Datasets:

kushsaraf
/

robot-intelligence-dataset

@@ -1,82 +1,3 @@
-===================================================================================================
-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.

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-1 WALKING
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-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)

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perception_dataset/features_info.txt CHANGED Viewed

@@ -1,59 +1,3 @@
-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'

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perception_dataset/test/Inertial Signals/body_acc_x_test.txt CHANGED Viewed