Classifying social and physical pain from multimodal physiological signals using machine learning
Abstract Accurate pain assessment is essential for effective management; however, most studies have focused on differentiating pain from non-pain or estimating pain intensity rather than distinguishing between distinct pain types. We present a machine learning method for classifying physical and soc...
Saved in:
| Main Authors: | Eun-Hye Jang, Young-Ji Eum, Daesub Yoon, Sangwon Byun |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-12476-8 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Classifying Emotionally Induced Pain Intensity Using Multimodal Physiological Signals and Subjective Ratings: A Pilot Study
by: Eun-Hye Jang, et al.
Published: (2025-06-01) -
Pseudo-labeling based adaptations of pain domain classifiers
by: Tobias B. Ricken, et al.
Published: (2025-04-01) -
Pain and acupuncture: What is it in me that hurts?
by: Terje Alraek
Published: (2021-06-01) -
Investigating the Analgesic Mechanisms of Acupuncture for Cancer Pain: Insights From Multimodal Bioelectrical Signal Analysis
by: Huang J, et al.
Published: (2025-03-01) -
Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning
by: Pengbo Zhao, et al.
Published: (2024-01-01)