================================================================== Human Activity Recognition Using Smartphones Dataset
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.
'README.txt'
'features_info.txt': Shows information about the variables used on the feature vector.
'features.txt': List of all features. (A 561-feature vector with time and frequency domain variables.)
'activity_labels.txt': Links the class labels with their activity name.
'train/X_train.txt': Training set.
'train/y_train.txt': Training labels.
'test/X_test.txt': Test set.
'test/y_test.txt': Test labels.
Average/mean in standard units of each variable for each activity and each subject.
'data.frame': 180 obs. of 68 variables:
$ ACTDESC : chr "LAYING" "LAYING" "LAYING" "LAYING" ...
$ SUBJID : int 1 10 11 12 13 14 15 16 17 18 ...
$ timeBodyAccelerometerMeanXaxis : num 0.222 0.28 0.281 0.26 0.277 ...
$ timeBodyAccelerometerMeanYaxis : num -0.0405 -0.0243 -0.0177 -0.0175 -0.0204 ...
$ timeBodyAccelerometerMeanZaxis : num -0.113 -0.117 -0.109 -0.108 -0.104 ...
$ timeGravityAccelerometerMeanXaxis : num -0.249 -0.453 -0.135 -0.379 -0.157 ...
$ timeGravityAccelerometerMeanYaxis : num 0.706 -0.139 0.943 0.803 0.656 ...
$ timeGravityAccelerometerMeanZaxis : num 0.4458 -0.0311 0.1126 0.275 0.5989 ...
$ timeBodyAccelerometerJerkMeanXaxis : num 0.0811 0.0738 0.0767 0.0854 0.0768 ...
$ timeBodyAccelerometerJerkMeanYaxis : num 0.00384 0.0157 0.01222 0.00774 0.01834 ...
$ timeBodyAccelerometerJerkMeanZaxis : num 0.01083 0.00717 0.00278 -0.00437 -0.00988 ...
$ timeBodyGyroscopeMeanXaxis : num -0.01655 -0.01956 -0.01917 -0.01465 -0.00974 ...
$ timeBodyGyroscopeMeanYaxis : num -0.0645 -0.077 -0.0416 -0.0836 -0.0966 ...
$ timeBodyGyroscopeMeanZaxis : num 0.149 0.105 0.152 0.145 0.118 ...
$ timeBodyGyroscopeJerkMeanXaxis : num -0.107 -0.1 -0.102 -0.099 -0.102 ...
$ timeBodyGyroscopeJerkMeanYaxis : num -0.0415 -0.0389 -0.0412 -0.0411 -0.0418 ...
$ timeBodyGyroscopeJerkMeanZaxis : num -0.0741 -0.0591 -0.0667 -0.0679 -0.0649 ...
$ timeBodyAccelerometerMagnitudeMean : num -0.842 -0.957 -0.981 -0.948 -0.961 ...
$ timeGravityAccelerometerMagnitudeMean : num -0.842 -0.957 -0.981 -0.948 -0.961 ...
$ timeBodyAccelerometerJerkMagnitudeMean : num -0.954 -0.976 -0.983 -0.97 -0.985 ...
$ timeBodyGyroscopeMagnitudeMean : num -0.875 -0.938 -0.953 -0.931 -0.944 ...
$ timeBodyGyroscopeJerkMagnitudeMean : num -0.963 -0.971 -0.991 -0.971 -0.985 ...
$ frequencyBodyAccelerometerMeanXaxis : num -0.939 -0.969 -0.984 -0.956 -0.975 ...
$ frequencyBodyAccelerometerMeanYaxis : num -0.867 -0.954 -0.971 -0.951 -0.966 ...
$ frequencyBodyAccelerometerMeanZaxis : num -0.883 -0.964 -0.974 -0.955 -0.966 ...
$ frequencyBodyAccelerometerJerkMeanXaxis : num -0.957 -0.979 -0.985 -0.969 -0.985 ...
$ frequencyBodyAccelerometerJerkMeanYaxis : num -0.922 -0.968 -0.974 -0.963 -0.98 ...
$ frequencyBodyAccelerometerJerkMeanZaxis : num -0.948 -0.973 -0.98 -0.967 -0.981 ...
$ frequencyBodyGyroscopeMeanXaxis : num -0.85 -0.954 -0.976 -0.957 -0.969 ...
$ frequencyBodyGyroscopeMeanYaxis : num -0.952 -0.955 -0.983 -0.953 -0.969 ...
$ frequencyBodyGyroscopeMeanZaxis : num -0.909 -0.97 -0.961 -0.946 -0.969 ...
$ frequencyBodyAccelerometerMagnitudeMean : num -0.862 -0.951 -0.974 -0.944 -0.96 ...
