Localization from Activity Sensor Data

This is the code for the SenSys '20 poster "Localization from activity sensor data".

Setup

A Conda *.yml file is provided for recreating the development environment. Just run conda env create -f environment.yml to install all necessary packages.

How to Run

train_models.py trains the model using auto-sklearn, it is set to take 3 hours localize.py runs the localization and puts the results into a *.csv file results.ipynb visualizes the results and stores the plots, we used in the paper

To run, activate the Conda environment, place the data into the data folder and type python train_models.py && python localize.py.

Data

We use proprietary cow data, which we can unfortunately not share. We use MERRA2 data, the *.csv files for each position need the corresponding postal code as prefix, e.g. 1210_SoDa_MERRA2_lat48.283_lon16.412_2019-01-01_2019-12-31_2029583187.csv.

Citing

If you find our work useful, please cite it using the following BibTex entry:

@inproceedings{papst2020,
  title = {Localization from activity sensor data},
  author = {Papst, Franz and Stricker, Naomi and Saukh, Olga},
  booktitle = {Proceedings of the 18th Conference on Embedded Networked Sensor Systems},
  pages = {703--704},
  year = {2020}
}