In ubiquitous environments, users are exposed to public spaces and places where they are supposed to interact and provide also private information. In order to enhance user acceptance of such ubiquitous appliances they have to be designed to consider trust and trustworthiness already in the design phase. We focus on regarding trust in early phases and provide tools for designers by describing trust issues through patterns which are made available through design repositories. Such patterns help designers of ubiquitous applications to create designs quicker based on the availability of already proven solutions they can rely on.

Ubiquitous computing (Ubicomp) increases in proliferation. Multiple and ever growing in numbers, computational devices are now at the users' disposal throughout the physical environment, while simultaneously being effectively invisible. Consequently, a significant challenge is service discovery. Services may for instance be physical, such as printing a document, or virtual, such as communicating information. The existing solutions, such as Bluetooth and UPnP, address part of the issue, specifically low-level physical interconnectivity. Still absent are solutions for high-level challenges, such as connecting users with appropriate services. In order to provide appropriate service offerings, service discovery in Ubicomp must take the users' context, tasks, goals, intentions, and available resources into consideration. It is possible to divide the high-level service-discovery issue into two parts; inadequate service models, and insufficient common-sense models of human activities. This thesis contributes to service discovery in Ubicomp, by arguing that in order to meet these high-level challenges, a new layer is required. Furthermore, the thesis presents a prototype implementation of this new service-discovery architecture and model. The architecture consists of hardware, ontology-layer, and common-sense-layer. This work addresses the ontology and common-sense layers. Subsequently, implementation is divided into two parts; Oden and Magubi. Oden addresses the issue of inadequate service models through a combination of service-ontologies in concert with logical reasoning engines, and Magubi addresses the issue of insufficient common-sense models of human activities, by using common sense models in combination with rule engines. The synthesis of these two stages enables the system to reason about services, devices, and user expectations, as well as to make suitable connections to satisfy the users' overall goal. Designing common-sense models and service ontologies for a Ubicomp environment is a non-trivial task. Despite this, we believe that if correctly done, it might be possible to reuse at least part of the knowledge in different situations. With the ability to reason about services and human activities it is possible to decide if, how, and where to present the services to the users. The solution is intended to off-load users in diverse Ubicomp environments as well as provide a more relevant service discovery. / <p>Report code: LiU-Tek-Lic-2007:14.</p>

Co-design är en systemutvecklingsmetod som går ut på att potentiella slutanvändare får vara med under designprocessen och designa produkter i samråd med övriga intressenter, exempelvis designers och utvecklare. En utmaning för dagens IT-utveckling är att den ökade digitaliseringen i vår vardag skapar nya kontexter av användningsområden där designarbeten måste anpassas för att kunna genomföras. Undersökningens syfte har varit att ta reda på hur man kan anpassa designtekniker och interaktiva verktyg för att kunna genomföra co-design på distans i hemmiljö. För att kunna svara på frågeställningen konstruerades en iterativ genomförandeprocess bestående av 3 digitala sessioner där 2 designers och 4 rekryterade co-designers träffades i grupp under 3 tillfällen. I genomförandet användes skisser, prototyper och scenarion som grund för att konstruera en mobil applikation för ett framtida smartkök. Undersökningen visade att distansutförande kan påverka tidsåtgången negativt för en del arbetsmoment och att det i vissa fall skulle vara fördelaktigt att i stället anordna fysiska träffar. Några av de svårigheter som identifierades var att digitala kommunikationsverktyg kan begränsa deltagarna och hindra dem från att yttra sina tankar och åsikter. Trots detta visar resultaten på att co-design på distans kan främja ömsesidigt lärande och att det är möjligt att anpassa både designtekniker och interaktiva verktyg för att fungera i olika miljöer. Distansutförande har fördelen att det möjliggör åtkomst till användningsmiljöer som vanligtvis skulle vara svåråtkomliga, däribland slutanvändarens egna hem.

