=================================================================================================== 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 – UniversitÃ degli Studi di Genova, Genoa (I-16145), Italy. 2 – CETpD – Technical Research Centre for Dependency Care and Autonomous Living Universitat PolitÃ¨cnica de Catalunya (BarcelonaTech). Vilanova i la GeltrÃº (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   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: ===========================  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  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.  Jorge Luis Reyes-Ortiz, Alessandro Ghio, Xavier Parra-Llanas, Davide Anguita, Joan Cabestany, Andreu CatalÃ . 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.