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环境辅助生活(AAL)数据集中用于人类活动识别(HAR)的智能手机数据集

环境辅助生活(AAL)数据集中用于人类活动识别(HAR)的智能手机数据集

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Computer Classification

-- Creators: Kadian Alicia Davis (1), Evans Boateng Owusu (2) 1 -- Department of Electrical, Electronic, Telecommunicati......

数据结构 ? 15.41M

    Data Structure ?

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    README.md

    -- 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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