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Experimental Exploration of Objective Human Pain Assessment Using Multimodal Sensing Signals
Yingzi Lin1, Yan Xiao2, Li Wang1
1Intelligent Human Machine Systems Laboratory, College of Engineering, Northeastern University, Boston, MA, United States.
Frontiers in Neuroscience
|February 28, 2022
Summary
This study developed an objective pain intensity estimation system using multiple physiological signals. Multimodal sensing, including facial expressions and EEG, accurately detected varying pain levels in healthy subjects.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Physiology
Background:
- Pain assessment remains subjective, necessitating objective methods.
- Optimizing pain management requires accurate intensity estimation.
- Multimodal sensing offers a promising avenue for objective pain evaluation.
Purpose of the Study:
- To develop an objective pain intensity estimation system.
- To utilize multimodal sensing signals for pain detection.
- To validate the system's efficacy in experimental studies.
Main Methods:
- Recruited 28 healthy subjects for experimental studies.
- Collected nine physiological signals: facial expressions (FE), electroencephalography (EEG), eye movement (EM), skin conductance (SC), blood volume pulse (BVP), electromyography (EMG), respiration rate (RR), skin temperature (ST), and blood pressure (BP).
- Applied statistical analysis and machine learning algorithms for data interpretation.
Main Results:
- Facial expressions, EEG, SC, BVP, and BP effectively detected pain states.
- Multimodal sensing demonstrated promise in identifying different pain intensity levels.
- Decision-level multimodal fusion achieved efficient and accurate pain classification.
Conclusions:
- Objective pain intensity estimation is achievable using multimodal sensing.
- Combining physiological signals enhances pain detection accuracy.
- This approach holds potential for improved pain assessment and treatment.
