US-TransportationMode

US-Transporation is the name of our dataset that contains sensor data from over 13 users. In light of the lack in the literature of a common benchmark for TMD, we have collected a large set of measurements belonging to different subjects and through a simple Android Application. We openly release the dataset, so that other researchers can benefit from it for further improvements and research reproducibility. Our dataset is built from people of different gender, age and occupation. Moreover, we do not impose any restriction on the use of the application, hence every user records the data performing the action as she/he is used to, in order to assess real world conditions. In this page in addition to downloadable datasets, you can find Python's code for extracting features,and building machine learning models to make predictions. You can find more information about the dataset and our work at: http://cs.unibo.it/projects/us-tm2017/index.html.

Please cite the paper below in your publications if it helps your research:

@article{carpineti18,
  Author = {Claudia Carpineti, Vincenzo Lomonaco, Luca Bedogni, Marco Di Felice, Luciano Bononi},
  Journal = {Proc. of the 14th Workshop on Context and Activity Modeling and Recognition (IEEE COMOREA 2018)},
  Title = {Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity},
  Year = {2018}
  DOI = {https://doi.org/10.1109/PERCOMW.2018.8480119}
}

On-line version available here: https://ieeexplore.ieee.org/abstract/document/8480119

Menù

Dependecies
Documentation
- Code
- Get started
- Project Structure
License
Team of collaborators
FAQ

Dependecies

In order to extecute the code in the repository you'll need to install the following dependencies:

Documentation

Code

In this section we show the functionalities developed in our work and the relative parameters used.

TMDataset.py

TMDetection.py

Get started

Before starting, you must first download the data:

python download_dataset.py

Then you have to clean the raw data and extract the feature:

python TMDataset.py

Finally you can build models:

python TMDetection.py

For further and detail information about our code, see our tutorial section

Project Structure

Up to now the projects is structured as follows:

.
├── TransportationData
|   ├── datasetBalanced
|         └── ...
|   └── _RawDataOriginal
|         └── ...
├── README.md
├── LICENSE
├── const.py
├── function.py
├── TMDataset.py
├── TMDetection.py
├── util.py
├── sintetic_dataset_generator.py
├── sintetic_dataset_config.json
├── download_dataset.py
└── cleanLog.log

License

This work is licensed under a MIT License.

Team of collaborators

This project has been developed at the University of Bologna with the effort of different people:

Marco Di Felice, Associate Professor - email: [email protected]
Luciano Bononi, Associate Professor - email : [email protected]
Luca Bedogni, Research Associate - email: [email protected]
Vincenzo Lomonaco, PhD Student - email: [email protected]
Matteo Cappella • Master student - email: [email protected]
Simone Passaretti • Master student - email: [email protected]

Past collaborators

Claudia Carpineti • Master graduate student - email: [email protected]

FAQ

I would need to know the units of the timestamps of each sensor measurements. In your article, you mention that the sampling frequency is approximately 20Hz. However, you do not specify the units of these time stamps. Are they given in seconds or milliseconds?

The timestamps are in milliseconds!