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Predicting the Gender of Individuals with Tinnitus based on Daily Life Data of the TrackYourTinnitus mHealth Platform (2021)
Allgaier, Johannes ; Schlee, Winfried ; Langguth, Berthold ; Probst, Thomas ; Pryss, Rüdiger
Tinnitus is an auditory phantom perception in the absence of an external sound stimulation. People with tinnitus often report severe constraints in their daily life. Interestingly, indications exist on gender differences between women and men both in the symptom profile as well as in the response to specific tinnitus treatments. In this paper, data of the TrackYourTinnitus platform (TYT) were analyzed to investigate whether the gender of users can be predicted. In general, the TYT mobile Health crowdsensing platform was developed to demystify the daily and momentary variations of tinnitus symptoms over time. The goal of the presented investigation is a better understanding of gender-related differences in the symptom profiles of users from TYT. Based on two questionnaires of TYT, four machine learning based classifiers were trained and analyzed. With respect to the provided daily answers, the gender of TYT users can be predicted with an accuracy of 81.7%. In this context, worries, difficulties in concentration, and irritability towards the family are the three most important characteristics for predicting the gender. Note that in contrast to existing studies on TYT, daily answers to the worst symptom question were firstly investigated in more detail. It was found that results of this question significantly contribute to the prediction of the gender of TYT users. Overall, our findings indicate gender-related differences in tinnitus and tinnitus-related symptoms. Based on evidence that gender impacts the development of tinnitus, the gathered insights can be considered relevant and justify further investigations in this direction.
Applying Eye Movement Modeling Examples to guide novices' attention in the comprehension of process models (2021)
Winter, Michael ; Pryss, Rüdiger ; Probst, Thomas ; Reichert, Manfred
Process models are crucial artifacts in many domains, and hence, their proper comprehension is of importance. Process models mediate a plethora of aspects that are needed to be comprehended correctly. Novices especially face difficulties in the comprehension of process models, since the correct comprehension of such models requires process modeling expertise and visual observation capabilities to interpret these models correctly. Research from other domains demonstrated that the visual observation capabilities of experts can be conveyed to novices. In order to evaluate the latter in the context of process model comprehension, this paper presents the results from ongoing research, in which gaze data from experts are used as Eye Movement Modeling Examples (EMMEs) to convey visual observation capabilities to novices. Compared to prior results, the application of EMMEs improves process model comprehension significantly for novices. Novices achieved in some cases similar performances in process model comprehension to experts. The study's insights highlight the positive effect of EMMEs on fostering the comprehension of process models.
Flexible development of location-based mobile augmented reality applications with AREA (2020)
Schickler, Marc ; Reichert, Manfred ; Geiger, Philip ; Winkler, Jens ; Funk, Thomas ; Weilbach, Micha ; Pryss, Rüdiger
Mobile applications have garnered a lot of attention in the last years. The computational capabilities of mobile devices are the mainstay to develop completely new application types. The provision of augmented reality experiences on mobile devices paves one alley in this field. For example, in the automotive domain, augmented reality applications are used to experience, inter alia, the interior of a car by moving a mobile device around. The device’s camera then detects interior parts and shows additional information to the customer within the camera view. Another application type that is increasingly utilized is related to the combination of serious games with mobile augmented reality functions. Although the latter combination is promising for many scenarios, technically, it is a complex endeavor. In the AREA (Augmented Reality Engine Application) project, a kernel was implemented that enables location-based mobile augmented reality applications. Importantly, this kernel provides a flexible architecture that fosters the development of individual location-based mobile augmented reality applications. The work at hand shows the flexibility of AREA based on a developed serious game. Furthermore, the algorithm framework and major features of it are presented. As the conclusion of this paper, it is shown that mobile augmented reality applications require high development efforts. Therefore, flexible frameworks like AREA are crucial to develop respective applications in a reasonable time.
Applying Machine Learning to Daily-Life Data From the TrackYourTinnitus Mobile Health Crowdsensing Platform to Predict the Mobile Operating System Used With High Accuracy: Longitudinal Observational Study (2020)
Pryss, Rüdiger ; Schlee, Winfried ; Hoppenstedt, Burkhard ; Reichert, Manfred ; Spiliopoulou, Myra ; Langguth, Berthold ; Breitmayer, Marius ; Probst, Thomas
Background: Tinnitus is often described as the phantom perception of a sound and is experienced by 5.1% to 42.7% of the population worldwide, at least once during their lifetime. The symptoms often reduce the patient's quality of life. The TrackYourTinnitus (TYT) mobile health (mHealth) crowdsensing platform was developed for two operating systems (OS)-Android and iOS-to help patients demystify the daily moment-to-moment variations of their tinnitus symptoms. In all platforms developed for more than one OS, it is important to investigate whether the crowdsensed data predicts the OS that was used in order to understand the degree to which the OS is a confounder that is necessary to consider.
Corona Health — A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic (2021)
Beierle, Felix ; Schobel, Johannes ; Vogel, Carsten ; Allgaier, Johannes ; Mulansky, Lena ; Haug, Fabian ; Haug, Julian ; Schlee, Winfried ; Holfelder, Marc ; Stach, Michael ; Schickler, Marc ; Baumeister, Harald ; Cohrdes, Caroline ; Deckert, Jürgen ; Deserno, Lorenz ; Edler, Johanna-Sophie ; Eichner, Felizitas A. ; Greger, Helmut ; Hein, Grit ; Heuschmann, Peter ; John, Dennis ; Kestler, Hans A. ; Krefting, Dagmar ; Langguth, Berthold ; Meybohm, Patrick ; Probst, Thomas ; Reichert, Manfred ; Romanos, Marcel ; Störk, Stefan ; Terhorst, Yannik ; Weiß, Martin ; Pryss, Rüdiger
Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.
