This work describes ways to use a person identification based on faces on mobile devices with Android platform. A reader is introduced into a structure of this system and a way to create applications for it. Besides, there are also methods usable to the face identification. Some of these methods (used in an implementation) are described in more detail. This work also contains a description of model AAM (Active Appearance Model) for implementing in mobile devices and evaluation of used algorithms results.

Die Angebote auf dem Verkehrsmittelmarkt werden immer differenzierter: Innovative Lösungen wie Bike- oder Carsharing- Konzepte schließen aus Angebotsperspektive zunehmend die Lücke zwischen dem reinen Individualverkehr (IV) und dem klassischen öffentlichen Verkehr (ÖV). Ein Umsteigen auf Bus und Bahn kommt jedoch für Autofahrer nur dann in Frage, wenn die gewohnten Mobilitätsmuster im Hinblick u.a. auf Fahrtzeiten, Routen und zusätzliche Erledigungen auch mit dem ÖV gleichermaßen komfortabel möglich sind. Smartphones bieten in diesem Zusammenhang die Möglichkeit, auch von unterwegs aus Routen mit Bus, Bahn, Leihrädern und Carsharing so flexibel und spontan zu planen, dass das Navigieren durch den öffentlichen Verkehr deutlich komfortabler und übersichtlicher wird. Außerdem kann das Abrufen von Informationen über Smartphones dazu beitragen, in öffentlichen Räumen ein Gefühl der Privatheit und der Sicherheit zu erzeugen. Somit wird der Zugang zum öffentlichen Verkehr durch Routenplanungs-Apps für viele Menschen vereinfacht. Das InnoZ hat im Projekt „cairo“ über zwei Jahre die Nutzeranforderungen an ein mobiles Routing für den öffentlichen Verkehr untersucht und dabei auch Änderungen im Zeitverlauf erfasst. Insgesamt konnten damit nicht nur generelle Anforderungen an die technische Ausgestaltung der Routing-App ausgewertet, sondern auch konkrete Informationsbedarfe nach Orten, Wegen oder Verkehrsmitteln bestimmt werden. Die Ergebnisse dieser Studie werden im vorliegenden Baustein vorgestellt. / During recent years, the mobility service market has diversified: Innovative solutions like bike- and carsharing concepts increasingly fill the gap between individual car transport, and public transport. Most car owners, though, will only change to public transport, if travelling becomes as comfortable as car trips in terms of travelling routines, times, activities, and routes. In this context, smartphones indeed facilitate the access to public transport by enabling a flexible and spontaneous planning of bus, train, bike rental and carsharing trips “on the go”. Moreover, smartphones can enhance the feeling of security and privacy in public spaces in general. As a consequencee, navigating through the public transport network becomes much easier and comfortable for many user groups. Within the “cairo” project, researchers at InnoZ have assessed user preferences on mobile routing over a period of two years, and could therefore also detect changes over time. Apart from general requirements on technical functions, actual information needs with regards to location, transport modes, or routes have been a central subject of research. The results of this study are summarized in this Baustein.

The goal of this thesis is to develop a smartphone application for augmented reality view; it is an initial attempt to realize a driver assistance functionality using just a smartphone and an external lens. Initially it depicts a brief analysis about the most feasible development technologies for mobile application development, selecting a proper lens and positioning of the smartphone in the car. Later, it discusses the strategies for real-time object detection using OpenCV; the video frames are processed using the strategies to find patterns in the videos. Different techniques like Hough-line transform, watershed, contour detection, color segmentation, color thresholding and HAAR cascades are implemented and compared in terms of real time detection of the desired objects. Then a unified algorithm is implemented for the given scenario which overcomes the challenges faced during the conceptualization phase. Finally, the results are depicted with the snapshots of the real time detection done from the smartphone and then evaluated against the vision of the application and the achieved tasks. This thesis is concluded by stating the prospects of this mobile application in the future.

