Interviewer: What is your name, position/role and your institution?

Elizabeth Argyle: I'm Elizabeth Argyle i'm a research fellow in the human factors research group at the University of Nottingham.

Interviewer: Can you provide a short overview of the research you have conducted?

Elizabeth Argyle: Digital manufacturing is an exciting area to work in right now because there are so many new opportunities for technology to support humans at work and for it to support industrial productivity. There are so many different potential benefits that introducing digital technology can have both related to productivity as well as sustainability and many other areas.

However, it is worth considering that there are some potential issues that need to be addressed before these technologies can be introduced and research is starting to show that there can be some different unintended consequences particularly in regard to how workers experience these technologies if introduced in ways that don't consider up front how they might impact the people who are using them.

Our work in digiTop has looked at one type of technology in particular particularly related to sensing and by sensing we're focusing in our work package at the University of Nottingham on physiological data. So we're looking at this in the context of operator state monitoring situations where we might be looking at using physiological data collected through body worn sensors to try to estimate human physiological state as well as cognitive state this is particularly important in situations where there's a large degree of decision-making and cognitive work so you may have moved from a situation where work in an industrial setting was primarily physical but this is starting to transition to more monitoring and oversight situations perhaps monitoring machinery or settings on a process.

So we have looked at a number of different physiological sensors to try to understand what are some of the best parameters that we can estimate from human physiology that map onto these constructs such as mental workload, situation awareness, fatigue and others. We started the project looking at a series of personas which are fictional representations of potential manufacturing end users so they're everything from a line worker to a manager to a technology integrator and from these we identified a set of use cases which we've tried to base a series of studies on through our work package here at the University of Nottingham.

We've focused on two in particular most recently one looking at using physiological data to try to predict attentional degradation and mental workload and another where we've looked at a scenario involving variable assembly and task switching this is especially interesting in situations where you might have assembly line production which are moving towards more of a variable assembly setup where you have multiple product variants assembled on one line that has to reconfigure.

There are potential impacts to the workers on the line in that there are cognitive demands placed on them in this new framework of assembly. So we were trying to look at physiological sensors that could try to identify these types of factors that could lead to performance degradation.

In our first study we looked at identifying physiological variables that could predict loss of attention and increased mental workload in a quality control related task and we found several variables that mapped particularly well onto mental workload. In our second study which is currently ongoing we looked at a variable assembly task and we particularly were interested in trying to understand the effects when you have a switch between assembly tasks on the physiological response to try to understand if we can predict performance degradation from that.

We've had some really exciting opportunities to learn about these different types of physiological sensors and while there's still a lot of work that needs to be done in order to realize these in an operational context i think that there's a lot of promise to them and with future research and collaboration i i do believe that i think we're going to have some exciting times ahead for understanding and supporting workers in managing their fatigue levels and mental workload and and many different things that could support productivity and safety in industrial contexts.

Interviewer: Can you explain what the outcomes of your research mean for users/ industry?

Elizabeth Argyle: So from this we were able to identify a set of physiological variables that mapped quite nicely onto these two outcomes and from that we could say that there are some some things that we can learn from industry. Industry could particularly take away from our physiological research in that there are many different contexts where you might have safety critical operations.

So imagine you may have a scenario where you have say for example in the process industry you may have some processes that may need to be monitored quite carefully but as time goes by your operators may become more fatigued they may experience attentional degradation it may be that there are some time pressures or other factors that create high mental workload for them which we know from research is linked to performance decrement and potential for increased errors.

So errors in this case can have significant impacts on human well-being and safety and so what we want to do is try to develop these sensing systems that can help support operators and workers in managing their own wellness and safety by being able to track and estimate when they might be likely to experience performance degradation because of these types of cognitive factors.