| 要約: | In recent years, the detection of cognitive load has received a lot of attention. Understanding the circumstances in which cognitive load occurs and reliably predicting such occurrences, offers the potential for considerable advances in the field of Human-Computer Interaction (HCI). Numerous HCI applications, ranging from medical and health-related solutions to (smart) automotive environments, would directly benefit from the reliable detection of cognitive load. However, this task still remains highly challenging. We present a machine learning (ML) approach based on ensemble learning for robust cognitive load classification. The features used by the proposed solution are generated from the interpretation of physiological measurements (e.g., heart rate, r-r interval, skin temperature, and skin response) from a wearable device. Hence, our approach consists of two steps: (1) transforming the original data into discriminative features and (2) training an ensemble model to accurately and robustly predict cognitive load. The empirical results confirm that our method has a superior performance compared to various state-of-the-art baselines on the original and transformed data. Moreover, in the open-data CogLoad@UbiComp 2020 Competition, the proposed approach achieved the best results among 17 competing approaches and outperformed all participating competitors by a considerable margin.
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