Marc Schickler

Engineering an advanced location-based augmented reality engine for smart mobile devices

Daily business routines more and more require to access information systems in a mobile manner, while preserving a desktop-like feeling at the same time. The goal of this work is to outline the engineering process of a sophisticated mobile service running on a smartphone. More precisely, we show how to develop the core of a location-based augmented reality engine for the iPhone 4S based on the operating system iOS 5.1 (or higher). We denote this engine as AREA. In particular, we develop concepts for coping with limited resources on a mobile device, while providing a smooth user augmented reality experience at the same time. We further present and develop a suitable application architecture in this context, which easily allows integrating augmented reality with a wide range of applications.

End-User Programming of Mobile Services: Empowering Domain Experts to Implement Mobile Data Collection Applications

The widespread use of smart mobile devices (e.g., in clinical trials or online surveys) offers promising perspectives with respect to the controlled collection of high-quality data. The design, implementation and deployment of such mobile data collection applications, however, is challenging in several respects. First, various mobile operating systems need to be supported, taking the short release cycles of vendors into account as well. Second, domain-specific requirements need to be flexibly aligned with mobile application development. Third, usability styleguides need to be obeyed. Altogether, this turns both programming and maintaining mobile applications into a costly, time-consuming, and error-prone endeavor. To remedy these drawbacks, a model-driven framework empowering domain experts to implement robust mobile data collection applications in an intuitive way was realized. The design of this end-user programming framework is based on experiences gathered in real-life mobile data collection projects. Facets of various stakeholders involved in such projects are discussed and an overall architecture as well as its components are presented. In particular, it is shown how the framework enables domain experts (i.e., end users) to flexibly implement mobile data collection applications on their own. Overall, the framework allows for the effective support of mobile services in a multitude of application domains.

An Engine Enabling Location-Based Mobile Augmented Reality Applications

Contemporary smart mobile devices are already capable of running advanced mobile applications with demanding resource requirements. However, utilizing the technical capabilities of such devices constitutes a challenging task (e.g., when querying their sensors at run time). This paper deals with the design and implementation of an advanced mobile application, which enables location-based mobile augmented reality on different mobile operating systems (i.e., iOS and Android). In particular, this kind of application is characterized by high resource demands. For example, at run time various calculations become neccessary in order to correctly position and draw virtual objects on the screen of the smart mobile device. Hence, we focus on the lessons learned when implementing a robust and efficient, location-based mobile augmented reality engine as well as efficient mobile business applications based on it.

Process-Driven Data Collection with Smart Mobile Devices

Paper-based questionnaires are often used for collecting data in application domains like healthcare, psychology or education. Such paper-based approach, however, results in a massive workload for processing and analyzing the collected data. In order to relieve domain experts from these manual tasks, we propose a process-driven approach for implementing as well as running respective mobile business applications. In particular, the logic of a questionnaire is described in terms of an explicit process model. Based on this process model, in turn, multiple questionnaire instances may be created and enacted by a process engine. For this purpose, we present a generic architecture and demonstrate the development of electronic questionnaires in the context of scientific studies. Further, we discuss the major challenges and lessons learned. In this context the presented process-driven approach offers promising perspectives in respect to the development of mobile data collection applications.

A Mobile Service Engine Enabling Complex Data Collection Applications

The widespread distribution of smart mobile devices offers promising perspectives for the timely collection of huge amounts of data. When realizing sophisticated mobile data collection applications, numerous technical issues arise. For example, as many real-world projects require the support of different mobile operating systems, platform-specific peculiarities must be properly handled. Existing approaches often rely on specifically tailored mobile applications. As a drawback, changes to the data collection procedure result in costly code adaptations. To remedy this drawback, a model-driven approach is proposed, enabling end-users (i.e., domain experts) to create mobile data collection applications themselves. This model relies on complex questionnaires called instruments. An instrument not only contains all information about the data to be collected, but additionally comprises information on how it shall be processed on different mobile operating systems. For this purpose, we developed an advanced mobile (kernel) service being capable of processing sophisticated instruments on various platforms. This paper discusses fundamental kernel requirements and introduces the developed architecture. Altogether, the mobile service allows for the effective use of smart mobile devices in data collection application scenarios (e.g., clinical trials).

