MyRoom: A user-centred model of affective responsive architecture

Can my immediate physical environment affect how I feel? The instinctive answer to this question must be a resounding “yes”. What might seem a throwaway remark is increasingly borne out by research in environmental and behavioural psychology, and in the more recent discipline of Evidence-Based Design. Research outcomes are beginning to converge with findings in neuroscience and neurophysiology, as we discover more about how the human brain and body functions, and reacts to environmental stimuli. What we see, hear, touch, and sense affects each of us psychologically and, by extension, physically, on a continual basis. The physical characteristics of our daily environment thus have the capacity to profoundly affect all aspects of our functioning, from biological systems to cognitive ability. This has long been understood on an intuitive basis, and utilised on a more conscious basis by architects and other designers. Recent research in evidence-based design, coupled with advances in neurophysiology, confirm what have been previously held as commonalities, but also illuminate an almost frightening potential to do enormous good, or alternatively, terrible harm, by virtue of how we make our everyday surroundings. The thesis adopts a design methodology in its approach to exploring the potential use of wireless sensor networks in environments for elderly people. Vitruvian principles of “commodity, firmness and delight” inform the research process and become embedded in the final design proposals and research conclusions. The issue of person-environment fit becomes a key principle in describing a model of continuously-evolving responsive architecture which makes the individual user its focus, with the intention of promoting wellbeing. The key research questions are: What are the key system characteristics of an adaptive therapeutic single-room environment? How can embedded technologies be utilised to maximise the adaptive and therapeutic aspects of the personal life-space of an elderly person with dementia?.

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.

A service-oriented user interaction analysis framework supporting adaptive applications

A comprehensive user model, built by monitoring a user's current use of applications, can be an excellent starting point for building adaptive user-centred applications. The BaranC framework monitors all user interaction with a digital device (e.g. smartphone), and also collects all available context data (such as from sensors in the digital device itself, in a smart watch, or in smart appliances) in order to build a full model of user application behaviour. The model built from the collected data, called the UDI (User Digital Imprint), is further augmented by analysis services, for example, a service to produce activity profiles from smartphone sensor data. The enhanced UDI model can then be the basis for building an appropriate adaptive application that is user-centred as it is based on an individual user model. As BaranC supports continuous user monitoring, an application can be dynamically adaptive in real-time to the current context (e.g. time, location or activity). Furthermore, since BaranC is continuously augmenting the user model with more monitored data, over time the user model changes, and the adaptive application can adapt gradually over time to changing user behaviour patterns. BaranC has been implemented as a service-oriented framework where the collection of data for the UDI and all sharing of the UDI data are kept strictly under the user's control. In addition, being service-oriented allows (with the user's permission) its monitoring and analysis services to be easily used by 3rd parties in order to provide 3rd party adaptive assistant services. An example 3rd party service demonstrator, built on top of BaranC, proactively assists a user by dynamic predication, based on the current context, what apps and contacts the user is likely to need. BaranC introduces an innovative user-controlled unified service model of monitoring and use of personal digital activity data in order to provide adaptive user-centred applications. This aims to improve on the current situation where the diversity of adaptive applications results in a proliferation of applications monitoring and using personal data, resulting in a lack of clarity, a dispersal of data, and a diminution of user control.

Practice-based personas: integrated user and practice models

Practices are routinised behaviours with social and material components and complex relationships over space and time. Practice-based design goes beyond interaction design to consider how these components and their relationships impact on the formation and enactment of a practice, where technology is just one part of the practice. Though situated user-centred design methods such as participatory design are employed for the design of practice, demand exists for additional methods and tools in this area. This paper introduces practice-based personas as an extension of the persona approach popular in interaction design, and demonstrates how a set of practice-based personas was developed for a given domain – academic practice. The three practice-based personas developed here are linked to a catalogue of forty practices, offering designers both a user perspective and a practice perspective when designing for the domain.

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.

Mobile cloud contextual awareness with the cloud personal assistant

This paper presents our efforts to bridge the gap between mobile context awareness, and mobile cloud services, using the Cloud Personal Assistant (CPA). The CPA is a part of the Context Aware Mobile Cloud Services (CAMCS) middleware, which we continue to develop. Specifically, we discuss the development and evaluation of the Context Processor component of this middleware. This component collects context data from the mobile devices of users, which is then provided to the CPA of each user, for use with mobile cloud services. We discuss the architecture and implementation of the Context Processor, followed by the evaluation. We introduce context profiles for the CPA, which influence its operation by using different context types. As part of the evaluation, we present two experimental context-aware mobile cloud services to illustrate how the CPA works with user context, and related context profiles, to complete tasks for the user.

