Stuart Moran

Analysing the factors affecting users in intelligent pervasive spaces

Recent advancements in pervasive computing allow embedded Information Communication Technology (ICT) systems to collect information and apply it in ways that result in new advanced services. In order to provide these services, a vast number of sensors are used to collect large levels of data pervasively. While successful in many environments, in some situations, monitoring technologies have been known to cause undesirable effects, such as increases in stress in those being observed. With an increase in the coverage of the data collection in pervasive spaces, we anticipate an increase in the impact of such effects. To date, the use of this monitoring technology and its effect on human behaviour have not been thoroughly investigated, meaning future system designs may result in (preventable) undesirable effects. This article analyses a series of recurring factors, identified in the literature and believed to influence occupant behaviour, from both physical and social perspectives. The use of such factors, and their relationships, as a means of analysing and comparing monitoring systems is also described. These factors/concepts can be linked to existing predictive behavioural models, and when empirical evidence is collected, a tool for predicting, and therefore preventing, the undesirable effects of this new technology may be possible.

Ubiquitous Monitoring in the Office: Salient Perceptions of Data Collection Devices

The principle of pervasive computing is to embed sensory and other electronic devices into everyday objects. This will seamlessly enable advanced services and applications, but requires the continuous collection of data about the user. While research into the effects of this new type of monitoring remains relatively unstudied, work has began to explore these issues. The focus of this research has been on the impact of the monitoring systems design, leaving the role of the user relatively unexplored. This acts as the foundation of this paper, which aims to explore user perceptions of ubiquitous monitoring systems. Questionnaire data is collected to test the strength and direction of relations between perceptions; which are then validated.

Ubiquitous monitoring and user behaviour: A preliminary model

Recent advances in ubiquitous computing are turning our environments into smart spaces, where technology is embedded into the physical environment. Significant levels of data must be constantly and ubiquitously collected to provide much of the new functionality; a process we refer to as ubiquitous monitoring. Existing research has shown that monitoring can often cause undesirable effects, such as increases in stress, and with the increased coverage of ubiquitous monitoring, we anticipate an increase in the impact of such effects. So far, a limited amount of research has investigated the impact of this new technology on users. As such, we propose a preliminary model consisting of a series of factors believed to influence user/occupant behaviour and augmented by the Theory of Planned Behaviour for explaining and potentially predicting any undesirable effects. As the model has the characteristic of system dynamics, a preliminary (proof of concept) simulation was carried out to examine the influence of each factor on one another, both directly and indirectly.

Assessment of assistive technologies based on the PSA-BI model

One consequence of ongoing discoveries and successes related to the prevention and treatment of illnesses and diseases is an aging population. With this comes an increase in chronic illnesses which will place a significant strain on existing healthcare resources. One proposed solution is the introduction of assistive technologies such as telecare, to allow patients to live and be monitored at home. Much of this technology can be enabled by pervasive technologies. As a part of the process of implementing this assistive technology, an assessment is carried out by social workers. This assessment aims to elicit user requirements regarding a telecare system, based upon which an appropriate system is proposed and implemented. Unfortunately, there can often be a mismatch between a users perceptions and expectations, and the system which is installed, leading to non-acceptance. This paper proposes and demonstrates how the PSABI model can be used as a part of this process. The factors that compose the model are shown to be effective at capturing information relevant to the monitoring aspects of telecare. The model itself is also simulated using VenSim, with a series of system designs assessed to see which meets the requirements of its users; and ultimately their acceptance of the technology.

