论文标题
部分可观测时空混沌系统的无模型预测
Measuring Cognitive Workload Using Multimodal Sensors
论文作者
论文摘要
这项研究旨在使用多模式感应方法和机器学习来确定一组指标，以估计认知工作量。进行了一组三项认知测试，以在两个级别的任务难度（轻松而硬）的两个级别的参与者中诱导认知工作量。使用四个传感器来测量参与者的生理变化，包括心电图（ECG），电肌活动（EDA），呼吸（RESS）和血氧饱和度（SPO2）。为了了解感知到的认知工作量，每次测试后都使用NASA-TLX，并使用卡方检验进行分析。使用生理数据对三个知识分类器（LDA，SVM和DT）进行了训练和测试。统计分析表明，参与者的认知工作量在测试之间显着不同（p <0.001），这证明了实验条件诱导不同认知水平的有效性。分类结果表明，ECG和EDA的融合为认知工作负载检测提供了良好的区分功率（ACC = 0.74）。这项研究为确定一组认知工作量指标提供了初步结果。需要进行未来的工作，以使用更现实的场景和更大的人口来验证指标。
This study aims to identify a set of indicators to estimate cognitive workload using a multimodal sensing approach and machine learning. A set of three cognitive tests were conducted to induce cognitive workload in twelve participants at two levels of task difficulty (Easy and Hard). Four sensors were used to measure the participants' physiological change, including, Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and blood oxygen saturation (SpO2). To understand the perceived cognitive workload, NASA-TLX was used after each test and analysed using Chi-Square test. Three well-know classifiers (LDA, SVM, and DT) were trained and tested independently using the physiological data. The statistical analysis showed that participants' perceived cognitive workload was significantly different (p<0.001) between the tests, which demonstrated the validity of the experimental conditions to induce different cognitive levels. Classification results showed that a fusion of ECG and EDA presented good discriminating power (acc=0.74) for cognitive workload detection. This study provides preliminary results in the identification of a possible set of indicators of cognitive workload. Future work needs to be carried out to validate the indicators using more realistic scenarios and with a larger population.
