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Human Activity Recognition Using Smartphones Dataset
Version 1.0
Jorge L. Reyes-Ortiz, Davide Anguita, Alessandro Ghio, Luca Oneto.
Smartlab - Non Linear Complex Systems Laboratory
DITEN - Università degli Studi di Genova.
Via Opera Pia 11A, I-16145, Genoa, Italy.
[email protected]
www.smartlab.ws \

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. See 'features_info.txt' for more details.

For each record 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.\

README.md: explanation in how all of the scripts work and how they are connected. \

CodeBook.md: code book that describes the variables, the data, and any transformations or work that you performed to clean up the data.\

run_analysis.R: R script that cleans, merges, labels and tidy the original data sets.\

tidy_data.txt: data set generated by using run_analysis.R, having averages of each variable for each activity and each subject.\