CODEBOOK.Rmd

---
title: "CODEBOOK for tidyGroupedData.csv"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source("run_analysis.R")
```


Data set used on this project, called Human Activity Recognition Using Smartphones Dataset, came from here:
http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones

Reference: Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. International Workshop of Ambient Assisted Living (IWAAL 2012). Vitoria-Gasteiz, Spain. Dec 2012

## Human Activity Recognition Using Smartphones Dataset
#### Experiment description
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. 

#### Collected 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.

## Generated tidyGroupedData.csv dataset
Human Activity Recognition Using Smartphones Dataset was processed by run_analysis.R and resulted on tidyGroupedData.csv dataset.

#### Process
- Train and Test datasets were merged
- Only feature means and standard deviations were keept for all windows, subjects and activities.
- Data were grouped by subjects and activities
- Ffeatures means and standard deviations were summirized by mean

#### codebook
- subject: integer from 1 to 30 to identify the subject of the study
- activity: factor to identify the real activity been executed while data were collected:
```{r }
activity_labels
```

##### Mesurements variable:
Mesurements variable are formed by te following parts in this order:

- 't' or 'f'.
    - 't' extends for Time domain
    - 'f' extends for Frequency domain
- feature
    - bodyacc for Body acceletarion
    - gravityacc for Gravity Acceleration
    - bodygyro for Body gyroscope
- jerk identifygng jerk transformation
- mag identifying magnitude
- mean or std for 
- x, y or z cordinates