MIMS: Mobile Interruption Management System

Mobile devices are increasing in an astronomical rate throughout the world. While it is bringing a lot of comfort to the users it is not coming without any hazards. Now a user is susceptible to mobile call interruptions wherever he is, whether he is in the middle of a very important discussion or in a very important task like performing a complicated emergency operation in a hospital. As a result researchers have been studying to find ways to minimize cost of mobile interruptions. In this thesis we have proposed a mobile interruption management system in which callers have been grouped and time intervals of a day have been classified to ascertain whether a call should be allowed to ring, go silent or vibrate. We have also included presence of Bluetooth devices and applications the mobile user is using to decide if the user needs to be interrupted. We have undertaken a survey of mobile users to compare various models and select appropriate default parameter values like caller groups, time intervals, and inclusion of special days/events. We have proposed a matrix containing default values of cost of mobile interruption, which will be adjusted according to contexts the user is in and then this cost is compared with the threshold value. If the cost of receiving the call is less than the threshold value, then device sound profile is set to ring or vibrate otherwise it is set to silent. We have also evaluated our model with existing models and found the system performing well

Improving availability awareness with relationship filtering

Awareness servers provide information about a person to help observers determine whether a person is available for contact. A trade -off exists in these systems: more sources of information, and higher fidelity in those sources, can improve people’s decisions, but each increase in information reduces privacy. In this thesis, we look at whether the type of relationship between the observer and the person being observed can be used to manage this trade-off. We conducted a survey that asked people what amount of information from different sources that they would disclose to seven different relationship types. We found that in more than half of the cases, people would give different amounts of information to different relationships. We then constructed a prototype system and conducted a Wizard of Oz experiment where we took the system into the real world and observed individuals using it. Our results suggest that awareness servers can be improved by allowing finer-grained control than what is currently available

Modeling Cost of Interruption (COI) to Manage Unwanted Interruptions for Mobile Devices

Unwanted and untimely interruptions have been a major cause in the loss of productivity in recent years. It has been found that they are mostly detrimental to the immediate task at hand. Multiple approaches have been proposed to address the problem of interruption by calculating cost of it. The Cost Of Interruption (COI) gives a measure of the probabilistic value of harmfulness of an inopportune interruption. Bayesian Inference stands as the premier model so far to calculate this COI. However, Bayesian-based models suffer from not being able to model context accurately in situations where a priori, conditional probabilities and uncertainties exist while utilizing context information. Hence, this thesis introduces the Dempster-Shafer Theory of Evidence to model COI. Along the way, it identifies specific contexts that are necessary to take into account. Simulation results and performance evaluation suggest that this is a very good approach to decision making. The thesis also discusses an illustrative example of a mobile interruption management application where the Dempster-Shafer theory is used to get a better measurement of whether or not to interrupt

Augmented Reality and Context Awareness for Mobile Learning Systems

Learning is one of the most interactive processes that humans practice. The level of interaction between the instructor and his or her audience has the greatest effect on the output of the learning process. Recent years have witnessed the introduction of e-learning (electronic learning), which was then followed by m-learning (mobile learning). While researchers have studied e-learning and m-learning to devise a framework that can be followed to provide the best possible output of the learning process, m-learning is still being studied in the shadow of e-learning. Such an approach might be valid to a limited extent, since both aims to provide educational material over electronic channels. However, m-learning has more space for user interaction because of the nature of the devices and their capabilities. The objective of this work is to devise a framework that utilises augmented reality and context awareness in m-learning systems to increase their level of interaction and, hence, their usability. The proposed framework was implemented and deployed over an iPhone device. The implementation focused on a specific course. Its material represented the use of augmented reality and the flow of the material utilised context awareness. Furthermore, a software prototype application for smart phones, to assess usability issues of m-learning applications, was designed and implemented. This prototype application was developed using the Java language and the Android software development kit, so that the recommended guidelines of the proposed framework were maintained. A questionnaire survey was conducted at the University, with approximately twenty-four undergraduate computer science students. Twenty-four identical smart phones were used to evaluate the developed prototype, in terms of ease of use, ease of navigating the application content, user satisfaction, attractiveness and learnability. Several validation tests were conducted on the proposed augmented reality m-learning verses m-learning. Generally, the respondents rated m-learning with augmented reality as superior to m-learning alone

Human centric situational awareness

Context awareness is an approach that has been receiving increasing focus in the past years. A context aware device can understand surrounding conditions and adapt its behavior accordingly to meet user demands. Mobile handheld devices offer a motivating platform for context aware applications as a result of their rapidly growing set of features and sensing abilities. This research aims at building a situational awareness model that utilizes multimodal sensor data provided through the various sensing capabilities available on a wide range of current handheld smart phones. The model will make use of seven different virtual and physical sensors commonly available on mobile devices, to gather a large set of parameters that identify the occurrence of a situation for one of five predefined context scenarios: In meeting, Driving, in party, In Theatre and Sleeping. As means of gathering the wisdom of the crowd and in an effort to reach a habitat sensitive awareness model, a survey was conducted to understand the user perception of each context situation. The data collected was used to build the inference engine of a prototype context awareness system utilizing context weights introduced in [39] and the confidence metric in [26] with some variation as a means for reasoning. The developed prototype\u27s results were benchmarked against two existing context awareness platforms Darwin Phones [17] and Smart Profile [11], where the prototype was able to acquire 5% and 7.6% higher accuracy levels than the two systems respectively while performing tasks of higher complexity. The detailed results and evaluation are highlighted further in section 6.4

Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts

A user centred approach to the modelling of contextualised experience adaptation in relation to video consumption

This research focused on the development of a user centric framework for the interpretation of contextualised TV and video viewing experiences (UX). Methods to address content overload and provide better contextualisation when consuming video have been an area of academic discussion for almost 20 years (Burke, Felfernig, & Goker, 2011). However over the same period technical system design for video has actually moved away from attempts to model the nature of real viewing contexts. With now near ubiquitous access to video from a range of disparate devices the addition of contextualisation within video applications and devices represents an opportunity in terms of improving viewer UX. Three user studies were carried out to inform development of the framework and employed mixed method approaches. The first focused on understanding where video is watched and the contextual factors that defined those places as viewing situations. This study derived eight Archetype viewing situations and associated contextual cues. The second study measured viewing UX in context. Significant differences in subjective ratings for measured UX were found when viewing was compared within subjects across Viewing Archetype situations. A third study characterised viewing UX, identifying behavioural, environmental and technological factors which through observed frequency and duration were identified as indicative enablers and detractors in the creation of viewing UX. Concepts generated within the studies that related to viewing context identification and viewing UX classification through experiential factors were integrated into the framework. The framework provides a way through which to identify, describe and improve viewing UX across contexts. Additionally the framework was referenced to develop an exemplar system model for contextual adaptation in order to show its relevance to the generation of technical system design. Finally information for designers was created in the form of scenarios and suggestions for use in order to bring the framework to life as a resource for development teams

Developing Unobtrusive Mobile Interactions: a Model Driven Engineering approach

In Ubiquitous computing environments, people are surrounded by a lot of embedded services. With the inclusion of pervasive technologies such as sensors or GPS receivers, mobile devices turn into an effective communication tool between users and the services embedded in their environment. All these services compete for the attentional resources of the user. Thus, it is essential to consider the degree in which each service intrudes the user mind when services are designed. In order to prevent service behavior from becoming overwhelming, this work, based on Model Driven Engineering foundations, is devoted to develop services according to user needs. In this thesis, we provide a systematic method for the development of mobile services that can be adapted in terms of obtrusiveness. That is, services can be developed to provide their functionality at different obtrusiveness levels by minimizing the duplication of efforts. For the system specification, a modeling language is defined to cope with the particular requirements of the context-aware user interface domain. From this specification, following a sequence of well-defined steps, a software solution is obtained.Gil Pascual, M. (2010). Developing Unobtrusive Mobile Interactions: a Model Driven Engineering approach. http://hdl.handle.net/10251/12745Archivo delegad

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

Mediating ICU patient situation-awareness with visual and tactile notifications

Indiana University-Purdue University Indianapolis (IUPUI)Healthcare providers in hospital intensive care units (ICUs) maintain patient situation awareness by following task management and communication practices. They create and manipulate several paper-based and digital information sources, with the overall aim to constantly inform themselves and their colleagues of dynamically evolving patient conditions. However, when increased communication means that healthcare providers potentially interrupt each other, enhanced patient-situation awareness comes at a price. Prior research discusses both the use of technology to support increased communication and its unintended consequence of (wanted and unwanted) notification interruptions. Using qualitative research techniques, I investigated work practices that enhance the patient-situation awareness of physicians, fellows, residents, nurses, students, and pharmacists in a medical ICU. I used the Locales Framework to understand the observed task management and communication work practices. In this study, paper notes were observed to act as transitional artifacts that are later digitized to organize and coordinate tasks, goals, and patient-centric information at a team and organizational level. Non digital information is often not immediately digitized, and only select information is communicated between certain ICU team members through synchronous mechanisms such as face-to-face or telephone conversations. Thus, although ICU providers are exceptionally skilled at working together to improve a critically ill patient’s condition, the use of paper-based artifacts and synchronous communication mechanisms induces several interruptions while contextually situating a clinical team for patient care. In this dissertation, I also designed and evaluated a mobile health technology tool, known as PANI (Patient-centered Notes and Information Manager), guided by the Locales framework and the participatory involvement of ICU healthcare providers as co designers. PANI-supported task management induces minimal interruptions by: (1) rapidly generating, managing, and sharing clinical notes and action-items among clinicians and (2) supporting the collaboration and communication needs of clinicians through a novel visual and tactile notification system. The long-term contribution of this research suggests guidelines for designing mobile health technology interventions that enhance ICU patient situation-awareness and reduce unwanted interruptions to clinical workflow