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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(imudata)
```
<!--[](https://travis-ci.org/SMAC-Group/imudata)-->
[](http://www.repostatus.org/#active)
[](https://www.gnu.org/licenses/gpl-3.0.en.html)
[](https://cran.r-project.org/)
[](commits/develop)
[)`-yellowgreen.svg)](/commits/master)
# `imudata` Overview <a href="https://smac-group.com/"><img src="man/figures/logo.png" align="right" style="width: 20%; height: 20%"/></a>
This package is meant to serve as a data collection tool for IMU data. This data can be used as a means to assess and test methods designed to analyse IMU error signals (i.e. long and complex autocorrelated signals). An example method used for this kind of data is implemented in the `gmwm` R package which can also model the latent models that often characterize this data.
The datasets available within the package are:
* `imu6` - A MTi-G MEMS-IMU dataset with 6 columns corresponding to the stochastic error measurements coming from the X, Y and Z axes for gyroscopes and accelerometers respectively and taken from a sensor calibrated in stationary conditions for 2 hours at 100Hz.
* `cont.imu1` - A MTi-G MEMS-IMU dataset with 1 column corresponding to the stochastic error measurements coming from the X-axis gyroscope and taken from a sensor calibrated in stationary conditions for 2 hours at 100Hz and possibly suffering from some contaminated measurements (e.g. outliers).
* `navchip` - A Navchip MEMS-IMU dataset with 6 columns, Axis: X,Y,Z - Type: Gyroscope & Accelerometer, from a stationary sensor run for 4 Hours
* `imar.gyro` - An IMAR MEMS-IMU dataset with 3 columns, Axis: X,Y,Z - Type: Accelerometer, from a stationary sensor run for 4 Hours
* `ln200.gyro` - A LN200 MEMS-IMU dataset with 3 columns, Axis: X,Y,Z - Type: Gyroscope, from a stationary sensor run for 6 Hours
* `ln200.accel` - A LN200 MEMS-IMU dataset with 3 columns, Axis: X,Y,Z - Type: Accelerometer, from a stationary sensor run for 6 Hours
* `kvh1750.acc` - Six samples collected from KVH1750 IMU at 100Hrz Axis: X,Y,Z - Type: Acce, from a stationary sensor run for 3 hours
* `kvh1750.gyro` - Six samples collected from KVH1750 IMU at 100Hrz Axis: X,Y,Z - Type: Accelerometer, from a stationary sensor run for 3 hours
* `mtig1khrz` - Six samples collected from MTI-G-710 IMU at 1000 Hz Axis: X,Y,Z - Type: Gyroscope & Accelerometer, from a stationary sensor run for 10 minutes
* `adis16405` - Six samples collected from ADIS 16405 IMU at 100Hrz Axis: X,Y,Z - Type: Gyroscope & Accelerometer, from a stationary sensor run for 3 hours
The first 6 datasets can be used as examples for the functions `auto.imu()`, `gmwm.imu()`, `wvar.imu()`, and `imu()` of the `gmwm` R package. Note that the `cont.imu1` data can be used as an illustration of the robustness properties of the robust version the Generalized Method of Wavelet Moments (GMWM). Here is a simple example:
<center>
</center>
# Install Instructions
To install the package you can use:
```{r, eval=FALSE}
# Install R devtools
install.packages("devtools")
# Install the package from github
devtools::install_github("SMAC-Group/imudata")
```
# Licensing
The license this source code is released under is the Creative Commons Attribution NonCommercial ShareAlike (CC-NC-SA). In some cases, the GPL license does apply. However, in the majority of the cases, the license in effect is the Creative Commons Attribution NonCommercial ShareAlike (CC-NC-SA) as the computational code is heavily dependent on armadilllo, which has an MIT license that enables us to recast our code to the Creative Commons Attribution NonCommercial ShareAlike (CC-NC-SA). See the LICENSE file for full text. Otherwise, please consult [TLDR Legal](https://tldrlegal.com/license/creative-commons-attribution-noncommercial-sharealike-(cc-nc-sa)) or [CC](https://creativecommons.org/licenses/by-nc-sa/4.0/#) which will provide a synopsis of the restrictions placed upon the data and code.