Ubiquitous Monitoring and Human Behaviour in Intelligent Pervasive Spaces

We are soon approaching the pervasive-era ofcomputing, where computers are embedded intoobjects and the environment in order to provide newservices to users. Significant levels of data arerequired in order for these services to function asintended, and it is this collection of data which werefer to as ubiquitous monitoring. Existing monitoringtechniques have often been known to cause undesirableeffects, and it is anticipated that ubiquitousmonitoring, with its increased coverage, will lead toincreases in their occurrence and impact. To date, theeffects of ubiquitous monitoring on human behaviourhave not been sufficiently investigated, furtherincreasing the risk of undesirable effects. We propose apreliminary model consisting of a series of factorsbelieved to influence human behavior and augmentedby the Theory of Planned Behaviour. This model mayallow us to understand, predict, and therefore preventany undesirable effects caused by ubiquitousmonitoring.

Ubiquitous Monitoring and User Perceptions as a Persuasive Strategy

Observation is one of the most commonly used techniques to change behaviour through persuasion. When pervasive technology adopts this technique, in the form of ubiquitous monitoring, the benefits are likely to be significant. However, there is generally a lack of systematic methods for designing persuasive systems. This research aims to explore the use of the PSA-BI model and the 3D-RAB model in creating a method for systematically designing intentional behavioural change into ubiquitous monitoring systems.

Studying the impact of ubiquitous monitoring technology on office worker behaviours: The value of sharing research data

Pervasive computing is a continually, and rapidly, growing field, although still remains in relative infancy. The possible applications for the technology are numerous, and stand to fundamentally change the way users interact with technology. However, alongside these are equally numerous potential undesirable effects and risks. The lack of empirical naturalistic data in the real world makes studying the true impacts of this technology difficult. This paper describes how two independent research projects shared such valuable empirical data on the relationship between pervasive technologies and users. Each project had different aims and adopted different methods, but successfully used the same data and arrived at the same conclusions. This paper demonstrates the benefit of sharing research data in multidisciplinary pervasive computing research where real world implementations are not widely available.

The Behavioural Implications of Ubiquitous Monitoring

Ubiquitous environments such as intelligent pervasive spaces are designed to make life better for users. In order to provide much of their intended functionality, significant amounts of data need to be collected about users through sensors deployed ubiquitously. Existing monitoring technologies have been known to often cause undesirable effects, and it is anticipated that ubiquitous monitoring, with its increased coverage, will result in an increase in the occurrence of these effects. So far, a limited amount of research has investigated the impact of this technology on users. As such, we present a preliminary model, consisting of a series of factors related to ubiquitous monitoring believed to influence behaviour, and augmented by the Theory of Planned Behaviour for understanding, predicting and therefore preventing any undesirable effects.

A Distributed Cognition Based Tool for Information Trajectory Analysis

Distributed cognition is a useful approach to study the interaction between agents (humans or artefacts) in a system, such as the collaboration involved in air traffic control. When taking a distributed cognition perspective, information processing can be directly observed. This provides the opportunity to explore how information moves through a system, and to identify specific areas where (pervasive) technology can be introduced. However, the analysis involved can not be simply used off-the-shelf and must be tailored to a specific scenario. Furthermore, the amount of time spent conducting the analysis is often quite significant. This motivates the need for a software tool to support this kind of analysis, to guide the researcher, and reduce the amount of time spent on analysis. This paper presents a prototype of such a tool.

A Semiotic Analysis of a Model for Understanding User Behaviours in Ubiquitously Monitored Environments

Improvements in electronics and computing have increased the potential of monitoring and surveillance technologies. Although now widely used, these technologies have been known to cause unintended effects, such as increases in stress in those being observed. Further advancements in technology lead people towards the pervasive era of computing, where a new means of monitoring ubiquitously becomes possible. This monitoring differs from existing methods in its distinct lack of physical boundaries. To address the effects of this kind of monitoring, this paper proposes a model consisting of a series of factors identified in the monitoring and pervasive literature believed to influence behaviour. The model aims to understand and predict behaviour, thereby preventing any potential undesirable effects, but also to provide a means to analyse the problem. Various socio-technical frameworks have been proposed to guide research within ubiquitous computing; this paper uses the semiotic framework to analyse the model in order to better understand and explain the behavioural impact of ubiquitous monitoring.