For decades automation has played a critical role in air and space flight as well as in industrial settings such as manufacturing and processing plants. Today, humans interact with automated systems every day including in their home (e.g. robotic vacuums and home monitoring systems) and in their vehicles (e.g. navigation systems). The role of automation is meant to improve the human condition and improve our quality of life. However the benefits of automation are sometimes lost when humans override an automated decision due to a fundamental lack of trust in the machine. Research has shown that close to 80% of the major accidents that have affected lives and the environment are linked to errors, some of which may have originated from trust issues in automation. Therefore, it is necessary for us to study human-machine interactions so that the automated systems of tomorrow are not only safer but more effective in promoting the health and prosperity of humans. The goal of this research project is to enable emotional intelligence in machines. That is to say, we believe that if a machine has the ability to sense and process the feelings, particularly, trust, of the human interacting with it, the human and machine can better cooperate to achieve a common goal or complete a given task. In order to realize this vision, it is necessary for machines to be able to 1) measure a human's emotional state and 2) respond accordingly to the human through changes in its user interface (UI). We will study the use of different psychophysiological tools to make these measurements as well as utilize mathematical tools to develop models that capture how humans respond emotionally to visual changes in machine UIs. The results of this study will then enable us to redesign machine UIs that exhibit emotional intelligence. This research project is a collaboration between two faculty, one with an expertise in control and automation and another with an expertise in design and psychology. By bridging the gap between these disciplines, the PIs will be able to facilitate a dialogue and future research engagement between researchers in the fields of dynamical systems and controls, design, and the social sciences. This is critical because the study of human-machine interactions inherently requires tools and methods from both traditional engineering disciplines as well as the social sciences. It is also important for young engineers to understand the value and benefits of interdisciplinary research and to understand their own discipline in the context of other fields. To that end, the PIs will present the results of this project to engineering undergraduates to expose them to cutting edge research that is rooted in improving the human condition through collaboration across seemingly disparate fields of study. This also aligns with national goals of increasing participation in STEM fields by appealing to a wider demographic of students, including women and minorities.
With increasing automation in all aspects of society, humans are increasingly being displaced as the primary decision-maker in various roles (such as aircraft pilots and plant operators). However, humans still have the ability to override automated decisions, and a significant problem arises when the human overrides an automated decision due to a fundamental lack of trust in the machine. In what are broadly being called Human-Agent Collectives, we expect to see a growing need for cooperation and trust between humans and machines to accomplish a large range of tasks. Therefore, the way in which the machine senses and responds to the human is of particular importance. The objective of the proposed research is to understand the dynamic relationship between machine user interfaces (UIs) and human trust in automated systems. The new knowledge gained through this research will enable us to improve human-machine interactions by ultimately redesigning the user interface. This modified user interface will include an emotional intelligence system for the machine to respond to the human in real-time. Two specific aims guide this research. The first aim is to conduct a dynamical characterization of real-time measurements of trust. Such measurements do not currently exist and are necessary in order to allow machines to sense the trust level of the humans that they are interacting with. We propose to identify a dynamic model of trust that relies on real-time psychophysiological measurements such as galvanic skin response (GSR) and electroencephalography (EEG), as well as eye-tracking. The second aim is to define a mathematical framework for modeling human emotional response to machines. Machines communicate with humans through various design features in their user interface (UI). We propose to conduct a human subjects study to mathematically characterize how specific machine UI features can be used by a machine to dynamically change human trust in the machine. Due to the use of psychophysiological sensors, these models will be more widely applicable as compared to previously developed trust models that were specific to the context of the human subject studies from which the data was collected. This cross-disciplinary project will advance knowledge and understanding within the dynamics and control, design, and social science communities. Through the proposed research, we will enable the design of a closed-loop emotional intelligence system that achieves the overarching goal of improving the relationship between human and machine, thereby leading to more reliable and efficient operation of a range of automated systems. Additionally, the PIs will co-organize events centered on human-machine interaction at both dynamics/control and mechanical design conferences to encourage a dialogue and research collaborations across these otherwise disparate fields. Finally, in educational outreach, the PIs will disseminate this research to mechanical engineering undergraduates to expose them to the excitement and benefits of interdisciplinary research; this directly supports the ASME's vision for the mechanical engineer of 2030 by broadening the brand of mechanical engineering to appeal to a wider demographic of students, including women and minorities.
