Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact

Repository Web            Google
View ALL Data Sets

× Check out the beta version of the new UCI Machine Learning Repository we are currently testing! Contact us if you have any issues, questions, or concerns. Click here to try out the new site.

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:  


Number of Instances:




Attribute Characteristics:


Number of Attributes:


Date Donated


Associated Tasks:


Missing Values?


Number of Web Hits:



-- 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,
The Netherlands
Donors: E. B. Owusu (owboateng '@', K. A. Davis (kadian.davis '@'
-- 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

Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML