The purpose of this paper is to propose foundations for a theory of situation awareness based on the analysis of interactions between agents (i.e. both human and non-human) in subsystems. This approach may help to promote a better understanding of technology-mediated interaction in systems, as well as helping in the formulation of hypotheses and predictions concerning distributed situation awareness. It is proposed that agents within a system each hold their own situation awareness, which may be very different from (although compatible with) that of other agents. It is argued that we should not always hope for, or indeed want, sharing of this awareness, as different system agents have different purposes. This view marks situation awareness as a dynamic and collaborative process binding agents together on tasks on a moment-by-moment basis. Implications of this viewpoint for the development of a new theory of, and accompanying methodology for, distributed situation awareness are offered.

The concept of situation awareness (SA) is frequently described in the literature. Theoretically, it remains predominantly an individual construct and the majority of the models presented describe SA from an individual perspective. In comparison, team SA has received less attention. SA in complex, collaborative environments thus remains a challenge for the human factors community, both in relation to the development of theoretical perspectives and of valid measures and to the development of guidelines for system, training and procedure design. This article presents a review and critique of what is currently known about SA and team SA, including a comparison of the most prominent individual and team models presented in the literature. In conclusion, it is argued that recently proposed systems level distributed SA approaches are the most suited to describing and assessing SA in real world, collaborative environments.

Wearable computer comfort can be affected by numerous factors, making its assessment based on one value with one scale inappropriate. This paper presents a tool that measures wearable comfort across six dimensions: emotion, attachment, harm, perceived change, movement, and anxiety. The dimensions for these comfort rating scales were specifically developed for wearable equipment assessment by applying multidimensional scaling to a comfort term association matrix developed using the results of groupings of wearable computer comfort terms. Testing the scales on four different types of wearable computer showed that the scales can be used to highlight differences in comfort between different types of technology for different aspects of comfort. An intraclass correlation of .872 suggested that the scales were used with a high level of reliability. A second study showed that modifications made to a wearable computer resulted in improvements in comfort, although they were not significant (p > .05). A potential application for this research is as an aid to designers and researchers for assessing the wearability, in terms of comfort, of wearable computer devices and to determine the effectiveness of any modifications made to the design of a wearable device.

There is increasing interest in the use of social network analysis as a tool to study the performance of teams and organizations. In this paper, processes of command and control in the emergency services are explored from the perspective of social network theory. We report a set of network analyses (comprising visualization, a selection of mathematical metrics, and a discussion of procedures) based on the observation of six emergency service incidents: three fire service operations involving the treatment of hazardous chemicals, and three police operations involving immediate response to emergency calls. The findings are discussed in terms of our attempts to categorize the network structures against a set of extant command and control network archetypes and the relationship between those structures; comments on the qualities the networks display are put into the contexts of the incidents reported. We suggest that social network analysis may have a valuable part to play in the general study of command and control.

In this paper we investigate whether virtual reality (VR) and augmented reality (AR) offer potential for the training of manual skills, such as for assembly tasks, in comparison to conventional media. We present results from experiments that compare assembly completion times for a number of different conditions. We firstly investigate completion times for a task where participants can study an engineering drawing and an assembly plan and then conduct the task. We then investigate the task under various VR conditions and context-free AR. We discuss the relative advantages and limitations of using VR and AR as training media for investigating assembly operations, and we present the results of our experimental work.

C4i is defined as the management infrastructure needed for the execution of a common goal supported by multiple agents in multiple locations and technology. In order to extract data from complex and diverse C4i scenarios a descriptive methodology called Event Analysis for Systemic Teamwork (EAST) has been developed. With over 90 existing ergonomics methodologies already available, the approach taken was to integrate a hierarchical task analysis, a coordination demand analysis, a communications usage diagram, a social network analysis, and the critical decision method. The outputs of these methods provide two summary representations in the form of an enhanced operation sequence diagram and a propositional network. These offer multiple overlapping perspectives on key descriptive constructs including who the agents are in a scenario, when tasks occur, where agents are located, how agents collaborate and communicate, what information is used, and what knowledge is shared. The application of these methods to live data drawn from the UK rail industry demonstrates how alternative scenarios can be compared on key metrics, how multiple perspectives on the same data can be taken, and what further detailed insights can be extracted. The ultimate aim of EAST is, by applying it across a number of scenarios in different civil and military domains, to provide data to develop generic models of C4i activity and to improve the design of systems aimed at enhancing this management infrastructure.