Research Hypothesis/Framework
New technologies such as Artificial Intelligence can support our cognitive strengths to provide relevant information about the status of a safety-critical system and suggest possible procedures to cope with plant status upsets. The research at HFISS provides novel methodologies for collaborative intelligence systems that integrate the most relevant standards to address Human Factors and Neuroergonomics. The exhaustive analyses based on human physiology are adopted to enable better, more efficient and safer working conditions in the modern industry.
In HFISS we understand that AI systems involved in collaborative robotics (cobotics) can embody human skills to extend our physical capabilities. The end-users should not be subject to a decision based solely on automated processing but, in addition, there should always be an oversight of the human performance aspect involved in the design of collaborative systems. This new era opens opportunities that, by no means signifies the displacement of human intervention, but the creation of hybrid teams where humans and machines collaborate synergistically to achieve common goals.
The research group has a multidisciplinary focus as it also stem from Current and ongoing research project successfully awarded to the Research Team members involved such as the Marie Curie ITN Called CISC on collaborative intelligence for Safety Critical tasks, and an ICT-2020 project called Teaming-AI in collaboration with colleagues from the school of computer Science focusing on human factors strand of the research and coordinate our contribution across a number of case studies on AI and Human interactions for advanced manufacturing tasks.
Our researchers at HFISS are aware that the quality of data and subjectivity of information introduce a not neglectable level of uncertainty that models must account for. Therefore, the data analyses carried out at HFISS takes account for dependencies in a safety-critical system able to adapt its learning and predictive skills depending on the available information.
Overall, the solutions developed at HFISS are designed to address the need for modern socio-technical systems that support the design of Human-Machine Interfaces based on scientific evidence.
The solutions developed at HFISS are produced to go well beyond the replacement of human operators by the creation of synergic human-machine cooperation to achieve higher performance and create sustainable, inclusive and safer environments. This transformative approach can proceed towards the application of modern technologies that focus on humans and our harmony with the environment.
Co-participation with industry, the main stakeholders, is key to the expertise and training offered by the Research Group for Human Factors in Safety & Sustainability (HFISS) at TU Dublin. The flourishing network will provide an optimal platform for employability as well as personal growth. Knowledge, expertise, technologies and skills exchange are fundamental part of HFISS accelerating the application of our researchers’ solutions in the public and private sectors.
Leva, M. C., Demichela, M., Comberti, L., & Caimo, A. (2022). Human performance in manufacturing tasks: Optimization and assessment of required workload and capabilities. Safety Science, 154, 105838.
Petruni, A., Giagloglou, E., Douglas, E., Geng, J., Leva, M. C., & Demichela, M. (2019). Applying Analytic Hierarchy Process (AHP) to choose a human factors technique: Choosing the suitable Human Reliability Analysis technique for the automotive industry. Safety Science, 119, 229-239.
Kontogiannis, T., Leva, M. C., & Balfe, N. (2017). Total safety management: principles, processes and methods. Safety science, 100, 128-142.