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

-- 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. Citation Request: Please refer to the Machine Learning Repository's citation policy