Complex diseases such as cancer, cardiovascular diseases and diabetes are often diagnosed too late, which significantly impairs treatment options and, in turn, lowers patient’s survival rate drastically and increases the costs significantly. Moreover, the growth of medical data is faster than the ability of healthcare systems to utilize them. Almost 80% of medical data are unstructured, but they are clinically relevant. On the other hand, technological advancements have made it possible to create different  igital health solutions where healthcare and ICT meet. Also, some individuals have already started to measure their body function parameters, track their health status, research their symptoms and even intervene in treatment options which means a great deal of data is being produced and also indicates that patient-driven health care models are transforming how health care functions. These models include quantified self-tracking, consumer-personalized-medicine and health social networks. This research aims to present an open innovation digital health platform which creates value  y using the overlaps between healthcare, information technology and artificial intelligence. This platform could potentially be utilized for early detection of complex diseases by leveraging Big Data technology which could improve awareness by recognizing pooled symptoms of a specific disease. This would enable individuals to effortlessly and quantitatively track and become aware of changes in their health, and through a dialog with a doctor, achieve diagnosis at a significantly earlier stage. This thesis focuses on a case study of the platform for detecting breast cancer at a  ignificantly earlier stage. A qualitative research method is implemented through reviewing the literature, determining the knowledge gap, evaluating the need, performing market research, developing a conceptual prototype and presenting the open innovation platform. Finally, the value creation, applications and challenges of such platform are investigated, analysed and discussed based on the collected data from interviews and surveys. This study combines an explanatory and an analytical research approach, as it aims not only to describe the case, but also to explain the value creation for different stakeholders in the value chain. The findings indicate that there is an urgent need for early diagnosis of complex diseases such as breast cancer) and also handling direct and indirect consequences of late diagnosis. A significant outcome of this research is the conceptual prototype which was developed based on the general proposed concept through a customer development process. According to the conducted surveys, 95% of the cancer patients and 84% of the healthy individuals are willing to use the proposed platform. The results indicate that it can create significant values for patients, doctors, academic institutions, hospitals and even healthy individuals.

I Abstract (in English): Diploma thesis Visual lifelogging: automatic memories and picturing ordinary deals with the topic of visual lifelogging in the context of current visual culture. It describes visual lifelogging in the context of photography and as an amplification of human abilities. Diploma thesis deals with history and lifelogging as a whole and it describes its forms, describes the tools of lifelogging and it deals also with topics of surveillance and its contrary, sousveillance. It explores visual lifelogging mostly from the photographic point of view and it deals with the use of different technologies to augment human memory or other human abilities.

This thesis is examining in which way the quantified self approach can have the effect on motivation in the process of education. The aim of this thesis is to determine whether the usage of application which is tracking and recording the study progress can have the influence on motivation of users and moreover, to evaluate the most effective motivational techniques used in these kinds of applications. Firstly, terms like motivation, self-regulated learning and the Quantified Self movement are clarified. Moreover, some of the researches from the field of tracking of educational and self-educational progress are described. Furthermore, various of motivational techniques are examined. In the part of the research, by the in-depth interviews it is examined the influence of the application Todait on the motivation of users and by the quantitative survey of questionnaire it is inquired what other functions would users prefer.

Cette thèse sur travaux explore l’adoption des technologies et des pratiques de quantification de soi avec un positionnement critique. Il s’agit de mieux comprendre l’expérience des utilisateurs avec les technologies de quantification de soi, telles que les bracelets connectés, et de questionner la contribution réelle de ces technologies au bien-être et à la santé des individus. Le premier essai présente une revue de la littérature systématique sur la quantification de soi et un agenda de recherche pour les chercheurs en management. Le deuxième essai est une étude qualitative qui révèle les relations de pouvoir qu’entretiennent les utilisateurs avec la technologie. Le troisième essai s’intéresse aux mécanismes sous-jacents qui guident la décision de l’utilisateur afin d’identifier les facteurs d’adoption d’une technologie de quantification de soi. / This thesis by articles explores the adoption of technologies and practices of quantified-self with a critical stance. The aim is to have a better understanding of users' experiences with self-quantization technologies, such as connected bracelets, and to question the real contribution of these technologies to the well-being and health of individuals. The first essay presents a systematic literature review on quantified-self and a research agenda for business researchers. The second essay is a qualitative study that reveals the power relationships that users have with technology. The third essay focuses on the underlying mechanisms that guide the user's decision in order to identify the factors that lead to the adoption of a quantified-self technology.

