IEEE SMC: Systems, Man, & Cybernetics Society

Transactions on Human Machine Systems

Scope

The scope of the IEEE Transactions on Human-Machine Systems includes the field of human-machine systems. It covers human systems and human organizational interactions including cognitive ergonomics, system test and evaluation, and human information processing concerns in systems and organizations.

IEEE Transactions on Human-Machine Systems was launched on January 1, 2013 to replace the human-machine systems sections of IEEE Transactions on Systems, Man and Cybernetics.

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Listen to David Kaber’s interview with Rodney Roberts (SMC VP for Systems Science & Engineering) on the current state of THMS, the Journal’s review process, key requirements for submissions, and upcoming special issue information.

2024: New Impact Factor = 4.4

Editor-in-Chief

Ljiljana Trajkovic
Ljiljana Trajkovic
Editor-In-Chief
Professor
School of Engineering Science
Simon Fraser University
8888 University Drive
Burnaby, BC V5A 1S6
Canada
Tel.: 1.604.519.1776
1.604.345.7705 (mobile)
Email Ljiljana Trajkovic

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Articles

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12 June 2026
Enhancing Early Mortality Prediction With Clinical Notes as Time-Series Scores
The optimization of early mortality prediction models to accurately identify high-risk patients during a hospital admission is key in medical research to assist in healthcare decision-making. While these models use structured (physiological features) or unstructured (free-text clinical notes) data, integrating them synergistically, especially for early prediction, remains a challenge. We...
02 June 2026
Deep Learning-Driven Decision Fusion: Spatio-Spectrogram Features for Inner Speech Recognition From Electroencephalogram Signals
Inner speech recognition using electroencephalogram (EEG) signals shows strong potential for developing assistive communication technologies. Existing methods often process spatial and temporal features separately, lack interpretability, and are usually tested on a single dataset, limiting their generalization. This study proposes a dual-branch deep learning framework that combines spatial features extracted...
27 April 2026
Inferring Pilot Cognitive States Using a Multimodal Physiology-Based Graph Fusion Model
Accurate recognition of pilots' cognitive states is essential for intelligent cockpit systems operating in complex and high-load flight environments. Existing methods either rely on single-modal physiological signals or fail to fully exploit the intrinsic temporal, spatial, and cross-modal structures of multimodal data. To address these limitations, we propose a multimodal...
20 April 2026
Enhancing Auditory Brainstem Response Extraction From Noised EEG With Adaptive Kalman Denoising Technique
Auditory brainstem response (ABR) is a weak evoked EEG signal that provides an objective measure for assessing auditory function. However, the traditional extraction method, namely, averaging is noise-sensitive and needs thousands of trials, which places high demands on the subjects and the experimental environment. Kalman weighted (KW) technique has the...
20 April 2026
A Vision-Driven End-User Robot Programming Method Based on the Alignment Between Body Language and Motion Commands
This work is motivated by the complexity of conventional robot programming methods, which require a deep understanding of both robotics and software development. This situation presents significant challenges for nontechnical end-users who need to program a new robotic task. To address this issue, we propose a new robot programming method...

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