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OPERAnet, a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors

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. Green open access

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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.ucl.ac.uk/id/eprint/10152391
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