journal article·2025-06-25Open access

Classifying Emotionally Induced Pain Intensity Using Multimodal Physiological Signals and Subjective Ratings: A Pilot Study

Eun-Hye Jang, Young-Ji Eum, Daesub Yoon, Sangwon Byun

Applied Sciences· Volume 15· Issue 13

Abstract

We explore the feasibility of classifying perceived pain intensity—despite the stimulus being identical—using multimodal physiological signals and self-reported emotional ratings. A total of 112 healthy participants watched the same anger-inducing video, yet reported varying pain intensities (5, 6, or 7 on a 7-point scale). We recorded electrocardiogram, skin conductance (SC), respiration, photoplethysmogram results, and finger temperature, extracting 12 physiological features. Participants also rated their valence and arousal. Using a random forest model, we classified pain versus baseline and distinguished intensity levels. Compared to baseline, the painful stimulus altered heart rate variability, SC, respiration, and pulse transit time (PTT). Higher perceived pain correlated with more negative valence, higher arousal, and elevated SC, suggesting stronger sympathetic activation. The classification of baseline versus pain using SC and respiratory features reached an F1 score of 0.83. For intensity levels 6 versus 7, including PTT and skin conductance response along with valence achieved an F1 score of 0.73. These findings highlight distinct psychophysiological patterns that reflect perceived intensity under the same stimulus. SC features emerged as key biomarkers, while valence and arousal offered complementary insights, supporting the development of personalized, psychologically informed pain assessment systems.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Emotion and Mood Recognition · Heart Rate Variability and Autonomic Control · Psychosomatic Disorders and Their Treatments

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Mobility User Experience Research Section, Electronics Telecommunication and Research Institute, Daejeon 34129, Republic of Korea

Department of Psychology, Chungnam National Universisty, Daejeon 34134, Republic of Korea

Department of Electronics Engineering, Incheon National University, Incheon 22012, Republic of Korea

Funding

Incheon National University Research Grant in 2022 and Korea Evaluation Institute of Industrial Technology grant funded by the Ministry of Trade, Industry and Energy, Korea

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