用PPG信号在虚拟现实里实时识别压力状态
Stress Detection from Photoplethysmography in a Virtual Reality Environment
- 通过PPG信号监测生理变化，非侵入式评估心理状态
- 在16名受试者上实现70.6%的压力/平静分类准确率
- 适合心理健康治疗、可穿戴设备集成的场景
个性化虚拟现实暴露疗法（VRET）可根据个体患者动态调整，提升治疗效果。然而，准确测量患者心理状态以调节疗法是关键挑战，现有研究多依赖主观评估，易产生偏差。本文提出一种基于虚拟现实暴露疗法的平台，利用非侵入且普遍可用的生理信号——光电容积脉搏波（PPG）来评估患者心理状态。在一项案例研究中，我们评估了使用PPG信号检测两种二元状态（平静与压力）的可行性。16名健康受试者分别暴露于放松和压力型虚拟现实环境。采用留一被试者交叉验证（LOSO），最优分类模型在两类状态间达到了70.6%的准确率，优于许多更复杂的方法。
原文摘要 · Abstract (English)
Personalized virtual reality exposure therapy is a therapeutic practice that can adapt to an individual patient, leading to better health outcomes. Measuring a patient's mental state to adjust the therapy is a critical but difficult task. Most published studies use subjective methods to estimate a patient's mental state, which can be inaccurate. This article proposes a virtual reality exposure therapy (VRET) platform capable of assessing a patient's mental state using non-intrusive and widely available physiological signals such as photoplethysmography (PPG). In a case study, we evaluate how PPG signals can be used to detect two binary classifications: peaceful and stressful states. Sixteen healthy subjects were exposed to the two VR environments (relaxed and stressful). Using LOSO cross-validation, our best classification model could predict the two states with a 70.6% accuracy which outperforms many more complex approaches.
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