Kim Youngho, Pyo Seonggeon, Lee Seunghee, Park Changeon, Song Sunghyuk
Department of Biomedical Engineering, Yonsei University, Wonju 26493, Republic of Korea.
Department of Robotics & Mechatronics, Korea Institute of Machinery & Materials, Daejeon 34103, Republic of Korea.
Sensors (Basel). 2025 Jan 23;25(3):680. doi: 10.3390/s25030680.
Quantification of pain or discomfort induced by pressure is essential for understanding human responses to physical stimuli and improving user interfaces. Pain research has been conducted to investigate physiological signals associated with discomfort and pain perception. This study analyzed changes in electrodermal activity (EDA), tissue oxygen saturation (StO), heart rate variability (HRV), and Visual Analog Scale (VAS) under pressures of 10, 20, and 30 kPa applied for 3 min to the thigh, knee, and calf in a seated position. Twenty participants were tested, and relationships between biosignals, pressure intensity, and pain levels were evaluated using Friedman tests and post-hoc analyses. Multiple linear regression models were used to predict VAS and pressure, and five machine learning models (SVM, Logistic Regression, Random Forest, MLP, KNN) were applied to classify pain levels (no pain: VAS 0, low: VAS 1-3, moderate: VAS 4-6, high: VAS 7-10) and pressure intensity. The results showed that higher pressure intensity and pain levels affected sympathetic nervous system responses and tissue oxygen saturation. Most EDA features and StO significantly changed according to pressure intensity and pain levels, while NN interval and HF among HRV features showed significant differences based on pressure intensity or pain level. Regression analysis combining biosignal features achieved a maximum R of 0.668 in predicting VAS and pressure intensity. The four-level classification model reached an accuracy of 88.2% for pain levels and 81.3% for pressure intensity. These results demonstrated the potential of EDA, StO, HRV signals, and combinations of biosignal features for pain quantification and prediction.
量化压力引起的疼痛或不适对于理解人类对物理刺激的反应以及改善用户界面至关重要。已经开展了疼痛研究来调查与不适和疼痛感知相关的生理信号。本研究分析了在坐姿下对大腿、膝盖和小腿施加10、20和30 kPa压力持续3分钟时，皮肤电活动（EDA）、组织氧饱和度（StO）、心率变异性（HRV）和视觉模拟评分（VAS）的变化。对20名参与者进行了测试，并使用弗里德曼检验和事后分析评估了生物信号、压力强度和疼痛水平之间的关系。使用多元线性回归模型预测VAS和压力，并应用五个机器学习模型（支持向量机、逻辑回归、随机森林、多层感知器、K近邻）对疼痛水平（无疼痛：VAS 0，低：VAS 1 - 3，中度：VAS 4 - 6，高：VAS 7 - 10）和压力强度进行分类。结果表明，较高的压力强度和疼痛水平会影响交感神经系统反应和组织氧饱和度。大多数EDA特征和StO根据压力强度和疼痛水平有显著变化，而HRV特征中的NN间期和HF基于压力强度或疼痛水平显示出显著差异。结合生物信号特征的回归分析在预测VAS和压力强度时的最大R值为0.668。四级分类模型对疼痛水平的准确率达到88.2%，对压力强度的准确率达到81.3%。这些结果证明了EDA、StO、HRV信号以及生物信号特征组合在疼痛量化和预测方面的潜力。
