Drowsiness is a common human physiological response. Research suggests that insufficient sleep results in low energy levels, various negative physiological and psychological effects on the body, and abnormal cognitive functioning. Studies have predominantly focused on driving while drowsy, using brainwave measurement and facial detection techniques to address this topic, whereas few have discussed the physiological prediction of drowsiness. In addition to driving, working conditions and environments as well as daily activities also correspond to the risk of accidents occurring when people are drowsy. This study designed an experiment consisting of five tests in which a brainwave sensor, eye tracker, heart rate sensor, and galvanic skin response sensor were used to record physiological changes in participants. The data indicated the binary outcomes of patients either being or not being in a state of drowsiness or sleepiness. During various states of drowsiness or sleepiness, brain wave activity, eye movement, heart rate, and GSR were measured, and differences in these physiological responses were analyzed. A classification model was also used to predict a participant’s state of drowsiness or sleepiness. Data on physiological characteristics included eye movement and the heart rate value, which was calculated during various states of drowsiness or sleepiness to obtain a value. Brain wave and GSR signals were converted through software development kit (SDK) programming at the sensor end. Subsequently, the data were processed through an artificial neural network (ANN), back propagation network, and support vector machine (SVM). In the experiment, the SVM hyperparameters were adjusted, the ANN model was added to the Adam optimization model, and overfitting was avoided to ensure that the results were comprehensive. According to the experiment results, the use of SVM yields the optimal classification performance, reaching an accuracy of 89.1%; 90% of participants were also categorized more accurately through SVM than ANN.

中文翻译：

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使用多模态生物信号预测自由生活条件下的生理认知状态

困倦是一种常见的人类生理反应。研究表明，睡眠不足会导致精力不足，对身体产生各种负面的生理和心理影响，以及认知功能异常。研究主要集中在昏昏欲睡时驾驶，使用脑电波测量和面部检测技术来解决这个话题，而很少讨论睡意的生理预测。除了驾驶，工作条件和环境以及日常活动也对应着人们在昏昏欲睡时发生事故的风险。本研究设计了一个由五项测试组成的实验，其中使用脑电波传感器、眼动仪、心率传感器和皮肤电反应传感器来记录参与者的生理变化。数据表明患者处于或未处于困倦或困倦状态的二元结果。在各种困倦或困倦状态下，测量脑电波活动、眼球运动、心率和 GSR，并分析这些生理反应的差异。分类模型也用于预测参与者的困倦或困倦状态。生理特征数据包括眼球运动和心率值，这是在各种困倦或困倦状态下计算得出的值。在传感器端通过软件开发工具包（SDK）编程转换脑电波和 GSR 信号。随后，数据通过人工神经网络（ANN）、反向传播网络和支持向量机（SVM）进行处理。在实验中，调整SVM超参数，在Adam优化模型中加入ANN模型，避免过拟合，保证结果全面。根据实验结果，使用SVM产生了最优的分类性能，达到了89.1%的准确率；90% 的参与者也通过 SVM 比 ANN 更准确地分类。