Human Activity Recognition Using Smartphones

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. Additional info available here :

http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

The raw data set contains 10299 instances partitioned into train and test data. It is organized into several files:

1. activity_labels.txt : mapping table for activities (ID, Label)
2. features.txt : variables names
3. subject_test.txt and subject_train.txt : identifier of the subject who carried out the experiment.
4. X_test.txt and X_train.txt : 561 variables storing measurement of triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration and triaxial Angular velocity from the gyroscope. Axis is identified in the last character of the variable name as X, Y or Z.
5. y_test.txt and y_train.txt : identifier of the activity performed.

Pre-requisites: The UCI HAR Dataset must be extracted in the repo subdirectory called "UCI_HAR_Dataset".