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https://github.com/vlomonaco/us-transportationmode

Transportation Mode Detection with Unconstrained Smartphones Sensors
https://github.com/vlomonaco/us-transportationmode

context-awareness internet-of-things machine-learning neural-networks random-forest

Last synced: 7 days ago
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Transportation Mode Detection with Unconstrained Smartphones Sensors

Host: GitHub
URL: https://github.com/vlomonaco/us-transportationmode
Owner: vlomonaco
License: mit
Created: 2017-06-16T16:32:07.000Z (over 7 years ago)
Default Branch: master
Last Pushed: 2021-04-20T08:20:07.000Z (over 3 years ago)
Last Synced: 2024-11-01T04:34:48.570Z (14 days ago)
Topics: context-awareness, internet-of-things, machine-learning, neural-networks, random-forest
Language: Python
Size: 33.4 MB
Stars: 40
Watchers: 9
Forks: 18
Open Issues: 1
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

# US-TransportationMode

[![license](https://img.shields.io/github/license/mashape/apistatus.svg)]()
[![built with Python2.7](https://camo.githubusercontent.com/65bf37ffbdcaef2a2cb000f66e2f395b32243357/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6275696c64253230776974682d707974686f6e322e372d7265642e737667)](https://www.python.org/)

**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](https://github.com/vlomonaco/US-TransportationMode/blob/master/README.md#dependecies)**
* **[Documentation](https://github.com/vlomonaco/US-TransportationMode/blob/master/README.md#documentation)**
* **[Code](https://github.com/vlomonaco/US-TransportationMode/blob/master/README.md#code)**
* **[Get started](https://github.com/vlomonaco/US-TransportationMode/blob/master/README.md#get-started)**
* **[Project Structure](https://github.com/vlomonaco/US-TransportationMode/blob/master/README.md#project-structure)**
* **[License](https://github.com/vlomonaco/US-TransportationMode/blob/master/README.md#license)**
* **[Team of collaborators](https://github.com/vlomonaco/US-TransportationMode/blob/master/README.md#team-of-collaborators)**
* **[FAQ](https://github.com/vlomonaco/US-TransportationMode/blob/master/README.md#faq)**

## Dependecies
In order to extecute the code in the repository you'll need to install the following dependencies:
* [Python 2.7](https://www.python.org/)
* [Scikit-learn](http://scikit-learn.org/stable/)
* [Pandas](http://pandas.pydata.org/)

## Documentation
### Code
In this section we show the functionalities developed in our work and the relative parameters used.
#### TMDataset.py

Function name
Parameter
Description

clean_file()

Fix original raw files problems:

delete measure from sensor_to_exclude

if sound or speed measure rows have negative time, use module

if **time** have incorrect values ("/", ">", "<", "-", "_"...), delete file

if file is empty, delete file

transform_raw_data()

Transform sensor raw data in orientation independent data (with magnitude metric)

__fill_data_structure

Fill tm, users, sensors data structures with the relative data from dataset

__range_position_in_header_with_features(sensor_name)
sensor_name: name of the sensor
Return position of input sensor in header with features

create_header_files()

Fill directory with all file consistent with the header without features

__create_time_files()

Fill directory with all file consistent with the featured header divided in time window

__create_dataset()

Create dataset file

__split_dataset()

Split passed dataframe into test and train

preprocessing_files()

Clean files and transform in orientation independent

analyze_sensors_support()

For each sensors analyze user support, put support result in sensor_support.csv [sensor,nr_user,list_users,list_classes]

create_balanced_dataset(sintetic)
sintetic: set if data are sintentic or not. Default the value is False.
Analyze dataset composition in term of class and user contribution fill balance_time with minimum number of window for transportation mode

get_excluded_sensors(sensor_set)
sensor_set: type of sensor dataset used with different sensor data.
Return list of excluded sensor based on the correspondent classification level

get_remained_sensors(sensor_set)
sensor_set: type of sensor dataset used with different sensor data.
Return list of considered sensors based on the correspondent classification level

get_sensors_set_features()

Return list of the sensors set with their features

get_sensor_features(sensor)
sensor: data of a specific sensor
Return the features of a specific sensor

#### TMDetection.py

Function name
Parameter
Description

decision_tree(sensors_set)
sensor_set: type of sensor dataset used with different sensor data
Decision tree algorithm training on training al train set and test on all test set

random_forest(sensors_set)
sensor_set: type of sensor dataset used with different sensor data
Random forest algorithm training on training al train set and test on all test set

neural_network(sensors_set)
sensor_set: type of sensor dataset used with different sensor data
Neural network algorithm training on training al train set and test on all test set

support_vector_machine(sensors_set)
sensor_set: type of sensor dataset used with different sensor data
Support vector machine algorithm training on training al train set and test on all test set

classes_combination(sensors_set)
sensor_set: type of sensor dataset used with different sensor data
Use different algorithms changing target classes, try all combination of two target classes

leave_one_subject_out(sensors_set)
sensor_set: type of sensor dataset used with different sensor data

support_vector_machine(sensors_set)
sensor_set: type of sensor dataset used with different sensor data
Use different algorithms leaving one subject out from training and testing only on this subject considering all classes in dataset and only user classes

single_sensor_accuracy()

Use feature relative to one sensor to build model and evaluate

### Get started
Before starting, you must first download the data:
```shell
python download_dataset.py
```
Then you have to clean the raw data and extract the feature:

```shell
python TMDataset.py
```
Finally you can build models:
```shell
python TMDetection.py
```
For further and detail information about our code, see our [tutorial section](http://cs.unibo.it/projects/us-tm2017/tutorial.html)

### Project Structure
Up to now the projects is structured as follows:
```unicode
.
├── 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!

> Can I assume that the units of the sensor data (accelerometer, gyroscope and magnetometer) are the standard ones (m/s^2, rad/s and uT, respectively)?

Yes.