<div>Pathogen identification at the point of use is critical in preventing disease transmission and enabling prompt treatment. Current rapid diagnostic tests suffer from high rates of false negatives because they are not capable of detecting the inherently low concentrations of pathogens found in early stages of infection or in environmental reservoirs. The gold standard method for timely pathogen identification is a nucleic acid amplification assay called polymerase chain reaction. Although polymerase chain reaction is extremely sensitive and specific, it requires expensive laboratory equipment and trained personnel to perform the sample preparation, cyclical heating, and amplicon analysis. Isothermal nucleic acid amplification assays are better suited for field use because they operate at a single temperature and are robust to common sample matrix inhibitors. Thus, there is a need to translate isothermal amplification assays to the point of use for rapid and sensitive detection of pathogens in complex samples.</div><div><br></div><div>Here, I outline an approach to bring laboratory-based sample preparation, assays, and analyses to the point of use via portable platforms. First, I characterize a loop-mediated isothermal amplification assay and combine it with lateral flow immunoassay for simple, colorimetric interpretation of results. Next, I optimize an ambient-temperature reagent storage method to eliminate cold-chain requirements and precision pipetting steps. I then incorporate loop-mediated isothermal amplification, lateral flow immunoassay, and reagent drying into two different integrated paperfluidic platforms and demonstrate their ability to separately detect bacteria and viruses in complex sample matrices. Finally, I couple loop-mediated isothermal amplification with particle diffusometry to optically determine pathogen presence by tracking the Brownian motion of particles added to an amplified sample. The combined loop-mediated isothermal amplification and particle diffusometry method is first characterized on a microscope and then translated to a smartphone-based platform. Each of these portable platforms are broadly applicable because they can be easily modified for identification of other pathogens at the point of use.</div>

This thesis describes the possibilities of using smartphones and their cameras in combination with modern computer vision algorithms to track and measure the speed of a floorball. Previous research within the area is described and an explanation is given as to why an implementation using three-frame temporal differencing to detect objects in motion works best to detect and track the ball. 100 floorball shots were recorded and measured using a speedometer radar and two different smartphones, one running the application and the other recording each shot. The video recording for each shot was then used to manually create a baseline for speed comparison. A second experiment was later conducted to analyse the sensitivity and effect on the determined ball size in the floorball shot analysis. The results from the first experiment show that the speedometer radar results in average deviate by 12% from the speed baseline. The speedshooting application however has results that, on average, deviate from the speed baseline by 6%. Furthermore, the results show that a faulty ball size detection is the major cause of error in the speedshooting application. The main conclusion that can be drawn from this is that it is possible to use a smartphone and computer vision methodologies to determine the speed of a floorball shot. In fact, it is even possible to do so with greater accuracy than the radar used in the experiments in this thesis. However, to prove the accuracy of the application for normal use, further testing needs to be conducted in new experiment conditions, for example by recording shots at higher speeds than those recorded in the experiments in this thesis.

This study investigated the factors that influence university students’ intention to use a smartphone. The study proposed and validated a research model based on the technology acceptance model (TAM). The TAM was modified and extended with four new constructs: social norms, perceived enjoyment, perceived value and ease of access. The constructs for the instrument of the study were adapted from previous related studies which had validated the instruments. Data were collected from 110 participants via a survey. The collected data were analyzed using hierarchical multiple-regression using the statistical package for social sciences (SPSS version 22.0). The model demonstrated a good fit where six independent variables together contributed 56.2% of the variance to the outcome or prediction. The results of the analysis were used to test a set of research hypotheses and to answer research questions. The following independent variables were found to be significant in determining university students’ intention to use mobile devices: perceived usefulness, social norms, perceived enjoyment, perceived value and ease of access. The control variables gender and degree level and the independent variable ease of use were not significant predictors. The results of this study may be useful to understand which factors are more important to the students. This understanding can be utilized by the University administrators for developing policies related to mobile learning and by the IT departments for planning organizational technology services.

