User interaction monitoring and analysis framework

An overview is given of a user interaction monitoring and analysis framework called BaranC. Monitoring and analysing human-digital interaction is an essential part of developing a user model as the basis for investigating user experience. The primary human-digital interaction, such as on a laptop or smartphone, is best understood and modelled in the wider context of the user and their environment. The BaranC framework provides monitoring and analysis capabilities that not only records all user interaction with a digital device (e.g. smartphone), but also collects all available context data (such as from sensors in the digital device itself, a fitness band or a smart appliances). The data collected by BaranC is recorded as a User Digital Imprint (UDI) which is, in effect, the user model and provides the basis for data analysis. BaranC provides functionality that is useful for user experience studies, user interface design evaluation, and providing user assistance services. An important concern for personal data is privacy, and the framework gives the user full control over the monitoring, storing and sharing of their data.

The framework for REVIEWS: an exploration into design principles for an electronic medical early warning system observation chart

The observation chart is for many health professionals (HPs) the primary source of objective information relating to the health of a patient. Information Systems (IS) research has demonstrated the positive impact of good interface design on decision making and it is logical that good observation chart design can positively impact healthcare decision making. Despite the potential for good observation chart design, there is a paucity of observation chart design literature, with the primary source of literature leveraging Human Computer Interaction (HCI) literature to design better charts. While this approach has been successful, this design approach introduces a gap between understanding of the tasks performed by HPs when using charts and the design features implemented in the chart. Good IS allow for the collection and manipulation of data so that it can be presented in a timely manner that support specific tasks. Good interface design should therefore consider the specific tasks being performed prior to designing the interface. This research adopts a Design Science Research (DSR) approach to formalise a framework of design principles that incorporates knowledge of the tasks performed by HPs when using observation charts and knowledge pertaining to visual representations of data and semiology of graphics. This research is presented in three phases, the initial two phases seek to discover and formalise design knowledge embedded in two situated observation charts: the paper-based NEWS chart developed by the Health Service Executive in Ireland and the electronically generated eNEWS chart developed by the Health Information Systems Research Centre in University College Cork. A comparative evaluation of each chart is also presented in the respective phases. Throughout each of these phases, tentative versions of a design framework for electronic vital sign observation charts are presented, with each subsequent iteration of the framework (versions Alpha, Beta, V0.1 and V1.0) representing a refinement of the design knowledge. The design framework will be named the framework for the Retrospective Evaluation of Vital Sign Information from Early Warning Systems (REVIEWS). Phase 3 of the research presents the deductive process for designing and implementing V0.1 of the framework, with evaluation of the instantiation allowing for the final iteration V1.0 of the framework. This study makes a number of contributions to academic research. First the research demonstrates that the cognitive tasks performed by nurses during clinical reasoning can be supported through good observation chart design. Secondly the research establishes the utility of electronic vital sign observation charts in terms of supporting the cognitive tasks performed by nurses during clinical reasoning. Third the framework for REVIEWS represents a comprehensive set of design principles which if applied to chart design will improve the usefulness of the chart in terms of supporting clinical reasoning. Fourth the electronic observation chart that emerges from this research is demonstrated to be significantly more useful than previously designed charts and represents a significant contribution to practice. Finally the research presents a research design that employs a combination of inductive and deductive design activities to iterate on the design of situated artefacts.

Child-centred design supported by comprehensive child application use analysis

Since children already use and explore applications on smartphones, we use this as the starting point for design. Our monitoring and analysis framework, BaranC, enables us to discover and analyse which applications children uses and precisely how they interact with them. The monitoring happens unobtrusively in the background so children interact normally in their own natural environment without artificial constraints. Thus, we can discover to what extent a child of a particular age engages with, and how they physically interact with, existing applications. This information in turn provides the basis for design of new child-centred applications which can then be subject to the same comprehensive child use analysis using our framework. The work focuses on the first aspect, namely, the monitoring and analysis of current child use of smartphones. Experiments show the value of this approach and interesting results have been obtained from this precise monitoring of child smartphone usage.

Baran: an interaction-centred user monitoring framework

User Quality of Experience (QoE) is a subjective entity and difficult to measure. One important aspect of it, User Experience (UX), corresponds to the sensory and emotional state of a user. For a user interacting through a User Interface (UI), precise information on how they are using the UI can contribute to understanding their UX, and thereby understanding their QoE. As well as a user’s use of the UI such as clicking, scrolling, touching, or selecting, other real-time digital information about the user such as from smart phone sensors (e.g. accelerometer, light level) and physiological sensors (e.g. heart rate, ECG, EEG) could contribute to understanding UX. Baran is a framework that is designed to capture, record, manage and analyse the User Digital Imprint (UDI) which, is the data structure containing all user context information. Baran simplifies the process of collecting experimental information in Human and Computer Interaction (HCI) studies, by recording comprehensive real-time data for any UI experiment, and making the data available as a standard UDI data structure. This paper presents an overview of the Baran framework, and provides an example of its use to record user interaction and perform some basic analysis of the interaction.

Market-oriented new product development of health promoting foods for the ageing population

Major factors influencing food development and food marketing strategies in global market places at present can be attributable to the changing age structure of the population. The significant shifts in global age structure will inevitably lead to the number of people aged 60 reaching an all-time high of one billion by the year 2020. The rapidly growing population of ageing people globally represents a large, neglected and very much under-developed category within the Food Industry. The primary focus of this study was the integration of knowledge creation techniques at early NPD stages, for the development of market-oriented new health promoting foods for the ageing population. The methodology of this study was centered on an exploratory sequential mixed methods strategy. Stage one of the study involved in-depth semi-structured interviews with 16 Stakeholders to facilitate the need identification stage of the NPD process. The main outputs identified were the need for: the fortification of foods for a preventative nutrition approach, the development of foods that targeted age-related conditions such as cognitive, heart, gut and bone health, the integration of ageing compensatory packaging adaptations and the creation of marketing messages with an active lifestyle message. Stage two consisted of a market-oriented computer assisted NPD technique, a user centered design interaction (UCD) to integrate consumers as co-creators throughout the idea generation stage of the NPD process. The most important product attributes identified in this stage included: products targeted at brain and cognitive health, liquid based beverages, easy to use packaging with environmentally friendly elements, simplistic marketing with a clear focus on health not age and realistic health claims constructed with consumer friendly terminology. Finally, Stage three used an abbreviated means-end chain (MEC) analysis to complete the concept development stage of the NPD process. This stage identified commercial information that could be used by food firms for the development of positioning and communication strategies. Equally, the information generated could be of high strategic importance to governments, policy makers, health professionals and medical professionals. The values and goals listed in this stage included: better overall health, active lifestyle, optimum nutrition and wellbeing feelings. Overall, this research illustrated that knowledge creation techniques can assist firms in the development of market-oriented health promoting foods for the ageing population.

A next application prediction service using the BaranC framework

Predicting user behaviour enables user assistant services provide personalized services to the users. This requires a comprehensive user model that can be created by monitoring user interactions and activities. BaranC is a framework that performs user interface (UI) monitoring (and collects all associated context data), builds a user model, and supports services that make use of the user model. A prediction service, Next-App, is built to demonstrate the use of the framework and to evaluate the usefulness of such a prediction service. Next-App analyses a user's data, learns patterns, makes a model for a user, and finally predicts, based on the user model and current context, what application(s) the user is likely to want to use. The prediction is pro-active and dynamic, reflecting the current context, and is also dynamic in that it responds to changes in the user model, as might occur over time as a user's habits change. Initial evaluation of Next-App indicates a high-level of satisfaction with the service.