000265352 001__ 265352
000265352 005__ 20190812204800.0
000265352 037__ $$aCONF
000265352 245__ $$aReal-Time Cognitive Workload Monitoring Based on Machine Learning Using Physiological Signals in Rescue Missions
000265352 260__ $$c2019-04-24
000265352 269__ $$a2019-04-24
000265352 300__ $$a7
000265352 336__ $$aConference Papers
000265352 520__ $$aHigh levels of cognitive workload decreases human's performance and leads to failures with catastrophic outcomes in risky missions. Today, reliable cognitive workload detection presents a common major challenge, since the workload is not directly observable. However, cognitive workload affects several physiological signals that can be measured non-invasively. The main goal of this work is to develop a reliable machine learning algorithm to identify the cognitive workload induced during rescue missions, which is evaluated through drone control simulation experiments. In addition, we aim to minimize the computing resources usage while maximizing the cognitive workload detection accuracy for a reliable real-time operation. We perform an experiment in which 24 subjects played a rescue mission simulator while respiration, electrocardiogram, photoplethysmogram, and skin temperature signals were measured. State-of-the-art feature-based machine learning algorithms are investigated for cognitive workload characterization using learning curves, data augmentation, and cross-validation techniques. The best classification algorithm is selected, optimized, and the most informative features are selected. Finally, the generalization power of the optimized model is evaluated on an unseen test set. We obtain an accuracy level of 86% on the new unseen datasets using the proposed and optimized eXtreme Gradient Boosting (XGB) algorithm. Then, we reduce the complexity of the machine learning model for future implementation on resource-constrained wearable embedded systems, by optimizing the model and selecting the 26 most important features. Overall, a generalizable and low-complexity machine learning model for cognitive workload detection based on physiological signals is presented for the first time in the literature.
000265352 700__ $$aMomeni, Niloofar
000265352 700__ $$g206640$$aDell'Agnola, Fabio Isidoro Tiberio$$0248838
000265352 700__ $$g261121$$aArza Valdes, Adriana$$0251510
000265352 700__ $$0240268$$aAtienza Alonso, David$$g169199
000265352 7112_ $$aInternational Engineering in Medicine and Biology Conference
000265352 8560_ $$famir.aminifar@epfl.ch
000265352 8564_ $$uhttps://infoscience.epfl.ch/record/265352/files/EMBC_paper.pdf$$s975935
000265352 909C0 $$mdavid.atienza@epfl.ch$$mhomeira.salimi@epfl.ch$$0252050$$zMarselli, Béatrice$$xU11977$$pESL
000265352 909CO $$pconf$$pSTI$$ooai:infoscience.epfl.ch:265352
000265352 960__ $$aamir.aminifar@epfl.ch
000265352 961__ $$afantin.reichler@epfl.ch
000265352 973__ $$aEPFL$$rREVIEWED
000265352 980__ $$aCONF
000265352 981__ $$aoverwrite