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

Mohammud J Bocus; Wenda Li; Shelly Vishwakarma; Roget Kou; Chong Tang; Karl Woodbridge; Ian Craddock; Ryan McConville; Raul Santos-Rodriguez; Kevin Chetty; Robert Piechocki

doi:10.1038/s41597-022-01573-2

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

Sci Data. 2022 Aug 3;9(1):474. doi: 10.1038/s41597-022-01573-2.

Authors

Affiliations

¹ School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths, University of Bristol, Bristol, BS8 1UB, UK. junaid.bocus@bristol.ac.uk.
² Department of Security and Crime Science, University College London, London, WC1H 9EZ, UK. wenda.li@ucl.ac.uk.
³ Department of Security and Crime Science, University College London, London, WC1H 9EZ, UK. s.vishwakarma@ucl.ac.uk.
⁴ School of Computer Science, Electrical and Electronic Engineering, and Engineering Maths, University of Bristol, Bristol, BS8 1UB, UK.
⁵ Department of Security and Crime Science, University College London, London, WC1H 9EZ, UK.

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.

Abstract

Grants and funding