A Lightweight Process Engine for Enabling Advanced Mobile Applications

The widespread dissemination of smart mobile devices offers new perspectives for timely data collection in large-scale scenarios. However, realizing sophisticated mobile data collection applications raises various technical issues like the support of different mobile operating systems and their platform-specific features. Often, specifically tailored mobile applications are implemented in order to meet particular requirements. In this context, changes of the data collection procedure become costly and profound programming skills are needed to adapt the respective mobile application accordingly. To remedy this drawback, we developed a model-driven approach, enabling end-users to create mobile data collection applications themselves. Basis to this approach are elements for flexibly defining sophisticated questionnaires, called instruments, which not only contain information about the data to be collected, but also on how the instrument shall be processed on different mobile operating systems. For the latter purpose, we provide an advanced mobile (kernel) service that is capable of processing the logic of sophisticated instruments on various platforms. The paper discusses fundamental requirements for such a kernel and introduces a generic architecture. The feasibility of this architecture is demonstrated through a prototypical implementation. Altogether, the mobile service allows for the effective use of smart mobile devices in a multitude of different data collection application scenarios (e.g., clinical and psychological trials).

A Configurator Component for End-User Defined Mobile Data Collection Processes

The widespread dissemination of smart mobile devices offers promising perspectives for collecting huge amounts of data. When realizing mobile data collection applications (e.g., to support clinical trials), challenging issues arise. For example, many real-world projects require support for heterogeneous mobile operating systems. Usually, existing data collection approaches are based on specifically tailored mobile applications. As a drawback, changes of a data collection procedure require costly code adaptations. To remedy this drawback, we implemented a model-driven approach that enables end-users to realize mobile data collection applications themselves. This paper demonstrates the developed configurator component, which enables domain experts to implement digital questionnaires. Altogether, the configurator component allows for the fast development of questionnaires and hence for collecting data in large-scale scenarios using smart mobile devices.

Towards Flexible Mobile Data Collection in Healthcare

The widespread dissemination of smart mobile devices offers promising perspectives for a variety of healthcare data collection scenarios. Usually, the implementation of mobile healthcare applications for collecting patient data is cumbersome and time-consuming due to scenario-specific requirements as well as continuous adaptations to already existing mobile applications. Emerging approaches, therefore, aim to empower domain experts to create mobile data collection applications themselves. This paper discusses flexibility issues considered by a generic and sophisticated framework for realizing mobile data collection applications. Thereby, flexibility is discussed along different phases of data collection scenarios. Altogether, the realized flexibility significantly increases the practical benefit of smart mobile devices in healthcare data collection scenarios.

Advanced Algorithms for Location-Based Smart Mobile Augmented Reality Applications

During the last years, the computational capabilities of smart mobile devices have been continuously improved by hardware vendors, raising new opportunities for mobile application engineers. Mobile augmented reality is one scenario demonstrating that smart mobile applications are becoming increasingly mature. In the AREA (Augmented Reality Engine Application) project, we developed a kernel that enables such location-based mobile augmented reality applications. On top of the kernel, mobile application developers can easily realize their individual applications. The kernel, in turn, focuses on robustness and high performance. In addition, it provides a flexible architecture that fosters the development of individual location-based mobile augmented reality applications. In the first stage of the project, the LocationView concept was developed as the core for realizing the kernel algorithms. This LocationView concept has proven its usefulness in the context of various applications, running on iOS, Android, or Windows Phone. Due to the further evolution of computational capabilities on one hand and emerging demands of location-based mobile applications on the other, we developed a new kernel concept. In particular, the new kernel allows for handling points of interests (POI) clusters or enables the use of tracks. These changes required new concepts presented in this paper. To demonstrate the applicability of our kernel, we apply it in the context of various mobile applications. As a result, mobile augmented reality applications could be run on present mobile operating systems and be effectively realized by engineers utilizing our approach. We regard such applications as a good example for using mobile computational capabilities efficiently in order to support mobile users in everyday life more properly.

Development of Mobile Data Collection Applications by Domain Experts: Experimental Results from a Usability Study

Despite their drawbacks, paper-based questionnaires are still used to collect data in many application domains. In the QuestionSys project, we develop an advanced framework that enables domain experts to transform paper-based instruments to mobile data collection applications, which then run on smart mobile devices. The framework empowers domain experts to develop robust mobile data collection applications on their own without the need to involve programmers. To realize this vision, a configurator component applying a model-driven approach is developed. As this component shall relieve domain experts from technical issues, it has to be proven that domain experts are actually able to use the configurator properly. The experiment presented in this paper investigates the mental efforts for creating such data collection applications by comparing novices and experts. Results reveal that even novices are able to model instruments with an acceptable number of errors. Altogether, the QuestionSys framework empowers domain experts to develop sophisticated mobile data collection applications by orders of magnitude faster compared to current mobile application development practices.