Human Activity Recognition Using Smartphones Data Set 2


Download: Data Folder, Data Set DescriptionAbstract: 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.
Data Set Characteristics:   Multivariate, Time-Series Number of Instances: 10299 Area: Computer
Attribute Characteristics: N/A Number of Attributes: 561 Date Donated 2012-12-10
Associated Tasks: Classification, Clustering Missing Values? N/A Number of Web Hits: 33493


Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto.
Smartlab – Non Linear Complex Systems Laboratory
DITEN – Università degli Studi di Genova, Genoa I-16145, Italy.
activityrecognition ‘@’

Data Set 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.

Attribute 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.

Relevant Papers:


Citation Request:

[1] Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012

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