Automatically identifying driver inattention could dramatically improve road safety. This paper presents a preliminary study aiming to correlate high levels of frustration with posture information collected from the driver's seat. Using a driving simulator, participants had to drive under normal and frustrating conditions, for example parking in a tight spot with some time constraint. Binary classification using a range of machine learning algorithms provided encouraging results, showing that posture features could help reflect frustration and possibly other drivers' mental states.

Physiological sensors are widely used in user studies, often by practitioners with limited expertise in networking. However, large data volumes, and processing times often prevent the use of a single computer to collect the readings in real time. With multiple collection machines appear the problems of data aggregation and, more importantly, synchronisation. This paper describes how the OML reporting library allows solving the aggregation problem at low cost by introducing a lightweight instrumentation reporting to a centralised database. However, with unknown delays in network paths during aggregation and unreliable clocks on acquisition machines, synchronisation is hard to attain. We present a preliminary study of the theoretical feasibility of post hoc synchronisation corrections, supported by an experiment applying correction techniques to artificially impaired clocks and network transmissions. Based on the results of this experiment this paper highlights potential improvements.

COVID-19 has had a substantial impact globally. It spreads readily, particularly in enclosed and crowded spaces, such as public transport carriages, yet there are limited studies on how this risk can be reduced. We developed a tool for exploring the potential impacts of mitigation strategies on public transport networks, called the Systems Analytics for Epidemiology in Transport (SAfE Transport). SAfE Transport combines an agent-based transit assignment model, a community-wide transmission model, and a transit disease spread model to support strategic and operational decision-making. For this simulated COVID-19 case study, the transit disease spread model incorporates both direct (person-to-person) and fomite (person-to-surface-to-person) transmission modes. We determine the probable impact of wearing face masks on trains over a seven day simulation horizon, showing substantial and statistically significant reductions in new cases when passenger mask wearing proportions are greater than 80%. The higher the level of mask coverage, the greater the reduction in the number of new infections. Also, the higher levels of mask coverage result in an earlier reduction in disease spread risk. These results can be used by decision makers to guide policy on face mask use for public transport networks.

Accurate and noise robust multimodal activity and mental state monitoring can be achieved by combining physiological, behavioural and environmental signals. This is especially promising in assistive driving technologies, because vehicles now ship with sensors ranging from wheel and pedal activity, to voice and eye tracking. In practice, however, multimodal user studies are confronted with challenging data collection and synchronisation issues, due to the diversity of sensing, acquisition and storage systems. Referencing current research on cognitive load measurement in a driving simulator, this paper describes the steps we take to consistently collect and synchronise signals, using the Orbit Measurement Library (OML) framework, combined with a multimodal version of a cinema clapperboard. The resulting data is automatically stored in a networked database, in a structured format, including metadata about the data and experiment. Moreover, fine-grained synchronisation between all signals is provided without additional hardware, and clock drift can be corrected post-hoc.