Användandet av bärbar teknologi förväntas öka och bli den största trenden inom träning ochhälsa. Klädesplagg, accessoarer och plåster används för att avslöja värden om allt frånhjärtfrekvens till sömnmönster till dietvanor. I takt med det ökande intresset för fysiologiskadata börjar även bärbar teknologi för kognitiva värden träda fram i form avkonsumenttillgängliga EEG-scanners. Aktörer som Muse, Neurosky och Emotiv ledermarknaden framåt och tillåter personer läsa av sina hjärnfrekvenser direkt hemifrån soffan.Syftet med studien är att undersöka intresse och förväntningar på kognitivt mätbar bärbarteknologi. Vidare är syftet även att utveckla en prototyp för att demonstrera hur en applikationtill denna typ av teknologi skulle kunna tänkas se ut, samt bidra till forskning ochmarknadsutveckling inom området. Studien är primärt utförd med en kvalitativ metod i form av en fokusgrupp, en semistrukturerad intervju och ett användbarhetstest, men visar även påkvantitativa inslag i form av ett enkätutskick där 139 anonyma respondenter deltog.Studiens slutsats pekar på ett intresse bland potentiella konsumenter för en mindre synlig bärbar teknologisk produkt med möjlighet att mäta värden som stressnivåer. Ett mindre intresse uttrycktes för delningsfunktioner och mätande av emotionella tillstånd. Produktens applikation förväntas vara lättförståelig och erbjuda både enklare och mer detaljerade data. De potentiella konsumenterna förväntar även att applikationen tydligt visualiserar vilka värden som mäts, samt att förbättringsförslag finns att tillgå. / The use of wearable devices is expected to increase and become the largest trend in fitness.Clothes, accessories and patches are used to reveal values ranging from heart rate to sleeppatterns to diet habits. In keeping with the growing interest in physiological data, portabletechnology for cognitive values are also emerging in the form of consumer-accessible EEGscanners. Actors like Muse, Neurosky and Emotiv are leading the market forward and allowspeople to read their brain frequencies directly from the couch in their home.The purpose of this study is to investigate the interest and expectations of cognitive measurable portable technology. Furthermore, the aim is also to develop a prototype to demonstrate how an application for this kind of technology could look, as well as contribute to research and market development in the area. The study was primarily done with a qualitative method in the form of a focus group, a semi-structured interview and a usability test, but also shows quantitative elements in the form of a poll where 139 anonymous respondents participated. The study's conclusion points to an interest among potential consumers for a less visible portable technological product with the ability to measure values such as stress levels. A lesser interest was expressed for sharing functions and measurement of emotional states. The product's application is expected to be easily understood and offers both simpler and more detailed data. Potential consumers also expect the application to clearly visualize which values are measured and that improvement proposals are available.

Transport systems account for a large part of the worlds CO2 emissions. In order to reach goals, set to lower emissions, we need to travel less by car and increase the use of sustainable means of transportation. Through the use of self-tracking devices and by visualizing the data collected, individuals can learn about, and discover, habits, patterns and practices amongst themselves. In this thesis, the question of how much individuals know about their own CO2 emissions created from the modes of transportation they use on an everyday basis, is explored. The paper examines how a visualization of personal movement and transport data affects individuals' understanding of their own CO2 emission as well as their motivation towards using more sustainable modes of transportation. A two-week user-study was conducted with 15 participants. The participants tracked their movements and transports using a mobile application on a smart phone, and their data was presented in a web-application. Prior and post to the user-study, a self-evaluation questionnaire based on the COM-B model was handed out. Results showed that participants' understanding increased regarding putting their emission amounts in relation to what is low and what is high between transportation modes. An increased awareness of personal transportation patterns and what the environmental impact the choice of transport mode has, was indicated. Further, participants' motivation towards using more sustainable modes of transportation seems to be dependent on realizing if they have low or high emissions but also if there exist available alternative transport options to switch to. / Transportsystem står för en stor del av världens koldioxidutsläpp. För att nå utsatta utsläppsmål måste färre resor göras med bil och användandet av hållbara transportmedel öka hos individer. Genom att börja spåra sig själv inom olika områden, och genom att bli presenterad sin insamlade data, kan individer upptäcka och lära sig om vanor, mönster och praxis bland dem själva. I denna avhandling undersöks hur mycket individer vet om sina egna koldioxidutsläpp som skapas av de transportmedel som de använder dagligen. I studien undersöks hur en visualisering av personlig rörelse- och transportdata påverkar individens förståelse av sitt eget koldioxidavtryck samt dennes motivation till att använda mer hållbara transportmedel. En användarstudie genomfördes med 15 deltagare över två veckor. Under denna period fick deltagarna spåra sina förflyttningar och vilka transportmedel som användes med hjälp av en applikation på en smart telefon. Det insamlade transportdata presenterades därefter i en webb- applikation med avseende på deltagarnas koldioxidutsläpp. Innan och efter användarstudien fick deltagarna svara på ett självutvärderingsformulär baserat på beteendemodellen COM-B. Resultaten visade att deltagarnas förståelse ökade när det gäller att sätta sina utsläppsmängder i förhållande till vad som är lågt respektive högt mellan olika transportmedel. En ökad medvetenhet om personliga transportmönster och hur stor miljöpåverkan valet av transportläge har, indikerades. Vidare verkar deltagarnas motivation att använda mer hållbara transportmedel vara beroende utav om de inser att de har låga eller höga utsläpp, men också om det finns tillgängliga, alternativa, transportmedel att byta till.

