  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.