Background: The emergence of mobile phones today and in history proves to beexceptionally unique in the consumer electronics market as well as the telecommunicationmarket. The reason behind the huge growth of the industry has been the exceptional evolutionof the technology used in the mobile devices in terms of performance and miniaturization. Purpose: The purpose of the research is to investigate the different stages within theconsumer decision making process and the influencing factors that have a grip overconsumers and shape the consumer behaviour towards smartphones. The study is aimed atexploring the main reasons that affect the buying decision of students regarding a specificcase, which is the iPhone product and its student users in the Swedish market. By focusing ona specific segment of the iPhone consumers, the research will present more accurate and clearresults. The theoretical perspective of the study will be using the traditional five-stagedecision-making model as a backbone thus putting the main focus of the analysis on theconsumer behaviour of the respondents. Method: Additionally, the paper will take the customers’ point of view in order to portray theinfluencing factors that exist within the consumers’ decision-making. For the purpose of thestudy, 12 iPhone users will be interviewed through semi-structured interviews and thegathered data would be analysed and coded in order to find and synthesize the results into aframework that will be developed specifically for the case. Conclusion: According to findings that were made regarding the iPhone case, there were afew factors that stood out as the most relevant when it comes to the students’ purchasedecision - brand loyalty, culture and society, perceived risk, financial influence, usage andproduct features. After the initial purchase of an iPhone which is mainly influenced by socialinfluences, it can be concluded that brand loyalty plays a key role in the repurchase behaviourof customers.

Smartphones are used today to accomplish a variety of different tasks, but it has some issues that might be solved with new technology. Augmented Reality is a developing technology that in the future can be used in our daily lives to solve some of the problems that smartphones have. Before people will adopt the new augmented technology it is important to have an intuitive method to interact with it. Hand gesturing has always been a vital part of human interaction. Using hand gestures to interact with devices has the potential to be a more natural and familiar method than traditional methods, such as keyboards, controllers, and computer mice. The aim of this thesis is to explore whether hand gesture recognition in an Augmented Reality head-mounted display can provide the same interaction possibilities as a smartphone touchscreen. This was done by implementing an application in Unity that mimics an interface of a smartphone, but uses hand gestures as input in AR. The Leap Motion Controller was the device used to perform hand gesture recognition. To test how practical hand gestures are as an interaction method, text typing was chosen as the task to be used to measure this, as it is used in many applications on smartphones. Thus, the results can be better generalized to real world usage.Five different keyboards were designed and tested in a pilot study. A controlled experiment was conducted, in which 12 participants tried two hand gesturing keyboards and a touchscreen keyboard. This was done to compare how hand gestures compare to touchscreen interaction. In the experiment, participants wrote words using the keyboards, while their completion time and accuracy was recorded. After using a keyboard, a questionnaire was completed by the participants to measure the usability.  The results consists of an implementation of five different keyboards, and data collected from the experiment. The data gathered from the experiment consists of completion time, accuracy, and usability derived from questionnaire responses. Statistical tests were used to determine statistical significance between the keyboards used in the experiment. The results are presented in graphs and tables. The results show that typing with pinch gestures in augmented reality is a slow and tiresome way of typing and affects the users completion time and accuracy negatively, in relation to using a touchscreen. The lower completion time, and higher usability, of the touchscreen keyboard could be determined with statistical significance. Prediction and auto-completion might help with fatigue as fewer key presses are needed to create a word. The research concludes that hand gestures are reasonable to use as input technique to accomplish certain tasks that a smartphone performs. These include simple tasks such as scrolling through a website or opening an email. However, tasks that involve typing long sentences, e.g. composing an email, is arduous using pinch gestures. When it comes to typing, the authors advice developers to employ a continuous gesture typing approach such as Swype for Android and iOS.

