Bocus, Bocus;
Li, Wenda;
Vishwakarma, Shelly;
Tang, Chong;
Kou, Roget;
Woodbridge, Karl;
Craddock, Ian;
... Chetty, Kevin; + view all
(2022)
OPERAnet, a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors.
Scientific data
, 9
, Article 474. 10.1038/s41597-022-01573-2.
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Abstract
This paper presents a comprehensive dataset intended to evaluate passive Human Activity Recognition (HAR) and localization techniques with measurements obtained from synchronized Radio-Frequency (RF) devices and vision-based sensors. The dataset consists of RF data including Channel State Information (CSI) extracted from a WiFi Network Interface Card (NIC), Passive WiFi Radar (PWR) built upon a Software Defined Radio (SDR) platform, and Ultra-Wideband (UWB) signals acquired via commercial off-the-shelf hardware. It also consists of vision/Infra-red based data acquired from Kinect sensors. Approximately 8 hours of annotated measurements are provided, which are collected across two rooms from 6 participants performing 6 daily activities. This dataset can be exploited to advance WiFi and vision-based HAR, for example, using pattern recognition, skeletal representation, deep learning algorithms or other novel approaches to accurately recognize human activities. Furthermore, it can potentially be used to passively track a human in an indoor environment. Such datasets are key tools required for the development of new algorithms and methods in the context of smart homes, elderly care, and surveillance applications.
| Type: | Article |
|---|---|
| Title: | OPERAnet, a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41597-022-01573-2 |
| Publisher version: | https://doi.org/10.1038/s41597-022-01573-2 |
| Language: | English |
| Additional information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10152391 |
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