$ frequencyBodyAccelerometerJerkMagnitudeMean: num -0.933 -0.969 -0.977 -0.962 -0.981 ...
$ frequencyBodyGyroscopeMagnitudeMean : num -0.862 -0.938 -0.967 -0.945 -0.958 ...
$ frequencyBodyGyroscopeJerkMagnitudeMean : num -0.942 -0.961 -0.986 -0.964 -0.978 ...
$ timeBodyAccelerometerStdXaxis : num -0.928 -0.968 -0.985 -0.955 -0.969 ...
$ timeBodyAccelerometerStdYaxis : num -0.837 -0.946 -0.972 -0.949 -0.951 ...
$ timeBodyAccelerometerStdZaxis : num -0.826 -0.959 -0.971 -0.948 -0.95 ...
$ timeGravityAccelerometerStdXaxis : num -0.897 -0.955 -0.98 -0.936 -0.958 ...
$ timeGravityAccelerometerStdYaxis : num -0.908 -0.967 -0.991 -0.974 -0.976 ...
$ timeGravityAccelerometerStdZaxis : num -0.852 -0.963 -0.984 -0.96 -0.96 ...
$ timeBodyAccelerometerJerkStdXaxis : num -0.958 -0.978 -0.985 -0.969 -0.985 ...
$ timeBodyAccelerometerJerkStdYaxis : num -0.924 -0.967 -0.973 -0.963 -0.98 ...
$ timeBodyAccelerometerJerkStdZaxis : num -0.955 -0.976 -0.982 -0.971 -0.983 ...
$ timeBodyGyroscopeStdXaxis : num -0.874 -0.962 -0.981 -0.966 -0.972 ...
$ timeBodyGyroscopeStdYaxis : num -0.951 -0.954 -0.982 -0.954 -0.963 ...
$ timeBodyGyroscopeStdZaxis : num -0.908 -0.972 -0.96 -0.95 -0.967 ...
$ timeBodyGyroscopeJerkStdXaxis : num -0.919 -0.966 -0.982 -0.967 -0.981 ...
$ timeBodyGyroscopeJerkStdYaxis : num -0.968 -0.967 -0.991 -0.966 -0.979 ...
$ timeBodyGyroscopeJerkStdZaxis : num -0.958 -0.984 -0.987 -0.97 -0.99 ...
$ timeBodyAccelerometerMagnitudeStd : num -0.795 -0.94 -0.973 -0.937 -0.948 ...
$ timeGravityAccelerometerMagnitudeStd : num -0.795 -0.94 -0.973 -0.937 -0.948 ...
$ timeBodyAccelerometerJerkMagnitudeStd : num -0.928 -0.968 -0.977 -0.963 -0.98 ...
$ timeBodyGyroscopeMagnitudeStd : num -0.819 -0.927 -0.955 -0.936 -0.945 ...
$ timeBodyGyroscopeJerkMagnitudeStd : num -0.936 -0.96 -0.984 -0.962 -0.975 ...
$ frequencyBodyAccelerometerStdXaxis : num -0.924 -0.968 -0.985 -0.955 -0.967 ...
$ frequencyBodyAccelerometerStdYaxis : num -0.834 -0.946 -0.974 -0.951 -0.947 ...
$ frequencyBodyAccelerometerStdZaxis : num -0.813 -0.96 -0.972 -0.948 -0.946 ...
$ frequencyBodyAccelerometerJerkStdXaxis : num -0.964 -0.979 -0.987 -0.973 -0.987 ...
$ frequencyBodyAccelerometerJerkStdYaxis : num -0.932 -0.968 -0.973 -0.965 -0.981 ...
$ frequencyBodyAccelerometerJerkStdZaxis : num -0.961 -0.979 -0.983 -0.973 -0.984 ...
$ frequencyBodyGyroscopeStdXaxis : num -0.882 -0.965 -0.982 -0.969 -0.973 ...
$ frequencyBodyGyroscopeStdYaxis : num -0.951 -0.953 -0.983 -0.955 -0.96 ...
$ frequencyBodyGyroscopeStdZaxis : num -0.917 -0.975 -0.963 -0.956 -0.97 ...
$ frequencyBodyAccelerometerMagnitudeStd : num -0.798 -0.944 -0.976 -0.942 -0.949 ...
$ frequencyBodyAccelerometerJerkMagnitudeStd : num -0.922 -0.965 -0.975 -0.962 -0.978 ...
$ frequencyBodyGyroscopeMagnitudeStd : num -0.824 -0.934 -0.955 -0.94 -0.946 ...
$ frequencyBodyGyroscopeJerkMagnitudeStd : num -0.933 -0.961 -0.984 -0.962 -0.973 ...
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