# OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors

arXiv: https://arxiv.org/abs/2110.04239 · HTML: https://mlsanity.com/paper/2110.04239/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2021.

## Built on by (most cited first, 13 of 13)

- [WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing](https://mlsanity.com/paper/2402.09430/) (2024)
- [Multimodal sensor fusion in the latent representation space](https://mlsanity.com/paper/2208.02183/) (2022)
- [A Survey on Wi-Fi Sensing Generalizability: Taxonomy, Techniques, Datasets, and Future Research Prospects](https://mlsanity.com/paper/2503.08008/) (2025)
- [Babel: A Scalable Pre-trained Model for Multi-Modal Sensing via Expandable Modality Alignment](https://mlsanity.com/paper/2407.17777/) (2024)
- [Self-Supervised Multimodal Fusion Transformer for Passive Activity Recognition](https://mlsanity.com/paper/2209.03765/) (2022)
- [OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern Recognition and Biometric Analysis](https://mlsanity.com/paper/2407.06137/) (2024)
- [Non-contact Multimodal Indoor Human Monitoring Systems: A Survey](https://mlsanity.com/paper/2312.07601/) (2023)
- [Towards White-Box Deep Wireless Sensing](https://mlsanity.com/paper/2507.21799/) (2025)
- [A Semi-supervised Sensing Rate Learning based CMAB Scheme to Combat COVID-19 by Trustful Data Collection in the Crowd](https://mlsanity.com/paper/2301.08563/) (2023)
- [Privacy in Multimodal Federated Human Activity Recognition](https://mlsanity.com/paper/2305.12134/) (2023)
- [Cross-Domain Multi-Person Human Activity Recognition via Near-Field Wi-Fi Sensing](https://mlsanity.com/paper/2510.17816/) (2025)
- [RF-LEGO: Modularized Signal Processing-Deep Learning Co-Design for RF Sensing via Deep Unrolling](https://mlsanity.com/paper/2604.10183/) (2026)
- [UniCSI Towards a Universal Wi-Fi CSI Encoder for Ubiquitous Human Sensing](https://mlsanity.com/paper/2610.09559/) (2026)

## Abstract (arXiv)

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.