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

2026-05-30

AI Summary

Existing ASMR research is hindered by the absence of standardized, open-access multimodal datasets. To address this gap, this work introduces the REST-ASMR dataset, which synchronously records high-resolution photoplethysmography (PPG) signals, audiovisual stimuli, and continuous subjective annotations from 34 participants while they viewed ASMR and natural control videos. This dataset constitutes the first publicly available, temporally aligned, high-density multimodal resource for ASMR research and reveals a distinctive cardiovascular deceleration effect specific to ASMR, along with consistent cross-participant responses. Using a BiLSTM model within a leakage-free double cross-validation framework, the system achieves 100% video-level classification accuracy, 75.51% frame-level global average accuracy, and a macro F1-score of 71.86%, while maintaining 100% specificity against natural video baselines.

Abstract

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.

Problem

• ASMR
• multimodal dataset
• photoplethysmography
• behavioral responses
• affective computing

Innovation

• multimodal dataset
• photoplethysmography (PPG)
• ASMR
• continuous behavioral annotation
• BiLSTM prediction