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.
A video of the experiment including an example of the 6 recorded activities with one of the participants can be seen in the following link: https://www.youtube.com/watch?v=XOEN9W05_4A
https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones
We are attempting to use Naive Bayes, Logistic Regression (with Ridge and Lasso), and Neural Nets in R code and python as well to compare the performance.
Human-Activity-Recognition-with-Smartphones/Human Activity Recognitionv2.1.pdf
Python_ClassificationAlgorithms_Code.py
ClassificationAlgorithms.R
ExploratoryAnalysis_Python_code.py ExploratoryAnalysis_R.R