@article{PrakashUnnikrishnanPryssetal.2021, author = {Prakash, Subash and Unnikrishnan, Vishnu and Pryss, R{\"u}diger and Kraft, Robin and Schobel, Johannes and Hannemann, Ronny and Langguth, Berthold and Schlee, Winfried and Spiliopoulou, Myra}, title = {Interactive system for similarity-based inspection and assessment of the well-being of mHealth users}, series = {Entropy}, volume = {23}, journal = {Entropy}, number = {12}, issn = {1099-4300}, doi = {10.3390/e23121695}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252333}, year = {2021}, abstract = {Recent digitization technologies empower mHealth users to conveniently record their Ecological Momentary Assessments (EMA) through web applications, smartphones, and wearable devices. These recordings can help clinicians understand how the users' condition changes, but appropriate learning and visualization mechanisms are required for this purpose. We propose a web-based visual analytics tool, which processes clinical data as well as EMAs that were recorded through a mHealth application. The goals we pursue are (1) to predict the condition of the user in the near and the far future, while also identifying the clinical data that mostly contribute to EMA predictions, (2) to identify users with outlier EMA, and (3) to show to what extent the EMAs of a user are in line with or diverge from those users similar to him/her. We report our findings based on a pilot study on patient empowerment, involving tinnitus patients who recorded EMAs with the mHealth app TinnitusTips. To validate our method, we also derived synthetic data from the same pilot study. Based on this setting, results for different use cases are reported.}, language = {en} } @article{KraftBirkReichertetal.2020, author = {Kraft, Robin and Birk, Ferdinand and Reichert, Manfred and Deshpande, Aniruddha and Schlee, Winfried and Langguth, Berthold and Baumeister, Harald and Probst, Thomas and Spiliopoulou, Myra and Pryss, R{\"u}diger}, title = {Efficient processing of geospatial mHealth data using a scalable crowdsensing platform}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {12}, issn = {1424-8220}, doi = {10.3390/s20123456}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-207826}, year = {2020}, abstract = {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.}, language = {en} } @article{KammererPryssHoppenstedtetal.2020, author = {Kammerer, Klaus and Pryss, R{\"u}diger and Hoppenstedt, Burkhard and Sommer, Kevin and Reichert, Manfred}, title = {Process-driven and flow-based processing of industrial sensor data}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {18}, issn = {1424-8220}, doi = {10.3390/s20185245}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213089}, year = {2020}, abstract = {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.}, language = {en} } @article{KammererGoesterReichertetal.2021, author = {Kammerer, Klaus and G{\"o}ster, Manuel and Reichert, Manfred and Pryss, R{\"u}diger}, title = {Ambalytics: a scalable and distributed system architecture concept for bibliometric network analyses}, series = {Future Internet}, volume = {13}, journal = {Future Internet}, number = {8}, issn = {1999-5903}, doi = {10.3390/fi13080203}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-244916}, year = {2021}, abstract = {A deep understanding about a field of research is valuable for academic researchers. In addition to technical knowledge, this includes knowledge about subareas, open research questions, and social communities (networks) of individuals and organizations within a given field. With bibliometric analyses, researchers can acquire quantitatively valuable knowledge about a research area by using bibliographic information on academic publications provided by bibliographic data providers. Bibliometric analyses include the calculation of bibliometric networks to describe affiliations or similarities of bibliometric entities (e.g., authors) and group them into clusters representing subareas or communities. Calculating and visualizing bibliometric networks is a nontrivial and time-consuming data science task that requires highly skilled individuals. In addition to domain knowledge, researchers must often provide statistical knowledge and programming skills or use software tools having limited functionality and usability. In this paper, we present the ambalytics bibliometric platform, which reduces the complexity of bibliometric network analysis and the visualization of results. It accompanies users through the process of bibliometric analysis and eliminates the need for individuals to have programming skills and statistical knowledge, while preserving advanced functionality, such as algorithm parameterization, for experts. As a proof-of-concept, and as an example of bibliometric analyses outcomes, the calculation of research fronts networks based on a hybrid similarity approach is shown. Being designed to scale, ambalytics makes use of distributed systems concepts and technologies. It is based on the microservice architecture concept and uses the Kubernetes framework for orchestration. This paper presents the initial building block of a comprehensive bibliometric analysis platform called ambalytics, which aims at a high usability for users as well as scalability.