Classifying Emotionally Induced Pain Intensity Using Multimodal Physiological Signals and Subjective Ratings: A Pilot Study
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,...
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MDPI AG
2025-06-01
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| author | Eun-Hye Jang Young-Ji Eum Daesub Yoon Sangwon Byun |
| author_facet | Eun-Hye Jang Young-Ji Eum Daesub Yoon Sangwon Byun |
| author_sort | Eun-Hye Jang |
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| description | 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. |
| format | Article |
| id | doaj-art-122be1ec5ac64ba2aa370f077cd766dc |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-122be1ec5ac64ba2aa370f077cd766dc2025-08-20T03:28:32ZengMDPI AGApplied Sciences2076-34172025-06-011513714910.3390/app15137149Classifying Emotionally Induced Pain Intensity Using Multimodal Physiological Signals and Subjective Ratings: A Pilot StudyEun-Hye Jang0Young-Ji Eum1Daesub Yoon2Sangwon Byun3Mobility User Experience Research Section, Electronics Telecommunication and Research Institute, Daejeon 34129, Republic of KoreaDepartment of Psychology, Chungnam National Universisty, Daejeon 34134, Republic of KoreaMobility User Experience Research Section, Electronics Telecommunication and Research Institute, Daejeon 34129, Republic of KoreaDepartment of Electronics Engineering, Incheon National University, Incheon 22012, Republic of KoreaWe 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.https://www.mdpi.com/2076-3417/15/13/7149emotional painperceived pain intensityphysiological signalsautonomic nervous systemvalencearousal |
| spellingShingle | Eun-Hye Jang Young-Ji Eum Daesub Yoon Sangwon Byun Classifying Emotionally Induced Pain Intensity Using Multimodal Physiological Signals and Subjective Ratings: A Pilot Study Applied Sciences emotional pain perceived pain intensity physiological signals autonomic nervous system valence arousal |
| title | Classifying Emotionally Induced Pain Intensity Using Multimodal Physiological Signals and Subjective Ratings: A Pilot Study |
| title_full | Classifying Emotionally Induced Pain Intensity Using Multimodal Physiological Signals and Subjective Ratings: A Pilot Study |
| title_fullStr | Classifying Emotionally Induced Pain Intensity Using Multimodal Physiological Signals and Subjective Ratings: A Pilot Study |
| title_full_unstemmed | Classifying Emotionally Induced Pain Intensity Using Multimodal Physiological Signals and Subjective Ratings: A Pilot Study |
| title_short | Classifying Emotionally Induced Pain Intensity Using Multimodal Physiological Signals and Subjective Ratings: A Pilot Study |
| title_sort | classifying emotionally induced pain intensity using multimodal physiological signals and subjective ratings a pilot study |
| topic | emotional pain perceived pain intensity physiological signals autonomic nervous system valence arousal |
| url | https://www.mdpi.com/2076-3417/15/13/7149 |
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