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Smartphone Dataset for Human Activity Recognition (HAR) in Ambient Assisted Living (AAL) Data Set
Download: Data Folder, Data Set Description

Abstract: This data is an addition to an existing dataset on UCI. We collected more data to improve the accuracy of our human activity recognition algorithms applied in the domain of Ambient Assisted Living.

Data Set Characteristics:  

Time-Series

Number of Instances:

5744

Area:

Computer

Attribute Characteristics:

Real

Number of Attributes:

561

Date Donated

2016-03-09

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

7700


Source:

-- 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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