Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4921-4924. doi: 10.1109/EMBC48229.2022.9871308.
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 ( ) 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.
本研究旨在通过多模态传感方法和机器学习来确定一组用于评估认知负荷的指标。在本研究中，我们让 12 名参与者在两种任务难度（简单和困难）下进行了三组认知测试，以诱导认知负荷。研究使用了四种传感器来测量参与者的生理变化，包括心电图（ECG）、皮肤电活动（EDA）、呼吸（RESP）和血氧饱和度（SpO2）。为了了解感知到的认知负荷，我们在每次测试后使用 NASA-TLX 进行评估，并使用卡方检验进行分析。我们使用三种知名的分类器（LDA、SVM 和 DT）分别对生理数据进行了训练和测试。统计分析表明，参与者在不同测试之间的感知认知负荷有显著差异（ ），这证明了实验条件可以有效地诱导不同的认知水平。分类结果表明，融合 ECG 和 EDA 可以很好地区分认知负荷（acc = 0.74）。本研究为识别认知负荷的可能指标提供了初步结果。未来的工作需要在更真实的场景和更大的人群中验证这些指标。
