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.
http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip
| Variable | Description | Range of values |
|---|---|---|
| subject | ID of the subject | Between 1 and 30 |
| activity.name | Activity peformed | One of: "LAYING", "SITTING", "STANDING", "WALKING", "WALKING_DOWNSTAIRS", "WALKING_UPSTAIRS" |
| feature.name | Feature name | One of 66 features listed in the table below |
| n.obs | Number of observations | |
| mean | Mean value |
The features selected for this database come from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ. These time domain signals (prefix 't' to denote time) were captured at a constant rate of 50 Hz. Then they were filtered using a median filter and a 3rd order low pass Butterworth filter with a corner frequency of 20 Hz to remove noise. Similarly, the acceleration signal was then separated into body and gravity acceleration signals (tBodyAcc-XYZ and tGravityAcc-XYZ) using another low pass Butterworth filter with a corner frequency of 0.3 Hz.
Subsequently, the body linear acceleration and angular velocity were derived in time to obtain Jerk signals (tBodyAccJerk-XYZ and tBodyGyroJerk-XYZ). Also the magnitude of these three-dimensional signals were calculated using the Euclidean norm (tBodyAccMag, tGravityAccMag, tBodyAccJerkMag, tBodyGyroMag, tBodyGyroJerkMag).
Finally a Fast Fourier Transform (FFT) was applied to some of these signals producing fBodyAcc-XYZ, fBodyAccJerk-XYZ, fBodyGyro-XYZ, fBodyAccJerkMag, fBodyGyroMag, fBodyGyroJerkMag. (Note the 'f' to indicate frequency domain signals).
These signals were used to estimate variables of the feature vector for each pattern:
- '-mean()', '-std()' are used for the estimated mean and standard deviations,
- '-X', '-Y', '-Z' are used to denote 3-axial signals in the X, Y and Z directions.
| stem | suffixes |
|---|---|
| tBodyAcc- | -mean()-X, -mean()-Y, -mean()-Z, -std()-X, -std()-Y, -std()-Z |
| tGravityAcc- | -mean()-X, -mean()-Y, -mean()-Z, -std()-X, -std()-Y, -std()-Z |
| tBodyAccJerk- | -mean()-X, -mean()-Y, -mean()-Z, -std()-X, -std()-Y, -std()-Z |
| tBodyGyro- | -mean()-X, -mean()-Y, -mean()-Z, -std()-X, -std()-Y, -std()-Z |
| tBodyGyroJerk- | -mean()-X, -mean()-Y, -mean()-Z, -std()-X, -std()-Y, -std()-Z |
| tBodyAccMag- | -mean(), -std() |
| tGravityAccMag- | -mean(), -std() |
| tBodyAccJerkMag- | -mean(), -std() |
| tBodyGyroMag- | -mean(), -std() |
| tBodyGyroJerkMag- | -mean(), -std() |
| fBodyAcc- | -mean()-X, -mean()-Y, -mean()-Z, -std()-X, -std()-Y, -std()-Z |
| fBodyAccJerk- | -mean()-X, -mean()-Y, -mean()-Z, -std()-X, -std()-Y, -std()-Z |
| fBodyGyro- | -mean()-X, -mean()-Y, -mean()-Z, -std()-X, -std()-Y, -std()-Z |
| fBodyAccMag- | -mean(), -std() |
| fBodyAccJerkMag- | -mean(), -std() |
| fBodyGyroMag- | -mean(), -std() |
| fBodyGyroJerkMag- | -mean(), -std() |