用手机传感器采集的脉搏信号，96.7%准确率识别压力事件
Stress Assessment with Convolutional Neural Network Using PPG Signals
- 用原始脉搏信号输入自适应卷积神经网络+MLP，直接识别压力
- 在公开数据集WESAD上实现96.7%的压力事件检测准确率
- 适合可穿戴设备做实时压力监测，无需复杂信号处理
压力是现代生活方式的主要问题之一，若长期持续会对身体造成不良影响。因此，早期发现压力对预防其危害、促进健康生活至关重要。可通过生理信号评估压力，其中光电容积脉搏波描记法（PPG）是最具优势的信号之一。本研究聚焦于利用Empatica E4传感器采集的原始PPG信号，开发一种新型压力事件评估方法。为此，采用自适应卷积神经网络（CNN）与多层感知机（MLP）相结合的方法，实现压力事件的检测。研究使用公开可用的可穿戴压力与效应检测（WESAD）数据集进行模型模拟和性能验证。所提模型能有效区分正常事件与压力事件，在测试集上达到96.7%的准确率。
原文摘要 · Abstract (English)
Stress is one of the main issues of nowadays lifestyle. If it becomes chronic it can have adverse effects on the human body. Thus, the early detection of stress is crucial to prevent its hurting effects on the human body and have a healthier life. Stress can be assessed using physiological signals. To this end, Photoplethysmography (PPG) is one of the most favorable physiological signals for stress assessment. This research is focused on developing a novel technique to assess stressful events using raw PPG signals recorded by Empatica E4 sensor. To achieve this goal, an adaptive convolutional neural network (CNN) combined with Multilayer Perceptron (MLP) has been utilized to realize the detection of stressful events. This research will use a dataset that is publicly available and named wearable stress and effect detection (WESAD). This dataset will be used to simulate the proposed model and to examine the advantages of the proposed developed model. The proposed model in this research will be able to distinguish between normal events and stressful events. This model will be able to detect stressful events with an accuracy of 96.7%.
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