Designing an adaptive salutogenic care environment

Humans are profoundly affected by the surroundings which they inhabit. Environmental psychologists have produced numerous credible theories describing optimal human environments, based on the concept of congruence or “fit” (1, 2). Lack of person/environment fit can lead to stress-related illness and lack of psychosocial well-being (3). Conversely, appropriately designed environments can promote wellness (4) or “salutogenesis” (5). Increasingly, research in the area of Evidence-Based Design, largely concentrated in the area of healthcare architecture, has tended to bear out these theories (6). Patients and long-term care residents, because of injury, illness or physical/ cognitive impairment, are less likely to be able to intervene to modify their immediate environment, unless this is designed specifically to facilitate their particular needs. In the context of care settings, detailed design of personal space therefore takes on enormous significance. MyRoom conceptualises a personalisable room, utilising sensoring and networked computing to enable the environment to respond directly and continuously to the occupant. Bio-signals collected and relayed to the system will actuate application(s) intended to positively influence user well-being. Drawing on the evidence base in relation to therapeutic design interventions (7), real-time changes in ambient lighting, colour, image, etc. respond continuously to the user’s physiological state, optimising congruence. Based on research evidence, consideration is also given to development of an application which uses natural images (8). It is envisaged that actuation will require machine-learning based on interpretation of data gathered by sensors; sensoring arrangements may vary depending on context and end-user. Such interventions aim to reduce inappropriate stress/ provide stimulation, supporting both instrumental and cognitive tasks.

Investigation into a best practice model for providing an integrated user experience with mobile cloud applications

Mobile Cloud Computing promises to overcome the physical limitations of mobile devices by executing demanding mobile applications on cloud infrastructure. In practice, implementing this paradigm is difficult; network disconnection often occurs, bandwidth may be limited, and a large power draw is required from the battery, resulting in a poor user experience. This thesis presents a mobile cloud middleware solution, Context Aware Mobile Cloud Services (CAMCS), which provides cloudbased services to mobile devices, in a disconnected fashion. An integrated user experience is delivered by designing for anticipated network disconnection, and low data transfer requirements. CAMCS achieves this by means of the Cloud Personal Assistant (CPA); each user of CAMCS is assigned their own CPA, which can complete user-assigned tasks, received as descriptions from the mobile device, by using existing cloud services. Service execution is personalised to the user's situation with contextual data, and task execution results are stored with the CPA until the user can connect with his/her mobile device to obtain the results. Requirements for an integrated user experience are outlined, along with the design and implementation of CAMCS. The operation of CAMCS and CPAs with cloud-based services is presented, specifically in terms of service description, discovery, and task execution. The use of contextual awareness to personalise service discovery and service consumption to the user's situation is also presented. Resource management by CAMCS is also studied, and compared with existing solutions. Additional application models that can be provided by CAMCS are also presented. Evaluation is performed with CAMCS deployed on the Amazon EC2 cloud. The resource usage of the CAMCS Client, running on Android-based mobile devices, is also evaluated. A user study with volunteers using CAMCS on their own mobile devices is also presented. Results show that CAMCS meets the requirements outlined for an integrated user experience.

Experimental analysis of novel telecom source materials and devices

Incumbent telecommunication lasers emitting at 1.5 µm are fabricated on InP substrates and consist of multiple strained quantum well layers of the ternary alloy InGaAs, with barriers of InGaAsP or InGaAlAs. These lasers have been seen to exhibit very strong temperature dependence of the threshold current. This strong temperature dependence leads to a situation where external cooling equipment is required to stabilise the optical output power of these lasers. This results in a significant increase in the energy bill associated with telecommunications, as well as a large increase in equipment budgets. If the exponential growth trend of end user bandwidth demand associated with the internet continues, these inefficient lasers could see the telecommunications industry become the dominant consumer of world energy. For this reason there is strong interest in developing new, much more efficient telecommunication lasers. One avenue being investigated is the development of quantum dot lasers on InP. The confinement experienced in these low dimensional structures leads to a strong perturbation of the density of states at the band edge, and has been predicted to result in reduced temperature dependence of the threshold current in these devices. The growth of these structures is difficult due to the large lattice mismatch between InP and InAs; however, recently quantum dots elongated in one dimension, known as quantum dashes, have been demonstrated. Chapter 4 of this thesis provides an experimental analysis of one of these quantum dash lasers emitting at 1.5 µm along with a numerical investigation of threshold dynamics present in this device. Another avenue being explored to increase the efficiency of telecommunications lasers is bandstructure engineering of GaAs-based materials to emit at 1.5 µm. The cause of the strong temperature sensitivity in InP-based quantum well structures has been shown to be CHSH Auger recombination. Calculations have shown and experiments have verified that the addition of bismuth to GaAs strongly reduces the bandgap and increases the spin orbit splitting energy of the alloy GaAs1−xBix. This leads to a bandstructure condition at x = 10 % where not only is 1.5 µm emission achieved on GaAs-based material, but also the bandstructure of the material can naturally suppress the costly CHSH Auger recombination which plagues InP-based quantum-well-based material. It has been predicted that telecommunications lasers based on this material system should operate in the absence of external cooling equipment and offer electrical and optical benefits over the incumbent lasers. Chapters 5, 6, and 7 provide a first analysis of several aspects of this material system relevant to the development of high bismuth content telecommunication lasers.

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.

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.

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 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.