OPERAnet, a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors.
OPERAnet, a multimodal activity recognition dataset acquired from radio frequency and vision-based sensors.
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OPERAnet是一个多模态活动识别数据集，从射频和基于视觉的传感器获取。
DOI：
10.1038/s41597-022-01573-2
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发表时间：
2022-08-03
期刊：
影响因子：
9.8
通讯作者：
Piechocki, Robert
中科院分区：
文献类型：
--
作者：
Bocus, Mohammud J.;Li, Wenda;Vishwakarma, Shelly;Kou, Roget;Tang, Chong;Woodbridge, Karl;Craddock, Ian;McConville, Ryan;Santos-Rodriguez, Raul;Chetty, Kevin;Piechocki, Robert
本文提出了一个全面的数据集，旨在评估被动的人类活动识别（HAR）和定位技术与同步射频（RF）设备和基于视觉的传感器获得的测量。该数据集由RF数据组成，包括从WiFi网络接口卡（NIC）提取的信道状态信息（CSI），基于软件定义无线电（SDR）平台构建的无源WiFi雷达（PWR），以及通过商用现成硬件获取的超宽带（UWB）信号。它还包括从Kinect传感器获取的基于视觉/视频的数据。提供了大约8小时的带注释的测量结果，这些测量结果是从进行6项日常活动的6名参与者的两个房间中收集的。该数据集可用于推进WiFi和基于视觉的HAR，例如，使用模式识别、骨架表示、深度学习算法或其他新颖方法来准确识别人类活动。此外，它可以潜在地用于被动地跟踪室内环境中的人。这些数据集是在智能家居、老年人护理和监控应用中开发新算法和方法所需的关键工具。
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.
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影响因子：
2.8
作者：
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通讯作者：
Wetherall, David
DOI：
10.1109/tim.2021.3091990
发表时间：
2021-01-01
影响因子：
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通讯作者：
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DOI：
10.1016/j.engappai.2019.103281
发表时间：
2020-01-01
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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