This study investigates if a smartphones orientation in the pocket affects the result of a decision tree model trained with data from personal falls, and also how a low-pass filter affects these results. A comparison is made between the results gathered from this study, compared to previous studies and products within the field. The data was gathered using a smartphone application and was later split up to get datasets for all the different orientations of the smartphone. Before training the models, the data was processed through a low pass filter. Results showed that low pass filtered signals generally performed better and that two of the trained models, could outscore at least one other algorithm cited in this thesis in at least one category. However, existing products on the market that were investigated do not disclose their statistics and a comparison to these products could not be made. The best two orientations for the phone to be placed in the pocket was when the face of the phone was pointing out from the leg, and top of the phone was pointing up and also when the face of the phone was pointing out from the leg, and the top of the phone was pointing down.

Denna avhandling beskriver hur en smartphone-baserad larm-applikation som ger säkrare resor för cyklister och löpare kan konstrueras. Genom att övervaka och utvärdera GPS-data från telefonen över tid skickar den föreslagna applikations-prototypen, namngiven till Are You OK App (AYOKA), automatiskt SMS-meddelanden och initierar telefonsamtal till kontakter i en lista konfigurerad av användaren när densamme har råkat ut för ett fall.I detta projekt härleds fall utifrån GPS-inaktivitet (när användarens geografiska koordinater är oförändrade inom ett valt tidsintervall). Detektion av GPS-inaktivitet, i kombination med att användaren inte har svarat, sätter igång larmfunktionen i applikationen. Projektet exemplifierar också hur implementeringen av ett flöde som delar data i ett moln, vilket använder Microsoft Azure som plattform, kan förbättra applikationens datainsamling avsevärt. Genom att använda en IoT Hub, Stream Analytics och en Azure SQL-databas, visar prototypen hur insamlad data kan centraliseras och potentiellt användas i framtida forskning inom övervakning och analys. Den testade prototypen visar ett förbättrat nöd- / säkerhetssystem som kan fungera i många olika sammanhang. Metoden för detektion passar relativt bra med fokus på cyklister och löpare eftersom dessa aktiviteter innebär att utövaren förflyttar sig, vilket i sin tur gör GPS-spårning effektiv. Några nackdelar som diskuteras är den höga grad av interaktion från användaren som behövs för att urskilja ett fall från en vald paus. För att möjliggöra detektering av fall från fysisk aktivitet som sker på en och samma geografiska plats, skulle det vara nödvändigt att i detekteringen använda data från accelerometer och gyroskop. I avhandlingen föreslås att prototypen, inklusive delnings-flödet för molndata, kan tjäna som ett ramverk för framtida system för smarta telefoner där fall-detektering använder sig av strömmad sensordata från enheten. / This thesis describes how a smartphone-based alarm application can be constructed to provide safer trips for cyclists and runners. Through monitoring and evaluating GPS data via the mobile device over time, the proposed application prototype, coined Are You OK App (AYOKA), automatically sends SMS messages and initiates phone calls to contacts in a user-configured list when a fall is detected. In this project, falls are inferred on the basis of GPS-inactivity (in this context defined as when the user’s geolocation has not changed within a selected time interval). Detection of GPS-inactivity, combined with a lack of response from the user, will trigger the alarming features of the application. The project also exemplifies how the implementation of a cloud data sharing flow, which uses Microsoft Azure as a platform, can significantly enhance the data gathering capabilities of the application. By utilizing an IoT Hub, Stream Analytics and an Azure SQL database, the prototype demonstrates how the gathered data can be centralized, and in future research could potentially be utilized for monitoring and analytical purposes. The method of detection performed relatively well with the focus on cyclists and runners since these activities involve changing of geographical coordinates, thereby making GPS-tracking effective. By focusing on detecting GPS-inactivity, it is argued that the prototype could potentially be utilized in other emergency scenarios apart from falls, such as being hit by a car. A disadvantage discussed includes the high degree of reliance on user participation to discern a fall from a voluntary pause. To enable detection of falls from physical activity occurring in one location, it would be necessary to incorporate data from accelerometer and gyroscope sensors into the current fall detection functionality. This thesis suggests that the prototype, including the cloud data sharing flow, can serve as a framework for future smartphone-based fall detection systems that use streamed sensor data.