}, language = {en} } @article{KraftReichertPryss2021, author = {Kraft, Robin and Reichert, Manfred and Pryss, R{\"u}diger}, title = {Towards the interpretation of sound measurements from smartphones collected with mobile crowdsensing in the healthcare domain: an experiment with Android devices}, series = {Sensors}, volume = {22}, journal = {Sensors}, number = {1}, issn = {1424-8220}, doi = {10.3390/s22010170}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-252246}, year = {2021}, abstract = {The ubiquity of mobile devices fosters the combined use of ecological momentary assessments (EMA) and mobile crowdsensing (MCS) in the field of healthcare. This combination not only allows researchers to collect ecologically valid data, but also to use smartphone sensors to capture the context in which these data are collected. The TrackYourTinnitus (TYT) platform uses EMA to track users' individual subjective tinnitus perception and MCS to capture an objective environmental sound level while the EMA questionnaire is filled in. However, the sound level data cannot be used directly among the different smartphones used by TYT users, since uncalibrated raw values are stored. This work describes an approach towards making these values comparable. In the described setting, the evaluation of sensor measurements from different smartphone users becomes increasingly prevalent. Therefore, the shown approach can be also considered as a more general solution as it not only shows how it helped to interpret TYT sound level data, but may also stimulate other researchers, especially those who need to interpret sensor data in a similar setting. Altogether, the approach will show that measuring sound levels with mobile devices is possible in healthcare scenarios, but there are many challenges to ensuring that the measured values are interpretable.}, language = {en} } @article{HolfelderMulanskySchleeetal.2021, author = {Holfelder, Marc and Mulansky, Lena and Schlee, Winfried and Baumeister, Harald and Schobel, Johannes and Greger, Helmut and Hoff, Andreas and Pryss, R{\"u}diger}, title = {Medical device regulation efforts for mHealth apps during the COVID-19 pandemic — an experience report of Corona Check and Corona Health}, series = {J — Multidisciplinary Scientific Journal}, volume = {4}, journal = {J — Multidisciplinary Scientific Journal}, number = {2}, issn = {2571-8800}, doi = {10.3390/j4020017}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-285434}, pages = {206 -- 222}, year = {2021}, abstract = {Within the healthcare environment, mobile health (mHealth) applications (apps) are becoming more and more important. The number of new mHealth apps has risen steadily in the last years. Especially the COVID-19 pandemic has led to an enormous amount of app releases. In most countries, mHealth applications have to be compliant with several regulatory aspects to be declared a "medical app". However, the latest applicable medical device regulation (MDR) does not provide more details on the requirements for mHealth applications. When developing a medical app, it is essential that all contributors in an interdisciplinary team — especially software engineers — are aware of the specific regulatory requirements beforehand. The development process, however, should not be stalled due to integration of the MDR. Therefore, a developing framework that includes these aspects is required to facilitate a reliable and quick development process. The paper at hand introduces the creation of such a framework on the basis of the Corona Health and Corona Check apps. The relevant regulatory guidelines are listed and summarized as a guidance for medical app developments during the pandemic and beyond. In particular, the important stages and challenges faced that emerged during the entire development process are highlighted.}, language = {en} } @article{KirikkayisGallikWinteretal.2023, author = {Kirikkayis, Yusuf and Gallik, Florian and Winter, Michael and Reichert, Manfred}, title = {BPMNE4IoT: a framework for modeling, executing and monitoring IoT-driven processes}, series = {Future Internet}, volume = {15}, journal = {Future Internet}, number = {3}, issn = {1999-5903}, doi = {10.3390/fi15030090}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-304097}, year = {2023}, abstract = {The Internet of Things (IoT) enables a variety of smart applications, including smart home, smart manufacturing, and smart city. By enhancing Business Process Management Systems with IoT capabilities, the execution and monitoring of business processes can be significantly improved. Providing a holistic support for modeling, executing and monitoring IoT-driven processes, however, constitutes a challenge. Existing process modeling and process execution languages, such as BPMN 2.0, are unable to fully meet the IoT characteristics (e.g., asynchronicity and parallelism) of IoT-driven processes. In this article, we present BPMNE4IoT—A holistic framework for modeling, executing and monitoring IoT-driven processes. We introduce various artifacts and events based on the BPMN 2.0 metamodel that allow realizing the desired IoT awareness of business processes. The framework is evaluated along two real-world scenarios from two different domains. Moreover, we present a user study for comparing BPMNE4IoT and BPMN 2.0. In particular, this study has confirmed that the BPMNE4IoT framework facilitates the support of IoT-driven processes.}, language = {en} }