Rebecca Grant

The field of ergonomics and human factors (E/HF) has an urgent need for increased uptake of specialists to meet the growing demands of the sector and related industries employing ergonomists. In the absence of E/HF being part of the school curriculum, one way of achieving this is through public engagement activities. Specialists in E/HF are motivated by understanding people and interactions, inherent qualities in outreach and engagement, so we are well placed to practice what we preach by understanding our own users, the consumers of our science. In this paper, we present a curriculum map and museum exhibition model as part of ongoing work to develop a public exhibition, called 'Think Human', hosted at the Museum of Making in Derby, England, from September 2024, as part of ongoing work in timely celebration of the 75th anniversary of the UK's Chartered Institute of Ergonomics and Human Factors (CIEHF).

The adoption of Industry 4.0 technologies in manufacturing systems has accelerated in recent years, with a shift towards understanding operators’ well-being and resilience within the context of creating a human-centric manufacturing environment. In addition to measuring physical workload, monitoring operators’ cognitive workload is becoming a key element in maintaining a healthy and high-performing working environment in future digitalized manufacturing systems. The current approaches to the measurement of cognitive workload may be inadequate when human operators are faced with a series of new digitalized technologies, where their impact on operators’ mental workload and performance needs to be better understood. Therefore, a new method for measuring and determining the cognitive workload is required. Here, we propose a new method for determining cognitive-workload indices in a human-centric environment. The approach provides a method to define and verify the relationships between the factors of task complexity, cognitive workload, operators’ level of expertise, and indirectly, the operator performance level in a highly digitalized manufacturing environment. Our strategy is tested in a series of experiments where operators perform assembly tasks on a Wankel Engine block. The physiological signals from heart-rate variability and pupillometry bio-markers of 17 operators were captured and analysed using eye-tracking and electrocardiogram sensors. The experimental results demonstrate statistically significant differences in both cardiac and pupillometry-based cognitive load indices across the four task complexity levels (rest, low, medium, and high). Notably, these developed indices also provide better indications of cognitive load responding to changes in complexity compared to other measures. Additionally, while experts appear to exhibit lower cognitive loads across all complexity levels, further analysis is required to confirm statistically significant differences. In conclusion, the results from both measurement sensors are found to be compatible and in support of the proposed new approach. Our strategy should be useful for designing and optimizing workplace environments based on the cognitive load experienced by operators.

Measured variability of product within Cell and Gene Therapy (CGT) manufacturing arises from numerous sources across pre-analytical to post-analytical phases of testing. Operators are a function of the manufacturing process and are an important source of variability as a result of personal differences impacted by numerous factors. This research uses measurement uncertainty in comparison to Coefficient of Variation to quantify variation of participants when they complete Flow Cytometry data analysis through a 5-step gating sequence. Two study stages captured participants applying gates using their own judgement, and then following a diagrammatical protocol, respectively. Measurement uncertainty was quantified for each participant (and analysis phase) by following Guide to the Expression of Uncertainty in Measurement protocols, combining their standard deviations in quadrature from each gating step in the respective protocols. When participants followed a diagrammatical protocol, variation between participants reduced by 57%, increasing confidence in a more uniform reported cell count percentage. Measurement uncertainty provided greater resolution to the analysis processes, identifying that most variability contributed in the Flow Cytometry gating process is from the very first gate, where isolating target cells from dead or dying cells is required. This work has demonstrated the potential for greater usage of measurement uncertainty within CGT manufacturing scenarios, due to the resolution it provides for root cause analysis and continuous improvement.

Background: Flow Cytometry is a complex measurement characterisation technique, utilised within the manufacture, measurement and release of Cell and Gene Therapy products for rapid, high content and multiplexed discriminatory cell analysis. A number of factors influence the variability in the measurement reported including, but not limited to, biological variation, reagent variation, laser and optical configurations and data analysis methods. This research focuses on understanding the contribution of manual operator variability within the data analysis phase. Methods: 38 participants completed a questionnaire providing information about experience and motivational factors, before completing a simple gating study. Results were analysed using Gauge Repeatability & Reproducibility techniques to quantify participant uncertainty. The various stages of the gating sequence were combined through summation in quadrature and expanded to give each participant a representative uncertainty value. Results: 85 % of participants surveyed preferred manual gating to automated data analysis, with primary reasons being legacy (″it′s always been done that way″) and accuracy, not in the metrological sense, but in clear definition of the correct target population. The median expanded uncertainty was calculated as 3.6 % for the population studied, with no significant difference between more or less experienced users. Discussion: Operator subjectivity can be quantified to include within measurement uncertainty budgets, required for various standards and qualifications. An emphasis on biomanufacturing measurement terminology is needed to help understand future and potential solutions, possibly looking at translational clinical models to engage and enhance better training and protocols within industrial and research settings.