“How come you don’t call me?” Smartphone communication app usage as an indicator of loneliness and social well-being across the adult lifespan during the COVID-19 pandemic (2021)
Wetzel, Britta ; Pryss, Rüdiger ; Baumeister, Harald ; Edler, Johanna-Sophie ; Gonçalves, Ana Sofia Oliveira ; Cohrdes, Caroline
Loneliness and lack of social well-being are associated with adverse health outcomes and have increased during the COVID-19 pandemic. Smartphone communication data have been suggested to help monitor loneliness, but this requires further evidence. We investigated the informative value of smartphone communication app data for predicting subjective loneliness and social well-being in a sample of 364 participants ranging from 18 to 78 years of age (52.2% female; mean age = 42.54, SD = 13.22) derived from the CORONA HEALTH APP study from July to December 2020 in Germany. The participants experienced relatively high levels of loneliness and low social well-being during the time period characterized by the COVID-19 pandemic. Apart from positive associations with phone call use times, smartphone communication app use was associated with social well-being and loneliness only when considering the age of participants. Younger participants with higher use times tended to report less social well-being and higher loneliness, while the opposite association was found for older adults. Thus, the informative value of smartphone communication use time was rather small and became evident only in consideration of age. The results highlight the need for further investigations and the need to address several limitations in order to draw conclusions at the population level.
Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings (2019)
Kammerer, Klaus ; Hoppenstedt, Burkhard ; Pryss, Rüdiger ; Stökler, Steffen ; Allgaier, Johannes ; Reichert, Manfred
o build, run, and maintain reliable manufacturing machines, the condition of their components has to be continuously monitored. When following a fine-grained monitoring of these machines, challenges emerge pertaining to the (1) feeding procedure of large amounts of sensor data to downstream processing components and the (2) meaningful analysis of the produced data. Regarding the latter aspect, manifold purposes are addressed by practitioners and researchers. Two analyses of real-world datasets that were generated in production settings are discussed in this paper. More specifically, the analyses had the goals (1) to detect sensor data anomalies for further analyses of a pharma packaging scenario and (2) to predict unfavorable temperature values of a 3D printing machine environment. Based on the results of the analyses, it will be shown that a proper management of machines and their components in industrial manufacturing environments can be efficiently supported by the detection of anomalies. The latter shall help to support the technical evangelists of the production companies more properly.
Efficient processing of geospatial mHealth data using a scalable crowdsensing platform (2020)
Kraft, Robin ; Birk, Ferdinand ; Reichert, Manfred ; Deshpande, Aniruddha ; Schlee, Winfried ; Langguth, Berthold ; Baumeister, Harald ; Probst, Thomas ; Spiliopoulou, Myra ; Pryss, Rüdiger
Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.
Combining Mobile Crowdsensing and Ecological Momentary Assessments in the Healthcare Domain (2020)
Kraft, Robin ; Schlee, Winfried ; Stach, Michael ; Reichert, Manfred ; Langguth, Berthold ; Baumeister, Harald ; Probst, Thomas ; Hannemann, Ronny ; Pryss, Rüdiger
The increasing prevalence of smart mobile devices (e.g., smartphones) enables the combined use of mobile crowdsensing (MCS) and ecological momentary assessments (EMA) in the healthcare domain. By correlating qualitative longitudinal and ecologically valid EMA assessment data sets with sensor measurements in mobile apps, new valuable insights about patients (e.g., humans who suffer from chronic diseases) can be gained. However, there are numerous conceptual, architectural and technical, as well as legal challenges when implementing a respective software solution. Therefore, the work at hand (1) identifies these challenges, (2) derives respective recommendations, and (3) proposes a reference architecture for a MCS-EMA-platform addressing the defined recommendations. The required insights to propose the reference architecture were gained in several large-scale mHealth crowdsensing studies running for many years and different healthcare questions. To mention only two examples, we are running crowdsensing studies on questions for the tinnitus chronic disorder or psychological stress. We consider the proposed reference architecture and the identified challenges and recommendations as a contribution in two respects. First, they enable other researchers to align our practical studies with a baseline setting that can satisfy the variously revealed insights. Second, they are a proper basis to better compare data that was gathered using MCS and EMA. In addition, the combined use of MCS and EMA increasingly requires suitable architectures and associated digital solutions for the healthcare domain.
Process-driven and flow-based processing of industrial sensor data (2020)
Kammerer, Klaus ; Pryss, Rüdiger ; Hoppenstedt, Burkhard ; Sommer, Kevin ; Reichert, Manfred
For machine manufacturing companies, besides the production of high quality and reliable machines, requirements have emerged to maintain machine-related aspects through digital services. The development of such services in the field of the Industrial Internet of Things (IIoT) is dealing with solutions such as effective condition monitoring and predictive maintenance. However, appropriate data sources are needed on which digital services can be technically based. As many powerful and cheap sensors have been introduced over the last years, their integration into complex machines is promising for developing digital services for various scenarios. It is apparent that for components handling recorded data of these sensors they must usually deal with large amounts of data. In particular, the labeling of raw sensor data must be furthered by a technical solution. To deal with these data handling challenges in a generic way, a sensor processing pipeline (SPP) was developed, which provides effective methods to capture, process, store, and visualize raw sensor data based on a processing chain. Based on the example of a machine manufacturing company, the SPP approach is presented in this work. For the company involved, the approach has revealed promising results.
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