Although sensor planning in computer vision has been a subject of research for over two decades, a vast majority of the research seems to concentrate on two particular applications in a rather limited context of laboratory and industrial workbenches, namely 3D object reconstruction and robotic arm manipulation. Recently, increasing interest is engaged in research to come up with solutions that provide wide-area autonomous surveillance systems for object characterization and situation awareness, which involves portable, wireless, and/or Internet connected radar, digital video, and/or infrared sensors. The prominent research problems associated with multisensor integration for wide-area surveillance are modality selection, sensor planning, data fusion, and data exchange (communication) among multiple sensors. Thus, the requirements and constraints to be addressed include far-field view, wide coverage, high resolution, cooperative sensors, adaptive sensing modalities, dynamic objects, and uncontrolled environments. This article summarizes a new survey and analysis conducted in light of these challenging requirements and constraints. It involves techniques and strategies from work done in the areas of sensor fusion, sensor networks, smart sensing, Geographic Information Systems (GIS), photogrammetry, and other intelligent systems where finding optimal solutions to the placement and deployment of multimodal sensors covering a wide area is important. While techniques covered in this survey are applicable to many wide-area environments such as traffic monitoring, airport terminal surveillance, parking lot surveillance, etc., our examples will be drawn mainly from such applications as harbor security and long-range face recognition.

With the proliferation of mobile applications and the abundance of wireless devices, it is increasingly common for devices to support multiple radios. When two devices are communicating they should choose the best available radio based on user preference and application requirements. This type of “radio switching” should happen automatically, so that the system optimizes performance dynamically. To achieve this objective, we design an Autonomous and Intelligent Radio Switching (AIRS) system to leverage the radio heterogeneity common in today's wireless devices. The AIRS system consists of three key components. First, we design a radio preference evaluation module to dynamically select the best radio according to users' preference, application's QoS requirements, and the device battery usage. Second, we propose a link quality measurement and prediction module to predict the radio quality under a variety of mobility and interference conditions. Third, we present a radio switching decision making module to switch to the preferred available radio intelligently, based on the preference and link quality evaluations. The AIRS system maintains connectivity, as well as improves link quality, via dynamic and intelligent radio switching, regardless of interference or collisions from the interfaces of other devices. The radio preference evaluation module is able to generate and adjust a preference list dynamically. Multiple users' requirements are satisfied in a mutually beneficial manner and the selected radio is Pareto optimal. The link prediction module is able to achieve an accuracy above 90% under a variety of mobility and interference conditions. The module can dynamically increase the link measurement interval and significantly reduce its power consumption, without sacrificing accuracy. The decision algorithm uses several parameters to avoid switching radios too frequently, and is able to provide dynamic, but stable radio switching, while balancing the competing objectives of high throughput and low power consumption. Overall, the AIRS system is able to achieve high goodput (application level throughput) and long battery life as applied to handoff management in a frequently changing mobile environment.

Machine learning has become one of the most emerging topics in a lot of research areas, such as pervasive and ubiquitous computing. Such computing applications always rely on the supervised learning approach to recognize user’s context before a suitable level of services are provided. However, since more and more users are involved in modern applications, the monitored data cannot be guaranteed to be always true due to wrong information. This may cause the mislabeling in machine learning and so affects the prediction. The goal of this Ph.D. thesis is to improve the data quality and solve the mislabeling problem caused by considering non-expert users. To achieve this goal, we propose a novel algorithm, called Skeptical Learning, aiming at interacting with the users and filtering out anomalies when an invalid input is monitored. This algorithm guarantees the machine to use the pre-known knowledge to check the availability of its own prediction as well as the label provided by the users. This thesis clarifies how we design this algorithm and makes three main contributions: (i.) we study the predictability of human behavior through the notion of personal context; (ii.)we design and develop Skeptical Learning as a paradigm to deal with the unreliability of users when providing non-confidential labels that describe their personal context; (iii.) we introduce an MCS platform where we implement Skeptical Learning on top of it to solve unreliable labels issue. Our evaluations have shown that Skeptical Learning could be widely used in pervasive and ubiquitous computing applications to better understand the quality of the data relying on the machine knowledge, and thus prevent mislabeling problem due to non-expert information.

