1752 research outputs found
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Exploring the Usability of the German COVID-19 Contact Tracing App in a Combined Eye Tracking and Retrospective Think Aloud Study
Full text linkIn the course of the corona virus (COVID-19) pandemic, many digital solutions for mobile devices (e.g., apps) were presented in order to provide additional resources supporting the control of the pandemic. Contact tracing apps (i.e., identify persons who may have been in contact with a COVID-19 infected) constitute one of the most popular as well as promising solutions. However, as a prerequisite for an effective application, such apps highly depend on being used by large numbers of the population. Consequently, it is important that these apps offer a high usability for everyone. We therefore conducted an exploratory study to learn more about the usability of the German COVID-19 contact tracing app Corona-Warn-App (CWA). More specifically, N = 15 participants assessed the CWA, relying on a combined eye tracking and retrospective think aloud approach. The results indicate, on the one hand, that the CWA leaves a promising impression for pandemic control, as essential functions are easily recognized. However, on the other hand, issues were revealed (e.g., privacy policy) that could be addressed in future updates more properly
Konzeption und Realisierung einer mobilen Anwendung zum Zusammenfügen von Musikstücken und Teilen auf einer Plattform für iOS
No full textEs gibt aktuell kaum Zweifel daran, dass mobile Apps im Allgemeinen beliebt sind. Bei der Implementierung einer Applikation weiß man jedoch meistens nicht, wie beliebt sie wirklich sein wird. Gute Anhaltspunkte sind, wenn sie hilfreich ist, ein Problem löst oder Spaß macht. Die vorliegende Arbeit beschäftigt sich mit der Idee, andere Tonspuren mit einer eigenen zu fusionieren und das Resultat mit anderen Nutzern auf einer Plattform zu teilen
Seven Guidelines for Designing the User Interface in Robotic Process Automation
Full text linkRobotic Process Automation (RPA) aims to automate rule-based business process tasks by software robots (bots) mimicking human interactions. Despite the partial automation achieved with RPA, humans still need to interact with the bots, which requires appropriate user interfaces. However, existing RPA research has not evaluated RPA from a software-ergonomic perspective so far and no corresponding user interface design guidelines exist. The objective of this paper is to evaluate the usability of RPA bots in industry and to provide user interface design guidelines to bot developers. The results we obtained from 50 questionnaires filled by RPA users indicate that both the input/output and the dialogue interfaces of RPA need to be improved, especially regarding error tolerance, perceptibility, directability of user’s attention, suitability for the task, and availability. Finally, we derive seven guidelines for designing the user interface of RPA bots. Potential improvements include, among others, the quality of error messages, the efforts for error handling, and the monitoring of the current status of the tasks assigned to the bot
Process Modeling Kills Agility - Resolving Misunderstandings and Make BPM Agile
No full textDue to globalisation, companies have to adapt to constant change and quickly adjust
to their environment in order to meet customers’ requirements. In addition, competition
is forcing companies to adapt their products and processes more quickly.
Business Process Management (BPM) has established itself as a top discipline
that helps companies improve their performance. However, in recent studies, researchers
have expressed their dissatisfaction with BPM. Managing processes in
a classical way seems to be quite rigid, and researchers suggest countering the
inflexibility of BPM with a more agile approach that also focuses on the value of innovation.
Agility is one of the key success factors in modern industries. It supports
organisations cope with a changing environment, leading to rapid adaptation. Agile
companies are more flexible and sustainable. For this reason, many agile BPM
frameworks and methodologies have been proposed, but few of them have been
tested in practice, in particular, there is no framework that combines BPM, agility
and innovation in a single framework.
A preliminary framework presented in this thesis has its focus on agility and BPM,
based on a previously conducted literature review, and considers how innovation influences
the framework. Afterwards, it is reviewed by experts in the field of BPM and
agility. As a result, 13 experts challenge this framework and comment on its practicality.
Based on the interviews, the preliminary framework is revised and transformed
into the final agile BPM framework.
In summary, BPM and agility have more in common than it seems. One important
aspect is that BPM has often not been executed agile enough, as it could have been.
