[Grupa 3] MS1 + MS2 - Kurowski Sawicki

Projekty/Projekt2/Kacper_Kurowski_Bartosz_Sawicki/UCI HAR Dataset/README.txt

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+===================================================================================================
+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.

Projekty/Projekt2/Kacper_Kurowski_Bartosz_Sawicki/UCI HAR Dataset/activity_labels.txt

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