文章基本信息

标题：OPERAnet, a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors
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作者：Mohammud J.Bocus ; Wenda Li ; Shelly Vishwakarma 等
期刊名称：Scientific Data
电子版ISSN：2052-4463
出版年度：2022
卷号：9
期号：1
页码：1-18
DOI：10.1038/s41597-022-01573-2
语种：English
出版社：Nature Publishing Group
摘要：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 Defned Radio (SDR) platform, and Ultra-Wideband (UWB) signals acquired via commercial of-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.