Therefore, our framework highlights the agile characteristics of BPM, but also brings
new agile practices into BPM and shows an interdependency with innovation
A One-Dimensional Kalman Filter for Real-Time Progress Prediction in Object Lifecycle Processes
Full text linkReal-time monitoring of business processes offers promising perspectives to discover problems and optimisation potentials. Early detection is a key part in this endeavour. One crucial aspect of real-time monitoring is to determine the current progress of a running business process. This is particularly challenging for business processes that consist of a multitude of loosely coupled, smaller processes that interact with each other, like object lifecycle processes in data-centric approaches to business process management. In this paper, an approach to predict the remaining portion of the process path to be still executed in relation to the overall process is proposed. This prediction is based on a one-dimensional Kalman Filter. As a major benefit of this approach, real-time progress determination can start directly with the first run of the process, i.e., without
need for comprehensive event log data. This becomes possible due to the procedure applied by the Kalman Filter, which requires no log data. A quantitative study with 250 progress estimations for large object lifecycle processes results in a deviation of the average estimated progress from the real progress, calculated after the completion of the process, of about 5%. This emphasises that reasonable progress predictions are possible even in the absence of an event log, as it is the case when deploying new or changed processes to the run-time system
Konzeption und Realisierung einer Cross-developed Ecological Momentary Intervention App für Herz-Risiko-Patienten
Full text linkSmartphones gewinnen durch den zunehmenden Einsatz im Alltag an Bedeutung. Ein möglicher Einsatzbereich ist die des Gesundheitswesens, welche mobile Health (mHealth) genannt wird. In dieser können Patienten in Anwendungen Fragebögen auf mobilen Geräten beantworten, wodurch die Automatisierung der Auswertung und Datenerfassung für größerer Benutzergruppen ermöglicht werden kann. Im Rahmen dieser Arbeit wird ein generischer Fragebogen für Herz-Risiko-Patienten erstellt. Diesen Fragebogen können die Patienten in einer mobilen iOS und Android Anwendung, welche mit Hilfe von Flutter implementiert wurde, beantworten. Die Patienten erhalten anschließend eine automatisierte Auswertung der Fragen in Form eines Feedbacks. Der Datenaustausch erfolgt dabei mit einer REST API, die vom Institut für Datenbanken und Informationssysteme (DBIS) der Universität Ulm zur Verfügung gestellt wird. Zusätzlich bietet die entwickelte Applikation weitere Funktionen an, welche die REST API zur Verfügung stellt
Smartphone-based Walking Speed Estimation in the Context of Mindful Walking
No full textWith smartphones being more widespread and having more users than ever, the possibilities of using them for mHealth purposes are on the rise. Since users carry their smartphone on them constantly, they are the ideal tools for non-invasive fitness and health monitoring. The main focus in this thesis being the walking speed which is a
general indicator of health and can be seen as a vital sign. Walking speed can for example show mortality, high stress levels or diseases such as depression. One application aiming to counteract such conditions is mindful walking. The application monitors the walking speed of users and through haptic feedback reminds them to keep a steady pace which is below a predefined limit. This thesis serves as a framework for state-of-the-art walking speed estimation methods in the setting of free outdoor walking with a live speed monitoring, only using a smartphones’ internal sensors. Three general methods were used: GPS, step counting and raw sensor data in combination with machine learning. Due to the known inaccuracies of GPS in certain locations, the measurements were evaluated in the following four locations: city centre, forest, suburbs and open field. To be able to use the step counting method, first, the step length of each participant has to be determined which also tests two methods. The first one is an estimation by height and gender, the second one uses GPS and step counting over a predefined distance. To evaluate all these methods and find the most precise one, a series of experiments was done. Overall, GPS delivers the worst accurate walking speed estimation with an average root mean square error of at least 2.429 km/h doing the manual calculation and 1.126 km/h extracting the speed directly from the GPS signal. The method utilizing machine learning offers a root mean square error of 1.143 km/h, but shows possible ways of improvement. The most accurate walking speed estimation method for both Android and iOS uses step counting and has a root mean square error of 1.084 km/h and 1.014 km/h, respectively
Secured Refactoring of an existing Persistence Layer in an Open Source Framework with Higher-Order-Functions to Leverage Code Quality, Consistency and Coding Speed
No full textIn this thesis an application Axians IT & Cloud Automation sells is improved by refactoring with the help of higher-order function in an persistence layer. The improvements impact on three major categories of the application, namely coding speed, consistency across the application and the overall code quality. To achieve this goal the database CRUD operations of the persistence layer are analysed based on two different application projects, a template used for new customers and a project used by Axians to monitor the application, and later refactored into a single function. Finally the implemented code is evaluated to show the increase in code quality, consistency and coding speed
Design and Implementation of Just-In-Time Adaptive Interventions in an eHealth Platform
Full text linkMit zunehmender Bedeutung von Internet und Smartphones entwickelten sich in der Psychotherapie neue, vielversprechende Behandlungsansätze, die Probleme der klassischen Face-2-Face Therapie lösen. Durch Internet- and Mobile-based Interventions (IMIs) können kosteneffektive, flächendeckende und anonyme therapeutische Behandlungen über das Internet bereitgestellt werden. Eine fortgeschrittene Art von IMIs sind Just-In-Time Adaptive Interventions (JITAIs). JITAIs nutzen Sensoren in Smartphones, um die Umgebung und das Verhalten der Patient:innen im Alltag zu analysieren. Aus diesen Informationen ermitteln sie den Kontext und stellen ihnen Interventionen zum richtigen Zeitpunkt und mit den richtigen Inhalten zur Verfügung. Folglich personalisieren JITAIs die Behandlung, unterstützen Patient: innen im Alltag und erkennen kritische Situationen. Im Fokus dieser Masterarbeit steht die konzeptuelle und technische Umsetzung von JITAIs in E-Health Plattformen. Es wird eine mehrstufige Architektur vorgestellt, die verschiedene Datenströme modular erhebt und analysiert. Hierbei handelt es sich um Daten von Smartphone-Sensoren, Ecological Momentary Assessments und externen Geräten sowie um In-App Metadaten. Der Datenfluss kann einen Trigger auslösen, der Patient:innen eine Intervention vorschlägt. Es werden Visualisierungstechniken dieser Datenströme dargestellt sowie ein kontextsensitives Empfehlungssystem, das zusätzlich Feedback und Metadaten von Patient:innen berücksichtigt. Darüber hinaus zeigt die Arbeit auf, wie Interventionen und Inhalte abhängig von abgegebenen Antworten und persönlichen Informationen adaptiert werden können. Der praktische Teil erweitert die E-Health Plattform der Abteilung für Klinische Psychologie und Psychotherapie der Universität Ulm um die Adaptierung von Inhalten, sodass Patient:innen eine individuellere Behandlung erhalten. Die vorliegende Arbeit schafft Grundlagen für die Einführung von JITAIs in E-Health Plattformen. Insbesondere kann die erarbeitete Architektur zukünftig als Ausgangspunkt dienen, um die Datenströme mithilfe von künstlicher Intelligenz zu analysieren