bandsEnergy()-25,32
+400 fBodyAccJerk-bandsEnergy()-33,40
+401 fBodyAccJerk-bandsEnergy()-41,48
+402 fBodyAccJerk-bandsEnergy()-49,56
+403 fBodyAccJerk-bandsEnergy()-57,64
+404 fBodyAccJerk-bandsEnergy()-1,16
+405 fBodyAccJerk-bandsEnergy()-17,32
+406 fBodyAccJerk-bandsEnergy()-33,48
+407 fBodyAccJerk-bandsEnergy()-49,64
+408 fBodyAccJerk-bandsEnergy()-1,24
+409 fBodyAccJerk-bandsEnergy()-25,48
+410 fBodyAccJerk-bandsEnergy()-1,8
+411 fBodyAccJerk-bandsEnergy()-9,16
+412 fBodyAccJerk-bandsEnergy()-17,24
+413 fBodyAccJerk-bandsEnergy()-25,32
+414 fBodyAccJerk-bandsEnergy()-33,40
+415 fBodyAccJerk-bandsEnergy()-41,48
+416 fBodyAccJerk-bandsEnergy()-49,56
+417 fBodyAccJerk-bandsEnergy()-57,64
+418 fBodyAccJerk-bandsEnergy()-1,16
+419 fBodyAccJerk-bandsEnergy()-17,32
+420 fBodyAccJerk-bandsEnergy()-33,48
+421 fBodyAccJerk-bandsEnergy()-49,64
+422 fBodyAccJerk-bandsEnergy()-1,24
+423 fBodyAccJerk-bandsEnergy()-25,48
+424 fBodyGyro-mean()-X
+425 fBodyGyro-mean()-Y
+426 fBodyGyro-mean()-Z
+427 fBodyGyro-std()-X
+428 fBodyGyro-std()-Y
+429 fBodyGyro-std()-Z
+430 fBodyGyro-mad()-X
+431 fBodyGyro-mad()-Y
+432 fBodyGyro-mad()-Z
+433 fBodyGyro-max()-X
+434 fBodyGyro-max()-Y
+435 fBodyGyro-max()-Z
+436 fBodyGyro-min()-X
+437 fBodyGyro-min()-Y
+438 fBodyGyro-min()-Z
+439 fBodyGyro-sma()
+440 fBodyGyro-energy()-X
+441 fBodyGyro-energy()-Y
+442 fBodyGyro-energy()-Z
+443 fBodyGyro-iqr()-X
+444 fBodyGyro-iqr()-Y
+445 fBodyGyro-iqr()-Z
+446 fBodyGyro-entropy()-X
+447 fBodyGyro-entropy()-Y
+448 fBodyGyro-entropy()-Z
+449 fBodyGyro-maxInds-X
+450 fBodyGyro-maxInds-Y
+451 fBodyGyro-maxInds-Z
+452 fBodyGyro-meanFreq()-X
+453 fBodyGyro-meanFreq()-Y
+454 fBodyGyro-meanFreq()-Z
+455 fBodyGyro-skewness()-X
+456 fBodyGyro-kurtosis()-X
+457 fBodyGyro-skewness()-Y
+458 fBodyGyro-kurtosis()-Y
+459 fBodyGyro-skewness()-Z
+460 fBodyGyro-kurtosis()-Z
+461 fBodyGyro-bandsEnergy()-1,8
+462 fBodyGyro-bandsEnergy()-9,16
+463 fBodyGyro-bandsEnergy()-17,24
+464 fBodyGyro-bandsEnergy()-25,32
+465 fBodyGyro-bandsEnergy()-33,40
+466 fBodyGyro-bandsEnergy()-41,48
+467 fBodyGyro-bandsEnergy()-49,56
+468 fBodyGyro-bandsEnergy()-57,64
+469 fBodyGyro-bandsEnergy()-1,16
+470 fBodyGyro-bandsEnergy()-17,32
+471 fBodyGyro-bandsEnergy()-33,48
+472 fBodyGyro-bandsEnergy()-49,64
+473 fBodyGyro-bandsEnergy()-1,24
+474 fBodyGyro-bandsEnergy()-25,48
+475 fBodyGyro-bandsEnergy()-1,8
+476 fBodyGyro-bandsEnergy()-9,16
+477 fBodyGyro-bandsEnergy()-17,24
+478 fBodyGyro-bandsEnergy()-25,32
+479 fBodyGyro-bandsEnergy()-33,40
+480 fBodyGyro-bandsEnergy()-41,48
+481 fBodyGyro-bandsEnergy()-49,56
+482 fBodyGyro-bandsEnergy()-57,64
+483 fBodyGyro-bandsEnergy()-1,16
+484 fBodyGyro-bandsEnergy()-17,32
+485 fBodyGyro-bandsEnergy()-33,48
+486 fBodyGyro-bandsEnergy()-49,64
+487 fBodyGyro-bandsEnergy()-1,24
+488 fBodyGyro-bandsEnergy()-25,48
+489 fBodyGyro-bandsEnergy()-1,8
+490 fBodyGyro-bandsEnergy()-9,16
+491 fBodyGyro-bandsEnergy()-17,24
+492 fBodyGyro-bandsEnergy()-25,32
+493 fBodyGyro-bandsEnergy()-33,40
+494 fBodyGyro-bandsEnergy()-41,48
+495 fBodyGyro-bandsEnergy()-49,56
+496 fBodyGyro-bandsEnergy()-57,64
+497 fBodyGyro-bandsEnergy()-1,16
+498 fBodyGyro-bandsEnergy()-17,32
+499 fBodyGyro-bandsEnergy()-33,48
+500 fBodyGyro-bandsEnergy()-49,64
+501 fBodyGyro-bandsEnergy()-1,24
+502 fBodyGyro-bandsEnergy()-25,48
+503 fBodyAccMag-mean()
+504 fBodyAccMag-std()
+505 fBodyAccMag-mad()
+506 fBodyAccMag-max()
+507 fBodyAccMag-min()
+508 fBodyAccMag-sma()
+509 fBodyAccMag-energy()
+510 fBodyAccMag-iqr()
+511 fBodyAccMag-entropy()
+512 fBodyAccMag-maxInds
+513 fBodyAccMag-meanFreq()
+514 fBodyAccMag-skewness()
+515 fBodyAccMag-kurtosis()
+516 fBodyBodyAccJerkMag-mean()
+517 fBodyBodyAccJerkMag-std()
+518 fBodyBodyAccJerkMag-mad()
+519 fBodyBodyAccJerkMag-max()
+520 fBodyBodyAccJerkMag-min()
+521 fBodyBodyAccJerkMag-sma()
+522 fBodyBodyAccJerkMag-energy()
+523 fBodyBodyAccJerkMag-iqr()
+524 fBodyBodyAccJerkMag-entropy()
+525 fBodyBodyAccJerkMag-maxInds
+526 fBodyBodyAccJerkMag-meanFreq()
+527 fBodyBodyAccJerkMag-skewness()
+528 fBodyBodyAccJerkMag-kurtosis()
+529 fBodyBodyGyroMag-mean()
+530 fBodyBodyGyroMag-std()
+531 fBodyBodyGyroMag-mad()
+532 fBodyBodyGyroMag-max()
+533 fBodyBodyGyroMag-min()
+534 fBodyBodyGyroMag-sma()
+535 fBodyBodyGyroMag-energy()
+536 fBodyBodyGyroMag-iqr()
+537 fBodyBodyGyroMag-entropy()
+538 fBodyBodyGyroMag-maxInds
+539 fBodyBodyGyroMag-meanFreq()
+540 fBodyBodyGyroMag-skewness()
+541 fBodyBodyGyroMag-kurtosis()
+542 fBodyBodyGyroJerkMag-mean()
+543 fBodyBodyGyroJerkMag-std()
+544 fBodyBodyGyroJerkMag-mad()
+545 fBodyBodyGyroJerkMag-max()
+546 fBodyBodyGyroJerkMag-min()
+547 fBodyBodyGyroJerkMag-sma()
+548 fBodyBodyGyroJerkMag-energy()
+549 fBodyBodyGyroJerkMag-iqr()
+550 fBodyBodyGyroJerkMag-entropy()
+551 fBodyBodyGyroJerkMag-maxInds
+552 fBodyBodyGyroJerkMag-meanFreq()
+553 fBodyBodyGyroJerkMag-skewness()
+554 fBodyBodyGyroJerkMag-kurtosis()
+555 angle(tBodyAccMean,gravity)
+556 angle(tBodyAccJerkMean),gravityMean)
+557 angle(tBodyGyroMean,gravityMean)
+558 angle(tBodyGyroJerkMean,gravityMean)
+559 angle(X,gravityMean)
+560 angle(Y,gravityMean)
+561 angle(Z,gravityMean)

