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The uulmMAC Database-A Multimodal Affective Corpus for Affective Computing in Human-Computer Interaction.

Dilana Hazer-Rau1, Sascha Meudt2, Andreas Daucher1

  • 1Section Medical Psychology, University of Ulm, Frauensteige 6, 89075 Ulm, Germany.

Sensors (Basel, Switzerland)
|April 23, 2020
PubMed
Summary
This summary is machine-generated.

A new multimodal dataset, the University of Ulm Multimodal Affective Corpus (uulmMAC), was created for affective computing research. This dataset captures emotional and cognitive states during human-computer interaction (HCI) using diverse sensor data.

Keywords:
affective computingaffective corpuscognitive loademotion recognitionfrustrationhuman-computer interactioninterestmachine learningmultimodal sensorsoverloadstress researchunderload

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Area of Science:

  • Affective Computing
  • Human-Computer Interaction (HCI)
  • Multimodal Data Analysis

Background:

  • Affective computing research requires diverse, high-quality datasets for developing emotion recognition systems.
  • Existing datasets often lack the richness of real-world, interactive scenarios and multimodal data streams.

Purpose of the Study:

  • To introduce the University of Ulm Multimodal Affective Corpus (uulmMAC), a novel dataset for affective computing.
  • To provide a rich multimodal dataset acquired in a realistic, gamified human-computer interaction (HCI) setting.
  • To enable research on emotion recognition and cognitive load analysis in dynamic interactive environments.

Main Methods:

  • Designed and implemented a gamified interactive scenario to induce specific emotional and cognitive load states (Interest, Overload, Normal, Easy, Underload, Frustration).
  • Collected data from 57 subjects across 95 recording sessions using 16 sensor modalities (video, audio, biophysiological, depth, pose).
  • Incorporated subjective feedback, respiration baselines, and pre-experiment questionnaires (ERQ, TEIQue-SF, TIPI) for validation and stability assessment.

Main Results:

  • Subjective feedback confirmed significant differences between induced emotional and cognitive states.
  • Analysis of pre-experiment questionnaires indicated stable individual differences, supporting the reliability of the induction paradigm.
  • The final uulmMAC dataset comprises 57 subjects and 95 sessions, with post-processed and quality-checked multimodal data.

Conclusions:

  • The uulmMAC dataset is a valuable resource for affective computing and multimodal machine learning.
  • Its acquisition in a mobile, interactive HCI scenario makes it highly relevant for real-world applications.
  • The dataset's size, multimodal nature, and validated emotional state induction support transtemporal investigations and advanced affective computing research.