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

REQUEST TO UPDATE CONTENT BOOKMARK PAGE

Articles

SUBMIT MANUSCRIPT
CURRENT ISSUE
PAST ISSUES

04 November 2025
Emergency Motor Intention Detection Based on Unpredictable Anticipatory Activity: An EEG Study
Objective: Emergency anticipation (EA) refers to the brain’s rapid perceptual, cognitive, and motor preparation in response to imminent emergencies. Timely decoding of EA can facilitate proactive responses before full behavioral execution, which is critical in real-world scenarios such as avoiding hazards or mitigating accidents. However, the cortical activation underlying the...
04 November 2025
Experience in Engineering Complex Systems: Active Preference Learning With Multiple Outcomes and Certainty Levels
Black-box optimization involves solving optimization problems where the objective function and/or constraints are unknown, inaccessible, or do not explicitly exist. In many applications, particularly those involving human interaction, the optimization problem can only be accessed through physical experiments, with the available outcomes based on the preference of one candidate over...
28 October 2025
A Human–Machine Cooperative Control Strategy Based on Deep Reinforcement Learning to Enhance Heavy Vehicle Driving Safety
As heavy vehicles advance toward increased intelligence and modernization, the control of advanced driver assistance systems for ensuring driving safety faces significant challenges. To enhance the driving safety of heavy vehicles operated by drivers with varying driving styles, this article proposes a human–machine cooperative control (HMCC) strategy that combines steering...
23 October 2025
Automotive Cockpit-Driving Integration for Human-Centric Autonomous Driving: A Survey
Intelligent driving aims to handle dynamic driving tasks in complex environments, while driver behavior onboard is less focused. In contrast, an intelligent cockpit mainly focuses on interacting with a driver, with limited connection to the driving scenarios. Since the driver onboard could affect the driving strategy significantly and thus have...
22 October 2025
AV-Lip-Sync+: Leveraging AV-HuBERT to Exploit Multimodal Inconsistency for Deepfake Detection of Frontal Face Videos
Multimodal manipulations (also known as audio-visual deepfakes) make it difficult for unimodal deepfake detectors to detect forgeries in multimedia content. To avoid the spread of false propaganda and fake news, timely detection is crucial. The damage to either modality (i.e., visual or audio) can only be discovered through multimodal models...

See more at IEEE Xplore