Abstract
The real-time operator’s mental workload (MWL) monitoring system is crucial for the design and development of adaptive operator-aiding/assistance systems. Although the data-driven approach has shown promising performance for MWL recognition, its major challenge lies in the difficulty in acquiring extensive labeled data. This paper attempts to apply the semi-supervised extreme learning machine (ELM) to the challenging problem of operator’s mental workload classification based only on a small number of labeled physiological data. The real data analysis results show that the semi-supervised ELM method can effectively improve the accuracy and computational efficiency of the MWL pattern classification.
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Li, J., Zhang, J. (2017). Mental Workload Classification Based on Semi-Supervised Extreme Learning Machine. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_34
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DOI: https://doi.org/10.1007/978-3-319-68612-7_34
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