Instantaneous Mental Workload Recognition Using Wavelet-Packet Decomposition and Semi-Supervised Learning

Abstract

The real-time monitoring of human operator's mental workload (MWL) is crucial for development of adaptive/intelligent human-machine cooperative systems in various safety/mission-critical application fields. Although datadriven approach has shown promise in MWL recognition, its major difficulty lies in how to acquire sufficient labeled data to train the model. This paper applies semi-supervised extreme learning machine (SS-ELM) to the problem of MWL classification based only on a small number of labeled data. The experimental data analysis results have shown that the proposed SS-ELM paradigm can effectively improve the accuracy and efficiency of MWL classification. The proposed semi-supervised learning paradigm may provide an alternative data-driven machine learning approach to effectively utilize a large number of unlabeled data, which can be readily collected under naturalistic (operational) task environments in many real-world applications.