-- Creators: Kadian Alicia Davis (1), Evans Boateng Owusu (2)
    1 -- Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN),
         University of Genova, Genoa - Italy
    2 -- Independent Researcher,
         Eindhoven,
         The Netherlands
     Donors: E. B. Owusu (owboateng '@' gmail.com), K. A. Davis (kadian.davis '@' gmail.com)  
  -- Date: March, 2016

Data Set Information:

This dataset is an addition to the dataset at
     [Web link]
     We collected more dataset to improve the accuracy of our HAR algorithms applied in
     a Social connectedness experiment in the domain of Ambient Assisted Living.
     The dataset was collected from the in-built accelerometer and gyroscope of a
     smartphone worn around the waist of participants. See waist_mounted_phone.PNG.
     The data was collected from 30 participants within the age group of 22-79 years.
     Each activity (standing, sitting, laying, walking, walking upstairs, walking downstairs) was
     performed for 60secs and the 3-axial linear acceleration and 3-axial angular velocity  were
     collected at a constant rate of 50Hz.

Attribute Information:

For each record in the dataset it is provided:
  - Triaxial acceleration from the accelerometer (total acceleration).
    Filenames: final_acc_train.txt, final_acc_test.txt
  - Triaxial Angular velocity from the gyroscope.
    Filenames: final_gyro_train.txt, final_gyro_test.txt
  - A 561-feature vector with time and frequency domain variables
    (extracted from the triaxial data)
    Filenames: final_X_train.txt, final_X_test.txt
    For more information about the features extracted see (features.txt and features_info.txt)
  - The corresponding activity labels. Filenames: final_y_train.txt and final_y_test.txt.

Relevant Papers:

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.

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