A pro-active and dynamic prediction assistance using BaranC framework

Monitoring user interaction activities provides the basis for creating a user model that can be used to predict user behaviour and enable user assistant services. The BaranC framework provides components that perform UI monitoring (and collect all associated context data), builds a user model, and supports services that make use of the user model. In this case study, a Next-App prediction service 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; it is dynamic both in responding to the current context, and also 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.

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.

Delivering mobile cloud services to the user: description, discovery, and consumption

The mobile cloud computing paradigm can offer relevant and useful services to the users of smart mobile devices. Such public services already exist on the web and in cloud deployments, by implementing common web service standards. However, these services are described by mark-up languages, such as XML, that cannot be comprehended by non-specialists. Furthermore, the lack of common interfaces for related services makes discovery and consumption difficult for both users and software. The problem of service description, discovery, and consumption for the mobile cloud must be addressed to allow users to benefit from these services on mobile devices. This paper introduces our work on a mobile cloud service discovery solution, which is utilised by our mobile cloud middleware, Context Aware Mobile Cloud Services (CAMCS). The aim of our approach is to remove complex mark-up languages from the description and discovery process. By means of the Cloud Personal Assistant (CPA) assigned to each user of CAMCS, relevant mobile cloud services can be discovered and consumed easily by the end user from the mobile device. We present the discovery process, the architecture of our own service registry, and service description structure. CAMCS allows services to be used from the mobile device through a user's CPA, by means of user defined tasks. We present the task model of the CPA enabled by our solution, including automatic tasks, which can perform work for the user without an explicit request.

The impact of user charges on patient choice of healthcare services in Ireland

This research assesses the impact of user charges in the context of consumer choice to ascertain how user charges in healthcare impact on patient behaviour in Ireland. Quantitative data is collected from a subset of the population in walk-in Urgent Care Clinics and General Practitioner surgeries to assess their responses to user charges and whether user charges are a viable source of part-funding healthcare in Ireland. Examining the economic theories of Becker (1965) and Grossman (1972), the research has assessed the impact of user charges on patient choice in terms of affordability and accessibility in healthcare. The research examined a number of private, public and part-publicly funded healthcare services in Ireland for which varying levels of user charges exist depending on patients’ healthcare cover. Firstly, the study identifies the factors affecting patient choice of privately funded walk-in Urgent Care Clinics in Ireland given user charges. Secondly, the study assesses patient response to user charges for a mainly public or part-publicly provided service; prescription drugs. Finally, the study examines patients’ attitudes towards the potential application of user charges for both public and private healthcare services when patient choice is part of a time-money trade-off, convenience choice or preference choice. These services are valued in the context of user charges becoming more prevalent in healthcare systems over time. The results indicate that the impact of user charges on healthcare services vary according to socio-economic status. The study shows that user charges can disproportionately affect lower income groups and consequently lead to affordability and accessibility issues. However, when valuing the potential application of user charges for three healthcare services (MRI scans, blood tests and a branded over a generic prescription drug), this research indicates that lower income individuals are willing to pay for healthcare services, albeit at a lower user charge than higher income earners. Consequently, this study suggests that user charges may be a feasible source of part-financing Irish healthcare, once the user charge is determined from the patients’ perspective, taking into account their ability to pay.

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.

Time-limited home-care reablement services for maintaining and improving the functional independence of older adults

