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<p>We propose an emotion recognition dataset based on millimeter-wave (mmWave) radar and physiological reference signals. Compared to conventional methods, mmWave radar could obtain vital signs in a non-contact method without privacy concerns. We used validated stimuli to induce participants’ emotions and simultaneously recorded three types of signals: mmWave signals, photoplethysmography (PPG) pulse signals, and galvanic skin response (GSR) signals. Participants used the Self-Assessment Manikin (SAM) to provide subjective emotion ratings. We collected signals and emotion rating data from 15 participants and validated the effectiveness of emotion induction and the data quality. The dataset can be used for research such as: (1) mmWave radar-based vital sign extraction; (2) comparison of emotion recognition performance across different signals; (3) multi-modal fusion for emotion recognition; (4) individual differences in emotional responses; (5) cross-subject emotion recognition, among others.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月6日

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  • Jialong Cai,
  • Xinyan Zhang,
  • Yang Pan,
  • Hui Zhou

摘要

We propose an emotion recognition dataset based on millimeter-wave (mmWave) radar and physiological reference signals. Compared to conventional methods, mmWave radar could obtain vital signs in a non-contact method without privacy concerns. We used validated stimuli to induce participants’ emotions and simultaneously recorded three types of signals: mmWave signals, photoplethysmography (PPG) pulse signals, and galvanic skin response (GSR) signals. Participants used the Self-Assessment Manikin (SAM) to provide subjective emotion ratings. We collected signals and emotion rating data from 15 participants and validated the effectiveness of emotion induction and the data quality. The dataset can be used for research such as: (1) mmWave radar-based vital sign extraction; (2) comparison of emotion recognition performance across different signals; (3) multi-modal fusion for emotion recognition; (4) individual differences in emotional responses; (5) cross-subject emotion recognition, among others.