CORTEXA
← Browse
crossrefSensors2024-03-30Cited by 55

Towards a Human-Centric Digital Twin for Human–Machine Collaboration: A Review on Enabling Technologies and Methods

Maros Krupas, Erik Kajati, Chao Liu, Iveta Zolotova

With the intent to further increase production efficiency while making human the centre of the processes, human-centric manufacturing focuses on concepts such as digital twins and human–machine collaboration. This paper presents enabling technologies and methods to facilitate the creation of human-centric applications powered by digital twins, also from the perspective of Industry 5.0. It analyses and reviews the state of relevant information resources about digital twins for human–machine applications with an emphasis on the human perspective, but also on their collaborated relationship and the possibilities of their applications. Finally, it presents the results of the review and expected future works of research in this area.

View free PDFSource page

Related papers

crossrefSensors2022-12-12Cited by 37

Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion

Izaldein Al-Zyoud, Fedwa Laamarti, Xiaocong Ma, Diana Tobón, Abdulmotaleb El Saddik

Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three…

View free PDFSource page
crossrefSensors2025-07-05Cited by 11

Human-Centric Cognitive State Recognition Using Physiological Signals: A Systematic Review of Machine Learning Strategies Across Application Domains

Kaizhe Jin, Adrian Rubio-Solis, Ravi Naik, Daniel Leff, James Kinross, George Mylonas

This systematic review analyses advancements in cognitive state recognition from 2010 to early 2024, evaluating 405 relevant articles from an initial pool of 2398 records identified through five databases: Scopus, Engineering Village, Web of Science, IEEE Xplore, and PubMed. Stud…

View free PDFSource page
crossrefSensors2023-03-27

Design of Digital-Twin Human-Machine Interface Sensor with Intelligent Finger Gesture Recognition

Dong-Han Mo, Chuen-Lin Tien, Yu-Ling Yeh, Yi-Ru Guo, Chern-Sheng Lin, Chih-Chin Chen, et al.

In this study, the design of a Digital-twin human-machine interface sensor (DT-HMIS) is proposed. This is a digital-twin sensor (DT-Sensor) that can meet the demands of human-machine automation collaboration in Industry 5.0. The DT-HMIS allows users/patients to add, modify, delet…

View free PDFSource page
crossrefSensors2024-04-22Cited by 11

Prototype Learning for Medical Time Series Classification via Human–Machine Collaboration

Jia Xie, Zhu Wang, Zhiwen Yu, Yasan Ding, Bin Guo

Deep neural networks must address the dual challenge of delivering high-accuracy predictions and providing user-friendly explanations. While deep models are widely used in the field of time series modeling, deciphering the core principles that govern the models’ outputs remains a…

View free PDFSource page
crossrefSensors2024-01-19Cited by 73

Human Digital Twin in Industry 5.0: A Holistic Approach to Worker Safety and Well-Being through Advanced AI and Emotional Analytics

Saul Davila-Gonzalez, Sergio Martin

This research introduces a conceptual framework designed to enhance worker safety and well-being in industrial environments, such as oil and gas construction plants, by leveraging Human Digital Twin (HDT) cutting-edge technologies and advanced artificial intelligence (AI) techniq…

View free PDFSource page