Human Activity Recognition Using Smartphones

Donated on 12/9/2012

Human Activity Recognition database built from the recordings of 30 subjects performing activities of daily living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors.

Dataset Characteristics

Multivariate, Time-Series

Subject Area

Computer Science

Associated Tasks

Classification, Clustering

Feature Type


# Instances


# Features


Dataset Information

Additional Information

The experiments have been carried out with a group of 30 volunteers within an age bracket of 19-48 years. Each person performed six activities (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING) wearing a smartphone (Samsung Galaxy S II) on the waist. Using its embedded accelerometer and gyroscope, we captured 3-axial linear acceleration and 3-axial angular velocity at a constant rate of 50Hz. The experiments have been video-recorded to label the data manually. The obtained dataset has been randomly partitioned into two sets, where 70% of the volunteers was selected for generating the training data and 30% the test data. The sensor signals (accelerometer and gyroscope) were pre-processed by applying noise filters and then sampled in fixed-width sliding windows of 2.56 sec and 50% overlap (128 readings/window). The sensor acceleration signal, which has gravitational and body motion components, was separated using a Butterworth low-pass filter into body acceleration and gravity. The gravitational force is assumed to have only low frequency components, therefore a filter with 0.3 Hz cutoff frequency was used. From each window, a vector of features was obtained by calculating variables from the time and frequency domain. Check the README.txt file for further details about this dataset. A video of the experiment including an example of the 6 recorded activities with one of the participants can be seen in the following link: An updated version of this dataset can be found at It includes labels of postural transitions between activities and also the full raw inertial signals instead of the ones pre-processed into windows.

Has Missing Values?


Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values

0 to 10 of 561

Additional Variable Information

For each record in the dataset it is provided: - Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration. - Triaxial Angular velocity from the gyroscope. - A 561-feature vector with time and frequency domain variables. - Its activity label. - An identifier of the subject who carried out the experiment.

Papers Citing this Dataset

Online Nonparametric Anomaly Detection based on Geometric Entropy Minimization

By Yasin Yilmaz. 2017

Published in 2017 IEEE International Symposium on Information Theory (ISIT).

K-means clustering based filter feature selection on high dimensional data

By Dewi Ismi, Shireen Panchoo, Murinto Murinto. 2016

Published in International Journal of Advances in Intelligent Informatics.

0 to 2 of 2

2 citations


Jorge Reyes-Ortiz

Davide Anguita

Alessandro Ghio

Luca Oneto

Xavier Parra


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