features_info.txt ADDED Viewed

	@@ -0,0 +1,59 @@

+Feature Selection
+=================
+The features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ. These time domain signals (prefix 't' 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 (tBodyAcc-XYZ and tGravityAcc-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 (tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ). Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag).
+Finally a Fast Fourier Transform (FFT) was applied to some of these signals producing fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag. (Note the 'f' to indicate frequency domain signals).
+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
+The set of variables that were estimated from these signals are:
+mean(): Mean value
+std(): Standard deviation
+mad(): Median absolute deviation
+max(): Largest value in array
+min(): Smallest value in array
+sma(): Signal magnitude area
+energy(): Energy measure. Sum of the squares divided by the number of values.
+iqr(): Interquartile range
+entropy(): Signal entropy
+arCoeff(): Autorregresion coefficients with Burg order equal to 4
+correlation(): correlation coefficient between two signals
+maxInds(): index of the frequency component with largest magnitude
+meanFreq(): Weighted average of the frequency components to obtain a mean frequency
+skewness(): skewness of the frequency domain signal
+kurtosis(): kurtosis of the frequency domain signal
+bandsEnergy(): Energy of a frequency interval within the 64 bins of the FFT of each window.
+angle(): Angle between to 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
+The complete list of variables of each feature vector is available in 'features.txt'

test/Inertial Signals/body_acc_x_test.txt ADDED Viewed