Abstract
The real-time assessment of mental workload (MWL) is critical for development of intelligent human–machine cooperative systems in various safety–critical applications. Although data-driven machine learning (ML) approach has shown promise in MWL recognition, there is still difficulty in acquiring a sufficient number of labeled data to train the ML models. This paper proposes a semi-supervised extreme learning machine (SS-ELM) algorithm for MWL pattern classification requiring only a small number of labeled data. The measured data analysis results show that the proposed SS-ELM paradigm can effectively improve the accuracy and efficiency of MWL classification and thus provide a competitive ML approach to utilizing a large number of unlabeled data which are available in many real-world applications.
Highlights
Automation, automatic control system, and artificial intelligence (AI) techniques have been widely applied to various fields, but there is still a long way for the current automation and AI technologies to achieve fully-automated control for many real-world complex and uncertain systems
machine learning (ML) techniques have shown promising performance in model-based mental workload (MWL) detection, a practical limitation of ML methods is the lack of a sufficient number of labeled data for modeling training
As the Supervised Learning (SSL) method only requires a small amount of labeled data, in the present investigation it is applied to real-time detection of high-risk MWL based on physiological data
Summary
Automation, automatic control system, and artificial intelligence (AI) techniques have been widely applied to various fields, but there is still a long way for the current automation and AI technologies to achieve fully-automated control for many real-world complex and uncertain systems In this connection, human–machine systems (HMS) are still ubiquitous in most safety–critical application domains (Lal and Craig 2001). MWL measurement approaches can be roughly divided into three categories (Mahfouf et al 2007): (1) subjective assessment; (2) task performance measures; and (3) physiological data based assessment. Compared with the former two approaches, the last approach is featured by continuous on-line measurement. ElectroEncephaloGram (EEG), ElectroCardioGram (ECG) and ElectroOculoGram (EOG) have been widely used for MWL recognition
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