The primary aim of this study is to investigate the impact of an avatar to dog’s daily pet care for the quantified pet users. Furthermore, it explores the design challenges of creating a dog-like avatar for Human-Pet-Computer Interaction. Pawmoji, an emotionally expressive avatar, was designed as a supporting element on the interface to help users interpret the data and increase motivation. Pawmoji changes emotional states according to pet’s daily activities. The data from the device is conveyed to the user via changes in the emotional state of the avatar. This concept was evaluated by pet activity tracker users. In the evaluation, users interpreted the presented emotions of the dog-like avatar in a similar way to human emotions and generally identified the needs of their dogs with high accuracy. The study has also identified design challenges, such as representation and message delivery, and revealed the potential impact of the avatar in daily pet care activities: unpleasant emotions displayed by the avatar were found to be less motivating by the participants. The study shows that an emotionally expressive avatar has the potential to motivate the dog owners and increase awareness of the dog’s state and feelings during the pet-caretaking process. / Det primära syftet med den här studien är att undersöka en avatars påverkan på hundägares husdjursvård för den kvantifierade användaren. Vidare, en emotionell uttrycksfull hundliknande avatar skapades för att fungera som ett designkoncept. Pawmoji ändrar emotionella tillstånd i förhållande till djurets uppmätta aktiviteter. Data från enheten transporteras till användaren genom förändringar i avatarens emotionella tillstånd. Konceptet utvärderades genom en aktivitetsspårare för husdjur. I utvärderingen tolkade deltagarna känslorna som presenterades av den hundliknande avataren, på samma sätt som en människas känslor kan observeras och kunde generellt sett identifiera hundens behov med hög precision. Studien har också identifierat designutmaningar som framställning och mottagning av meddelande och upptäckte den potentiella påverkan avataren har i dagliga aktiviteter för husdjursvård: Obehagliga känslor som presenterades av avataren ansågs vara mindre motiverande för deltagarna. Studien visar att en emotionell uttrycksfull avatar har potentialen att motivera hundägarna och öka medvetenhet om hundens tillstånd och känslor under processen av djurskötning.

The application of automated reasoning approaches to Description Logic (DL) ontologies may produce certain consequences that either are deemed to be wrong or should be hidden for privacy reasons. The question is then how to repair the ontology such that the unwanted consequences can no longer be deduced. An optimal repair is one where the least amount of other consequences is removed. Most of the previous approaches to ontology repair are of a syntactic nature in that they remove or weaken the axioms explicitly present in the ontology, and thus cannot achieve semantic optimality. In previous work, we have addressed the problem of computing optimal repairs of (quantified) ABoxes, where the unwanted consequences are described by concept assertions of the light-weight DL EL. In the present paper, we improve on the results achieved so far in two ways. First, we allow for the presence of terminological knowledge in the form of an EL TBox. This TBox is assumed to be static in the sense that it cannot be changed in the repair process. Second, the construction of optimal repairs described in our previous work is best case exponential. We introduce an optimized construction that is exponential only in the worst case. First experimental results indicate that this reduces the size of the computed optimal repairs considerably.

Errors in Description Logic (DL) ontologies are often detected when a reasoner computes unwanted consequences. The question is then how to repair the ontology such that the unwanted consequences no longer follow, but as many of the other consequences as possible are preserved. The problem of computing such optimal repairs was addressed in our previous work in the setting where the data (expressed by an ABox) may contain errors, but the schema (expressed by an EL TBox) is assumed to be correct. Actually, we consider a generalization of ABoxes called quantified ABoxes (qABoxes) both as input for and as result of the repair process. Using qABoxes for repair allows us to retain more information, but the disadvantage is that standard DL systems do not accept qABoxes as input. This raises the question, investigated in the present paper, whether and how one can obtain optimal repairs if one restricts the output of the repair process to being ABoxes. In general, such optimal ABox repairs need not exist. Our main contribution is that we show how to decide the existence of optimal ABox repairs in exponential time, and how to compute all such repairs in case they exist.

Ontologies based on Description Logic (DL) represent general background knowledge in a terminology (TBox) and the actual data in an ABox. DL systems can then be used to compute consequences (such as answers to certain queries) from an ontology consisting of a TBox and an ABox. Since both human-made and machine-learned data sets may contain errors, which manifest themselves as unintuitive or obviously incorrect consequences, repairing DL-based ontologies in the sense of removing such unwanted consequences is an important topic in DL research. Most of the repair approaches described in the literature produce repairs that are not optimal, in the sense that they do not guarantee that only a minimal set of consequences is removed. In a series of papers, we have developed an approach for computing optimal repairs, starting with the restricted setting of an EL instance store, extending this to the more general setting of a quantified ABox (where some individuals may be anonymous), and then adding a static EL TBox. Here, we extend the expressivity of the underlying DL considerably, by adding nominals, inverse roles, regular role inclusions and the bottom concept to EL, which yields a fragment of the well-known DL Horn-SROIQ. The ideas underlying our repair approach still apply to this DL, though several non-trivial extensions are needed to deal with the new constructors and axioms. The developed repair approach can also be used to treat unwanted consequences expressed by certain conjunctive queries or regular path queries, and to handle Horn-ALCOI TBoxes with regular role inclusions. / This is an extended version of an article accepted at KR 2022.