提出低采样率腕部GSR处理方法，实现无单位依赖的应激检测。
Unit-Independent Low-Rate Wrist GSR Processing for Stress Detection Using Phasic nSCR Features
- 基于鲁棒z-score归一化提取每分钟皮肤电反应次数（nSCR/min）
- 25Hz数据下分类准确率达0.871，接近100Hz原数据效果
- 适用于可穿戴设备，避免设备间差异影响结果
皮电反应（GSR）广泛用于应激检测，但腕部GSR因绝对幅值与实验室级掌部测量差异大而具挑战性。本文提出一种无单位依赖的低采样率腕部GSR处理流程，提取每分钟皮肤电反应次数（nSCR/min）作为应激特征。在31名参与者参与坐姿基线、站姿基线、中性言语和Trier社交应激测试（TSST）期间，同步采集腕部与掌部GSR信号。该流程对原始信号去噪，分解为稳态皮肤电水平（SCL）和瞬时皮肤电反应（SCR），进行鲁棒z-score归一化，并检测瞬时SCR峰值以计算nSCR/min。在25Hz We-Be GSR数据上，使用随机森林进行二分类，区分TSST与坐姿/站姿基线的平衡准确率分别达到0.823和0.871。此外，25Hz特征在所有任务中表现与原始100Hz特征相当。结果表明，低采样率、无单位依赖的腕部GSR处理在可穿戴应激检测中具有可行性。
原文摘要 · Abstract (English)
Galvanic skin response (GSR) is widely used for stress detection, but wrist-based GSR remains challenging because its absolute amplitude can differ substantially from laboratory-grade palmar measurements. In this paper, we propose a unit-independent low-rate wrist GSR processing pipeline to extract the number of skin conductance responses per minute (nSCR/min) as a stress-related feature. We collect paired wrist and palmar GSR recordings from 31 participants during sitting baseline, standing baseline, neutral speaking, and the Trier Social Stress Test (TSST), a laboratory social stressor task. The proposed pipeline cleans the raw GSR signal, decomposes it into tonic skin conductance level (SCL) and phasic skin conductance response (SCR), applies robust z-score normalization, and detects phasic SCR peaks to compute nSCR/min. Using random forest on 25Hz We-Be GSR, nSCR/min achieved balanced accuracies of 0.823 and 0.871 for binary classification between TSST and the sitting and standing baselines, respectively. Moreover, the 25Hz We-Be GSR features achieved comparable balanced accuracy to the original 100Hz features across the evaluated tasks. These results suggest the feasibility of low-rate, unit-independent wrist GSR processing for wearable stress detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。