L-CAS domotic sensors Dataset

Description

This dataset contains domotic sensor data recorded at Lincoln Centre for Autonomous Systems (L-CAS) at the University of Lincoln, UK. Data was continuously recorded in two mongo databases, one for 2016 ( from 1st of Jul to 12th of October) and one for 2017 (from 25 Jan).  Each database contains one collection with data from zwave sensors and an uniscan multisensor connected to the domotic network.

Contributions

This dataset provides:

    Environmental data with more than 800k data entries

    Ground truth is provided by Long-Term activity image datasets  under request.

Citation

If you are considering using this data, please reference the following:

Fernandez-Carmona M., Cosar S., Coppola C. and Bellotto N. (2017) “Entropy-based Abnormal Activity Detection Fusing RGB-D and Domotic Sensors”. IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2017). [BibTeX] [©]

Recording platform:

Recordings were made using OpenHAB 1.8.3, with the following sensors:

Sensor Item field prefix location
Fibaro Wall Plug Sensor Kitchen_Plug_ Coffee machine
Fibaro Multi Sensor Kitchen_Multi_ Kitchen
Fibaro Door Sensor Toilet_Door_ Toilet door
Everspring Door Sensor Fridge_Door_ Fridge
Philio Multi Sensor Workshop_Multi_ Workshop door
Zenhou Wall Plug Sensor Printer_Plug_ Printer
Everspring Door Sensor External_Door_ External main door
Fibaro motion Sensor Lounge_Multi_ Lounge
Aeotec Multi Sensor 6 Office1_Multi_ Office area
Philio multi sensor Entry_Multi_ Entry door
Uniscan environmental sensor Env_ Robot

Download*

2016 Mongo Database

2017 Mongo database

*Files are password protected. Please send an email to Manuel Fernandez-Carmona to request the password.

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

Copyright (c) 2016 Manuel Fernandez-Carmona, and Nicola Bellotto.

Funding

This work was funded in part by the EU Horizon 2020 project ENRICHME, H2020-ICT-2014-1, Grant agreement no.: 643691.