arXiv:2606.00752cs.LGcs.CE2026-06

构建首个公开的多模态ASMR数据集,同步记录生理与行为反应。

A multimodal dataset of photoplethysmography and continuous behavioral responses to ASMR and nature videos

  • 采集34人高分辨率PPG与视频同步数据,含主观连续标注。
  • 97%参与者响应刺激,生理指标显示特异性心率减缓。
  • 模型精准识别ASMR状态,适合情绪计算与个性化放松研究。

自主感官巅峰体验(ASMR)是一种以愉悦刺痛感和心率减缓为特征的体感现象。然而,其研究受限于缺乏标准化、可公开获取的多模态数据集。为此,我们提出了REST-ASMR(对环境与感官触发的反应)数据集,通过自然放松视频作为对照刺激,同步采集被试的生理动态与行为报告。该数据集包含34名参与者的数据,涵盖高分辨率光体积描记法(PPG)、时间对齐的音视频刺激以及连续主观标注。技术验证表明,刺激有效性高达97%响应率,刺激特异性组间一致性显著(p < 0.05),且基于PPG提取的ASMR特异性心率减缓表现稳健。此外,双向长短期记忆模型在严格无泄漏的双独立四折交叉验证下,实现视频级分类完美准确率,帧级全局平均准确率为75.51%,宏平均F1得分为71.86%,对自然视频基线的特异性达100%。REST-ASMR为情感计算、多模态研究及个性化放松响应建模提供了密集的时间序列基础。

原文摘要 · Abstract (English)

Autonomous Sensory Meridian Response (ASMR) is a somatosensory phenomenon characterized by pleasant tingling sensations and cardiovascular slowing. However, ASMR research has been hindered by a dearth of standardized, open-access multimodal datasets. To address this limitation, we present REST-ASMR (Response to Environmental & Sensory Triggers), a synchronized multimodal dataset designed to capture behavioral reports and physiological dynamics during ASMR, with nature-relaxation videos as control stimuli. The dataset includes high-resolution photoplethysmography (PPG), time-aligned audiovisual stimuli, and continuous subjective annotations from 34 participants. Technical validation showed high stimulus efficacy (97% responder rate), significant stimulus-specific inter-subject agreement (p < 0.05), and a robust PPG-derived ASMR-specific cardiovascular deceleration. Additionally, a Bidirectional Long-Short Term Memory model successfully predicted subjective ASMR tingle states, achieving video-level ASMR vs. Nature classification with perfect accuracy and a frame-level global mean accuracy of 75.51%, macro F1-score of 71.86%, and 100% Nature-baseline specificity, under a strict, leakage-free subject-video double-independent 4-fold cross-validation. REST-ASMR constitutes a dense temporal foundation for affective computing, multimodal research, and the development of personalized models of relaxation-related responses.

ASMR多模态生理信号情感计算

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