Super sume pro hanging on stage 27/20/2023 ![]() scMM addresses the complexity of data by leveraging a mixture-of-experts multimodal variational autoencoder. Here, we present scMM, a novel deep generative model-based framework for the extraction of interpretable joint representations and crossmodal generation. However, currently, it is challenging to infer the joint representations and learn relationships among multiple modalities from complex multimodal single-cell data. The recent development of single-cell multiomics analysis has enabled simultaneous detection of multiple traits at the single-cell level, providing deeper insights into cellular phenotypes and functions in diverse tissues. 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. Furthermore, it can potentially be used to passively track a human in an indoor environment. 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. Approximately 8 hours of annotated measurements are provided, which are collected across two rooms from 6 participants performing 6 daily activities. It also consists of vision/Infra-red based data acquired from Kinect 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. 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.
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