ROSbag-based Multimodal Affective Dataset for Emotional and Cognitive States

TL;DR

This paper presents a large, synchronized multimodal affective dataset stored in ROSbag files, capturing emotional and cognitive states through physiological, behavioral, and subjective data for research in affective computing, HCI, and HRI.

Contribution

The paper introduces a novel, large-scale, synchronized multimodal affective dataset stored in ROSbag files, facilitating easier analysis and verification for affective computing research.

Findings

Dataset contains 1,602 ROSbag files totaling 787GB.

Includes synchronized physiological, behavioral, and subjective data.

Made publicly available for research use.

Abstract

This paper introduces a new ROSbag-based multimodal affective dataset for emotional and cognitive states generated using Robot Operating System (ROS). We utilized images and sounds from the International Affective Pictures System (IAPS) and the International Affective Digitized Sounds (IADS) to stimulate targeted emotions (happiness, sadness, anger, fear, surprise, disgust, and neutral), and a dual N-back game to stimulate different levels of cognitive workload. 30 human subjects participated in the user study; their physiological data was collected using the latest commercial wearable sensors, behavioral data was collected using hardware devices such as cameras, and subjective assessments were carried out through questionnaires. All data was stored in single ROSbag files rather than in conventional Comma-separated values (CSV) files. This not only ensures synchronization of signals and videos in a data set, but also allows researchers to easily analyze and verify their algorithms by connecting directly to this dataset through ROS. The generated affective dataset consists of 1,602 ROSbag files, and size of the dataset is about 787GB. The dataset is made publicly available. We expect that our dataset can be great resource for many researchers in the fields of affective computing, HCI, and HRI.