Lack of verified Inclusive Technology for Workers with disabilities in industry 4.0: a systematic review

ABSTRACT

Technologies from Industry 4.0 enhance human skills and capabilities in production. These advanced manufacturing and digital technologies unlock opportunities to integrate individuals with unique abilities into industrial environments, helping to attain social sustainability. However, the validation process with end-users in real-world manufacturing tasks ensures the technology is robust and aligned with individual needs. However, the topic is in its early stages, and only a few papers concerning validation have emerged in journals. This paper presents a systematic review utilising the PRISMA methodology to examine validated technologies proposed to empower differently-abled workers in the manufacturing sector. The supporting technologies were identified and sorted into four categories: collaborative robots, augmented reality, assistive technology, and gamification. Within the reviewed papers, quantitative and qualitative evidence emerged, showcasing how individuals with challenges proficiently employed technology to complete assembly tasks, elevate their working speed, and reduce the error rate. Nevertheless, there remains a lack of information concerning usability, intuitiveness, and ergonomic considerations. Furthermore, there’s an ongoing requirement for long-term studies, standardised methodologies, and statistical assessments conducted by a representative cross-section of participants. Beyond its influence on organisational social responsibility, this research aims to transcend the realm of cultivating a potential new workforce for manufacturing companies.

KEYWORDS:

• Enabling technology
• disability
• manufacturing
• workers
• inclusion
• industry 4.0

Acknowledgments

The authors want to thank to the Institute of Advanced Materials for Sustainable Manufacturing and Tecnologico de Monterrey.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors have declared no data availability in this manuscript.

Authors contribution

M.R., D.C.B., J.M., conceived of the presented idea. M.R., D.C.B., J.M., and D.L-B. carried out the papers screening. P.P. and A.M. contributed to the final version of the manuscript. All authors provided feedback on the research, analysis, and manuscript.

Additional information

Notes on contributors

Mario Rojas

Mario Rojas is doing a postdoctoral fellowship at the Institute of Advanced Materials for Sustainable Development, from Tecnologico de Monterrey. He completed a bachelor’s and a master’s degree in Electronic Sciences. Additionally, he received his Ph.D. degree in Engineering Sciences focusing on Robotics and assistive technology from Tecnologico de Monterrey. His research pursuits also encompass human-machine interfaces, artificial intelligence, and sensors.

David C. Balderas

David Balder as is a full-time Research Professor for the Computer Department Tecnologico de Monterrey CCM. He is part of the Institute of Advanced Materials for Sustainable Manufacturing, working in Simulations, Optimisation, Deep Learning, Robotics, and Vision research. He works in biomedical applications such as Brain-Computer Interfaces, haptics, and prostheses.

Javier Maldonado

Javier Maldonado Romo received his professional studies in telematics, a master’s degree in computer technology, and Ph.D. in robotic and mechatronic systems from the Instituto Politecnico Nacional de Mexico. He also studied management, focusing on sustainability at the Universidad Nacional Autonoma de Mexico. He is a postdoctoral fellow at the Institute of Advanced Materials for Sustainable Manufacturing at the Tecnologico de Monterrey in Mexico City. He focuses on developing novel products, processes, and services in digital manufacturing.

Pedro Ponce

Pedro Ponce is a control system and automation engineer. He did a master’s and doctoral degree in electrical engineering. His areas of interest are smart grids, microgrids, smart cities, AI, control systems, robotics, manufacturing, power electronics, digital twins, renewable energy, energy management, electric machines, and optimisation. He is a research leader in the Institute of Advanced Materials and Sustainable Manufacturing and a professor in the mechatronics department at Tecnologico de Monterrey on the Mexico City campus. Pedro Ponce specialises in control and automation, smart grids, electrical machines, machine learning, soft computing, digital twins, and metaheuristic optimisation.

Diego Lopez-Bernal

Diego Lopez-Bernal is an associate teacher at the Institute of Advanced Materials for Sustainable Manufacturing of the Monterrey Institute of Technology and Higher Education, Mexico. His scientific activity concerns signal processing, computer vision, robotics, and Brain-Computer Interfaces.

Arturo Molina

Arturo Molina is a distinguished scholar and Director of the Institute of Advanced Material for Sustainable Manufacturing at Tecnologico de Monterrey. With a Ph.D. in Manufacturing Engineering from Loughborough University of Technology and a University Doctorate in Mechanical Engineering from the Technical University of Budapest, he has contributed significantly to the field. His research focuses on manufacturing, computer science, and sustainable technologies.