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An emotion recognition dataset using millimeter wave radar and physiological reference signals.

Jialong Cai1, Xinyan Zhang2, Yang Pan3

  • 1School of Automation, Nanjing University of Science and Technology, Nanjing, China.

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This study introduces a novel emotion recognition dataset using non-contact millimeter-wave (mmWave) radar and physiological signals. This dataset enables advanced research in vital sign extraction and multi-modal emotion recognition.

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Area of Science:

  • Physiological computing
  • Human-computer interaction
  • Biomedical engineering

Background:

  • Conventional emotion recognition methods raise privacy concerns.
  • Millimeter-wave (mmWave) radar offers non-contact vital sign monitoring.
  • Physiological signals like PPG and GSR are established emotion indicators.

Purpose of the Study:

  • To introduce a novel dataset for emotion recognition.
  • To leverage mmWave radar for non-contact vital sign acquisition.
  • To facilitate research in multi-modal emotion recognition and individual differences.

Main Methods:

  • Collected mmWave radar, photoplethysmography (PPG), and galvanic skin response (GSR) signals.
  • Utilized validated stimuli for emotion induction.
  • Obtained subjective emotion ratings via Self-Assessment Manikin (SAM).

Main Results:

  • Successfully collected multi-modal physiological and radar data from 15 participants.
  • Validated the effectiveness of emotion induction protocols.
  • Confirmed the quality of the collected dataset for research.

Conclusions:

  • The proposed dataset supports mmWave radar-based vital sign extraction.
  • Enables comparative analysis of different physiological signals for emotion recognition.
  • Facilitates research in multi-modal fusion, individual differences, and cross-subject emotion recognition.