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Connected Everything knows that diverse teams deliver high quality research outcomes. Furthermore, Equality, Diversity and Inclusion approaches lead to happy and healthy research teams. Connected Everything wants to encourage, support and respect ideas from everyone and ensure our inclusive activities are representative of our network’s community

The future’s industrial workforce will be a product of today’s society. As manufacturing evolves to incorporate digital manufacturing technologies (DMTs), addressing societal concerns surrounding these technologies will support ethical and effective implementation.Using the ContraVision technique, we developed two videos showing fictional, futuristic utopian and dystopian perspectives on DMTs. These videos were used to elicit feedback from the public on perceptions of these technologies. Results showed that regardless of the utopic or dystopic condition, participants acknowledged benefits of such technologies despite expressing concerns, specifically identifying issues around ethical implementation and data security.

Whole Genome Sequencing Study: My Genomic Life Survey. This version has been refined for use with a larger participant population. It contains all items necessary to reproduce the survey. (PDF 646 kb)

Statistical Results. It contains all statistical tests undertaken for this study. (DOCX 24 kb)

Feedback is valuable for allowing us to improve on tasks. While retrospective feedback can help us improve for next time, feedback “in action” can allow us to improve the outcome of on-going tasks. In this paper, we use data from functional Near InfraRed Spectroscopy to provide participants with feedback about their Mental Workload levels during high-workload tasks. We evaluate the impact of this feedback on task performance and perceived task performance, in comparison to industry standard mid-task self assessments, and explore participants’ perceptions of this feedback. In line with previous work, we confirm that deploying self-reporting methods affect both perceived and actual performance. Conversely, we conclude that our objective concurrent feedback correlated more closely with task demand, supported reflection in action, and did not negatively affect performance. Future work, however, should focus on the design of this feedback and the potential behaviour changes that will result.

Inertial sensors offer the potential for integration into wireless virtual reality systems that allow the users to walk freely through virtual environments. However, owing to drift errors, inertial sensors cannot accurately estimate head and body orientations in the long run, and when walking indoors, this error cannot be corrected by magnetometers, due to the magnetic field distortion created by ferromagnetic materials present in buildings. This paper proposes a technique, called EHBD (Equalization of Head and Body Directions), to address this problem using two head- and shoulder-located magnetometers. Due to their proximity, their distortions are assumed to be similar and the magnetometer measurements are used to detect when the user is looking straight forward. Then, the system corrects the discrepancies between the estimated directions of the head and the shoulder, which are provided by gyroscopes and consequently are affected by drift errors. An experiment is conducted to evaluate the performance of this technique in two tasks (navigation and navigation plus exploration) and using two different locomotion techniques: (1) gaze-directed mode (GD) in which the walking direction is forced to be the same as the head direction, and (2) decoupled direction mode (DD) in which the walking direction can be different from the viewing direction. The obtained results show that both locomotion modes show similar matching of the target path during the navigation task, while DD’s path matches the target path more closely than GD in the navigation plus exploration task. These results validate the EHBD technique especially when allowing different walking and viewing directions in the navigation plus exploration tasks, as expected. While the proposed method does not reach the accuracy of optical tracking (ideal case), it is an acceptable and satisfactory solution for users and is much more compact, portable and economical

Research continues to correlate physical signals with mental activity, as opposed to physical activity, with physiological sensors. Further, with the proliferation of wearable technology, it seems imminent that our smart watches can soon keep track of our mental activity as well as our physical activity. Our research is working towards accurately measuring Mental Workload ‘in the wild’ using physiological sensors. While we work towards that goal, however, we have begun to explore the design aspects of representing personal cognitive data to users; analogous to a step counter for physical activity. We present the results of diary studies, focus groups, and prototyping exercises to identify design considerations for future cognitive activity trackers.

The European Rail Traffic Management System (ERTMS) will have an impact on the train driving task and train driver behaviour. This paper presents part of the final study of series of studies that have been conducted as part of a three-year research project, which investigated the effects of ERTMS on train drivers’ behaviours. In recent times, a number of models of train driving have been developed in order to inform train driving technology design and understand the implications of the design of the train driving task on driver performance and behaviour. An expert knowledge elicitation study was conducted to evaluate existing train driving models and to generate human factors guidance on future ERTMS train driving models. The study consisted of a workshop which was conducted using rail Human Factors (HF) experts who have been directly involved with projects examining ERTMS. Current train driving models were evaluated and ideas generated about how these should be shaped in the future to include systems such as ERTMS. The findings of the study contributed to bridging the gap between the theoretical understanding of train drivers’ cognitive strategies and practical implementation of novel technologies by the rail industry.

Secondary healthcare systems around the world are facing ever increasing pressures and governments frequently revise the provision of important services, often without available data or studies necessary to understand the demand placed on clinical teams. The situation is particularly severe during Out of Hours (OoH) settings, which account for over 75% of the working week. Here, doctors work in a stressful environment, performing complex tasks and making difficult prioritisation decisions. In this work, we discuss methodology and applications demonstrating the potential of data science and machine learning, in order to design intelligent systems capable of informing, supporting and driving improvements in health care policy and management. For that matter, we introduce the use of combined location and tasking informatics, sourcing diverse data allowing for the study of clinical behaviour and workload patterns in OoH secondary care settings; and we review example applications and ongoing work on Bayesian statistics, graphical models and queueing networks. Specifically, we exploit comprehensive data for the purposes of location identification and evaluation of task demandandworkload. Andweshowitispossibletogainvaluable information for the purposes of rota scheduling, task prioritisation or resource allocation, among other things.

This paper discusses an ongoing project that aims at improving the potential for resilience of a system responsible for the planning of rail engineering work delivery. This is being addressed by means of a methodology based on the observation and analysis of “real” planning activities, using resilience engineering concepts as a background. Interviews with planners have been carried out to provide an overview of the planning process and steer more in-depth investigation. Analysis of historic information and observation of planners’ main activities is underway. Given the nature of the process under study, information flows and communication issues have been given particular attention throughout the data collection and analysis stages. Initial data confirms that the planning process is greatly reliant on the capability of people using their knowledge and skills to communicate in a dynamic informational environment. Evidence was found of communication breakdowns at the boundaries of different planning levels and teams. The fact that the process is divided amongst several different areas of the organisation, often with different goals and needs, creates potential sources of conflict and tension.