Background: Reablement, also known as restorative care, is one possible approach to home-care services for older adults at risk of functional decline. Unlike traditional home-care services, reablement is frequently time-limited (usually six to 12 weeks) and aims to maximise independence by offering an intensive multidisciplinary, person-centred and goal-directed intervention. Objectives: To assess the effects of time-limited home-care reablement services (up to 12 weeks) for maintaining and improving the functional independence of older adults (aged 65 years or more) when compared to usual home-care or wait-list control group. Search methods: We searched the following databases with no language restrictions during April to June 2015: the Cochrane Central Register of Controlled Trials (CENTRAL); MEDLINE (OvidSP); Embase (OvidSP); PsycINFO (OvidSP); ERIC; Sociological Abstracts; ProQuest Dissertations and Theses; CINAHL (EBSCOhost); SIGLE (OpenGrey); AgeLine and Social Care Online. We also searched the reference lists of relevant studies and reviews as well as contacting authors in the field. Selection criteria: We included randomised controlled trials (RCTs), cluster randomised or quasi-randomised trials of time-limited reablement services for older adults (aged 65 years or more) delivered in their home; and incorporated a usual home-care or wait-list control group. Data collection and analysis: Two authors independently assessed studies for inclusion, extracted data, assessed the risk of bias of individual studies and considered quality of the evidence using GRADE. We contacted study authors for additional information where needed. Main results: Two studies, comparing reablement with usual home-care services with 811 participants, met our eligibility criteria for inclusion; we also identified three potentially eligible studies, but findings were not yet available. One included study was conducted in Western Australia with 750 participants (mean age 82.29 years). The second study was conducted in Norway (61 participants; mean age 79 years). We are very uncertain as to the effects of reablement compared with usual care as the evidence was of very low quality for all of the outcomes reported. The main findings were as follows. Functional status: very low quality evidence suggested that reablement may be slightly more effective than usual care in improving function at nine to 12 months (lower scores reflect greater independence; standardised mean difference (SMD) -0.30; 95% confidence interval (CI) -0.53 to -0.06; 2 studies with 249 participants). Adverse events: reablement may make little or no difference to mortality at 12 months' follow-up (RR 0.97; 95% CI 0.74 to 1.29; 2 studies with 811 participants) or rates of unplanned hospital admission at 24 months (RR 0.94; 95% CI 0.85 to 1.03; 1 study with 750 participants). The very low quality evidence also means we are uncertain whether reablement may influence quality of life (SMD -0.23; 95% CI -0.48 to 0.02; 2 trials with 249 participants) or living arrangements (RR 0.92, 95% CI 0.62 to 1.34; 1 study with 750 participants) at time points up to 12 months. People receiving reablement may be slightly less likely to have been approved for a higher level of personal care than people receiving usual care over the 24 months' follow-up (RR 0.87; 95% CI 0.77 to 0.98; 1 trial, 750 participants). Similarly, although there may be a small reduction in total aggregated home and healthcare costs over the 24-month follow-up (reablement: AUD 19,888; usual care: AUD 22,757; 1 trial with 750 participants), we are uncertain about the size and importance of these effects as the results were based on very low quality evidence. Neither study reported user satisfaction with the service. Authors' conclusions: There is considerable uncertainty regarding the effects of reablement as the evidence was of very low quality according to our GRADE ratings. Therefore, the effectiveness of reablement services cannot be supported or refuted until more robust evidence becomes available. There is an urgent need for high quality trials across different health and social care systems due to the increasingly high profile of reablement services in policy and practice in several countries.

A probabilistic approach to user mobility prediction for wireless services.

Mobile and wireless networks have long exploited mobility predictions, focused on predicting the future location of given users, to perform more efficient network resource management. In this paper, we present a new approach in which we provide predictions as a probability distribution of the likelihood of moving to a set of future locations. This approach provides wireless services a greater amount of knowledge and enables them to perform more effectively. We present a framework for the evaluation of this new type of predictor, and develop 2 new predictors, HEM and G-Stat. We evaluate our predictors accuracy in predicting future cells for mobile users, using two large geolocation data sets, from MDC [11], [12] and Crawdad [13]. We show that our predictors can successfully predict with as low as an average 2.2% inaccuracy in certain scenarios.

The cloud personal assistant for providing services to mobile clients

This paper introduces the original concept of a cloud personal assistant, a cloud service that manages the access of mobile clients to cloud services. The cloud personal assistant works in the cloud on behalf of its owner: it discovers services, invokes them, stores the results and history, and delivers the results to the mobile user immediately or when the user requests them. Preliminary experimental results that demonstrate the concept are included.

User experience of mobile cloud applications - current state and future directions

The increasing penetration rate of feature rich mobile devices such as smartphones and tablets in the global population has resulted in a large number of applications and services being created or modified to support mobile devices. Mobile cloud computing is a proposed paradigm to address the resource scarcity of mobile devices in the face of demand for more computing intensive tasks. Several approaches have been proposed to confront the challenges of mobile cloud computing, but none has used the user experience as the primary focus point. In this paper we evaluate these approaches in respect of the user experience, propose what future research directions in this area require to provide for this crucial aspect, and introduce our own solution.