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[pt] Nesta dissertação são investigadas formas de áudio biofeedback (ABF) para exercícios de estabilização segmentar usando sensores de movimento. A estabilização segmentar é uma das estratégias no tratamento de lombalgias. O exercício de Ponte foi escolhido como foco para a investigação. O sensor de movimento principal foi um acelerômetro tri-axial. Flex Sensors, Force Sensitive Resistor e múltiplos acelerômetros foram usados em outros protótipos. Os resultados desta dissertação, que incluem dados do acelerômetro, comentários, procedimentos, reflexões e implementação de protótipos com geração de 3 tipos de ABF, foram obtidos durante 5 ciclos de uma pesquisa-ação. Na pesquisa-ação, o pesquisador conduz a pesquisa realizando ações sucessivas que busquem reduzir um problema específico em um ambiente real. Nesta dissertação, o ambiente usado foi um local onde um paciente executa exercícios de estabilização segmentar e o problema identificado é a dificuldade do paciente executar os exercícios de forma correta. A ação é a disponibilização de um wearable – StableBelt – que produz ABF baseado nos movimentos do paciente durante um exercício de estabilização segmentar. Diferentes formas de áudio foram investigadas: música instrumental, piano e percussão. O StableBelt foi avaliado através de 3 testes com usuários. Após um teste preliminar com um participante, testes com 5 e depois 8 participantes foram feitos. No teste preliminar foi usada música instrumental e piano e percussão nos testes subsequentes. O último ciclo da pesquisa-ação foi dedicado ao conforto do StableBelt. Durante a investigação, foram entrevistados fisioterapeutas que pesquisam lombalgia e fisioterapeutas que usam estabilização segmentar na clínica. / [en] In this dissertation, different types of audio biofeedback (ABF) for core stabilization exercises using motion sensors are investigated. Core stabilization exercises are one of the strategies used in the treatment of low back pain. The Supine Bridge (SB) exercise was chosen as the focus for the investigation. The primary motion sensor used was a tri-axial accelerometer. Flex Sensors, Force Sensitive Resistors and multiple accelerometers were also used in other prototypes. The results of this dissertation, which include data from accelerometer, comments, process, reflections, and implementation of prototypes that generate 3 types of audio biofeedback, were gathered during 5 cycles of action research. In action research, the researcher conducts the research performing successive actions that attempt to reduce a specific problem in a real world environment. In this dissertation, the environment chosen was a place where a patient executes exercises and the problem identified is the difficulty of the patient to perform the exercises correctly. The action was the introduction of a wearable – StableBelt – which generates audio biofeedback based on the patient s movements during a core stabilization exercise. Different types of audio were investigated: instrumental music, piano and drums. The StableBelt was evaluated through 3 user tests. After a preliminary test with one participant, user tests with 5 and 8 participants were conducted. In the preliminary test, instrumental music was used and piano and drums in later tests. The last cycle of the action research was dedicated to the comfort of the StableBelt. During the investigation, physical therapists which research low back pain and physical therapists which use core stabilization exercises in their clinical practice were interviewed.

The aim of this thesis is to compare two different interface approaches for pervasive gameswith a focus on immersion. We designed and created two small pervasive games and implemented bothon two different platforms; smartphone and wearable device. We created four pervasive gameprototypes which we tested with a group of fourteen testers. We subsequently conducted interviewsusing the Repertory Grid Technique. The findings suggest that our testers appreciated wearable devicesmore than smartphones in the context of immersion, while they identified characteristics theyassociated with each platform; Smartphones were considered familiar, inconspicuous, casual but notvery exciting. Wearables were considered a new experience and fun but also strange and attention-drawing.