Real World Engagement Attention Dataset
(RWEAD)
Last updated 2026/10/28
The Real World Engagement Attention Dataset (RWEAD) bridges a critical gap in behavioural research by moving beyond constrained laboratory settings and artificial stimuli to capture how people truly engage with the world around them. The RWEAD project is founded on a simple, powerful premise: to understand engagement, we must observe it where it naturally occurs. This dataset is exclusively composed of real-world video recordings, meticulously captured and annotated to document the subtle and overt signals of human engagement. Whether it's a person absorbed in a performance, a group reacting in a social setting, or an individual's focused interaction with technology in a public space, RWEAD provides a window into genuine behavioural responses.
Core Focus:
Engagement: Measuring the depth of involvement and emotional/cognitive investment in a real-world activity or stimulus.
Attention: Tracking visual and behavioral focus—where do people look, what captures their gaze, and for how long in unstructured environments?
Behaviour: Documenting the accompanying physical manifestations —facial expressions, body language, gestures, and interactions that complete the picture of engagement.
Designed for researchers in computer vision, human-computer interaction, psychology, marketing, and AI development, the RWEAD dataset empowers you to build and validate models that are robust, ethical, and applicable to real-life scenarios. Explore the data, discover the annotations, and join us in advancing a more authentic understanding of human behaviour.
The dataset will be constructed using a modular design and released periodically. Modules 1 and submodules use imagery (RGB or RGB-D data) as the primary input. Modules 2 and submodules incorporate at least two sensor modalities (e.g., RGB-D, thermal, mmWave, microphone, and LiDAR).
Note: PoI - Point of interest or PoE - Point of Engagement, will be considered the same point, unless stated otherwise.
Dataset is structured into three primary categories (under construction):
(i) Corporate:
Amphitheatre
RWEAD #1: Attention - Room (2D: 14 (1080p) + 14 (1520p) 30s videos) - Public
RWEAD #1: Engagement - Amphitheatre (3D: 8 (1080p) + [104 + 52PoE] (1080p) 30s videos) - Public
RWEAD #1a: Engagement - Discussion Thesis (2D: 4 (1080p) ~1h30 videos) - Private
(ii) Open Spaces/Fairs
Indoor and Outdoor - Crowd
RWEAD #1d: Engagement and Sentiment - Crowded public classified videos (Under Construction) - Not yet public
(iii) Stands
Indoor
HallBuild5RGBD_2PoI
RWEAD #1b: Engagement and Sentiment - Hall Building, 2 PoI (RGB-D: 5 cameras (1080p) x 10 scenes) - Public
HallBuild1RGB_3AoI
RWEAD #1c: - KPI Constructs: conversion, commitment, and retention - Hall Building (RGB: 1 frontal cam (1080p) on an elevated point x 15 scenes) - public
HallBuild5Sensors_3PoI
RWEAD #2: Several Constructs & KPI - Hall Building, 3 PoI, 5 sensors (RGB-D; Thermal, mmWave, Microphone, LIDAR) - Public
RWEAD #1 download available at OSF - https://doi.org/10.17605/OSF.IO/453SH
RWEAD #1a private
RWEAD #1b download available at OSF - https://doi.org/10.17605/OSF.IO/AD6GH
RWEAD #1c download available at OSF - https://doi.org/10.17605/OSF.IO/WHNQV
RWEAD #2 download available at OSF - https://doi.org/10.17605/OSF.IO/EJQSA
For RWEAD #1 cite:
Lemos, Marco, Cardoso, Pedro J.S., & Rodrigues, João M.F. (2026) MiE: A Microscopic Model for Real-Time Group Engagement Estimation Using Gaze and Posture. Journal of Computational Science, 96. https://doi.org/10.1016/j.jocs.2026.102856.
For RWEAD #1b cite:
(temporary) Rodrigues, J. M. F., Lemos, M., El Afia, M., Solá, D., Walid, A., Santos, R., António, G., Cardoso, P. J. S., & Martins, J. A. (2026). MArtAE: A unified architecture for real-time audience experience analytics at live events. Accepted to the 8th International Conference on Soft Computing and its Engineering Applications (icSoftComp2026), Singapore.
For RWEAD #1c cite:
(temporary) Rodrigues, J. M. F., Lemos, M., El Afia, M., Solá, D., Walid, A., Santos, R., António, G., Cardoso, P. J. S., & Martins, J. A. (2026). MArtAE: A unified architecture for real-time audience experience analytics at live events. Accepted to the 8th International Conference on Soft Computing and its Engineering Applications (icSoftComp2026), Singapore.
For RWEAD #2 cite:
(temporary) Rodrigues, J. M. F., Lemos, M., El Afia, M., Solá, D., Walid, A., Santos, R., António, G., Cardoso, P. J. S., & Martins, J. A. (2026). MArtAE: A unified architecture for real-time audience experience analytics at live events. Accepted to the 8th International Conference on Soft Computing and its Engineering Applications (icSoftComp2026), Singapore.
ALGARVE 2030, Portugal 2030 and by the European Union, ALGARVE-FEDER-01180500, Ref. 17325 - Project AI.EVENTS
ALGARVE 2030, Portugal 2030 and by the European Union, ALGARVE-FEDER-02964500, Ref. 24298 - Project AI.INSIGHTSI