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.

English language editing services can help refine the language of your article and reduce the risk of rejection without review. IEEE authors are eligible for a 10% discount at American Journal Experts; visit https://www.aje.com/go/ieee/ to learn more. Please note these services are fee-based and do not guarantee acceptance.

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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25 June 2026
Exploring the Effect of Gaze and Distance Guidance in Room-Scale Virtual Reality
The growing popularity of virtual reality (VR) provides new opportunities for cultural heritage protection and online education. Many studies have investigated how to guide users to move to a specific view/position. However, the coguided interaction of position and gaze is still under researched. In this article, a novel interaction model...
25 June 2026
IoT-Integrated Augmented Reality for Spatially Aware Human–Machine Interaction for IEEE Transactions on Human-Machine Systems
A human–machine interface (HMI) is an essential component of any modern industrial machine, acting as the portal for human workers to setup, monitor, control, and troubleshoot machines. Yet, existing HMIs often pose challenges in usability and learnability due to overwhelming data, nonstandard interfaces, and physical design limitations. We argue that...
22 June 2026
CNDNet: A Cross-State Neural Decoding Network for Unified Semantic Decoding From Perceptual and Imagery EEG
Electroencephalography (EEG), owing to its noninvasiveness, high temporal resolution, and the central role of perception and imagery in human cognition, has made EEG-based neural decoding highly significant in fields such as cognitive science and brain–computer interfaces. However, most existing studies focus solely on semantic decoding from either visual perception or...
19 June 2026
Fixed-Time Inverse Reinforcement Learning Based Human–Machine Autonomous Game Control
With the expansion of artificial intelligence (AI) from information intelligence to physical intelligence (embodied intelligence), human–machine collaborative intelligence has become a key path to achieving strong AI. This article proposes an autonomous game control algorithm based on fixed-time inverse reinforcement learning to address the limitations of existing human–machine collaboration methods,...
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...

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