@article{KammererHoppenstedtPryssetal.2019, author = {Kammerer, Klaus and Hoppenstedt, Burkhard and Pryss, R{\"u}diger and St{\"o}kler, Steffen and Allgaier, Johannes and Reichert, Manfred}, title = {Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings}, series = {Sensors}, volume = {19}, journal = {Sensors}, number = {24}, issn = {1424-8220}, doi = {10.3390/s19245370}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-193885}, pages = {5370}, year = {2019}, abstract = {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.}, language = {en} } @article{SchicklerReichertGeigeretal.2020, author = {Schickler, Marc and Reichert, Manfred and Geiger, Philip and Winkler, Jens and Funk, Thomas and Weilbach, Micha and Pryss, R{\"u}diger}, title = {Flexible development of location-based mobile augmented reality applications with AREA}, series = {Journal of Ambient Intelligence and Humanized Computing}, volume = {11}, journal = {Journal of Ambient Intelligence and Humanized Computing}, issn = {1868-5137}, doi = {10.1007/s12652-020-02094-9}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-232773}, pages = {5809-5824}, year = {2020}, abstract = {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.}, 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{HelmerRodemersHottenrottetal.2023, author = {Helmer, Philipp and Rodemers, Philipp and Hottenrott, Sebastian and Leppich, Robert and Helwich, Maja and Pryss, R{\"u}diger and Kranke, Peter and Meybohm, Patrick and Winkler, Bernd E. and Sammeth, Michael}, title = {Evaluating blood oxygen saturation measurements by popular fitness trackers in postoperative patients: a prospective clinical trial}, series = {iScience}, volume = {26}, journal = {iScience}, number = {11}, issn = {2589-0042}, doi = {10.1016/j.isci.2023.108155}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-349913}, year = {2023}, abstract = {Summary Blood oxygen saturation is an important clinical parameter, especially in postoperative hospitalized patients, monitored in clinical practice by arterial blood gas (ABG) and/or pulse oximetry that both are not suitable for a long-term continuous monitoring of patients during the entire hospital stay, or beyond. Technological advances developed recently for consumer-grade fitness trackers could—at least in theory—help to fill in this gap, but benchmarks on the applicability and accuracy of these technologies in hospitalized patients are currently lacking. We therefore conducted at the postanaesthesia care unit under controlled settings a prospective clinical trial with 201 patients, comparing in total >1,000 oxygen blood saturation measurements by fitness trackers of three brands with the ABG gold standard and with pulse oximetry. Our results suggest that, despite of an overall still tolerable measuring accuracy, comparatively high dropout rates severely limit the possibilities of employing fitness trackers, particularly during the immediate postoperative period of hospitalized patients. Highlights •The accuracy of O2 measurements by fitness trackers is tolerable (RMSE ≲4\%) •Correlation with arterial blood gas measurements is fair to moderate (PCC = [0.46; 0.64]) •Dropout rates of fitness trackers during O2 monitoring are high (∼1/3 values missing) •Fitness trackers cannot be recommended for O2 measuring during critical monitoring}, language = {en} } @article{BeierleSchobelVogeletal.2021, author = {Beierle, Felix and Schobel, Johannes and Vogel, Carsten and Allgaier, Johannes and Mulansky, Lena and Haug, Fabian and Haug, Julian and Schlee, Winfried and Holfelder, Marc and Stach, Michael and Schickler, Marc and Baumeister, Harald and Cohrdes, Caroline and Deckert, J{\"u}rgen and Deserno, Lorenz and Edler, Johanna-Sophie and Eichner, Felizitas A. and Greger, Helmut and Hein, Grit and Heuschmann, Peter and John, Dennis and Kestler, Hans A. and Krefting, Dagmar and Langguth, Berthold and Meybohm, Patrick and Probst, Thomas and Reichert, Manfred and Romanos, Marcel and St{\"o}rk, Stefan and Terhorst, Yannik and Weiß, Martin and Pryss, R{\"u}diger}, title = {Corona Health — A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic}, series = {International Journal of Environmental Research and Public Health}, volume = {18}, journal = {International Journal of Environmental Research and Public Health}, number = {14}, issn = {1660-4601}, doi = {10.3390/ijerph18147395}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-242658}, year = {2021}, abstract = {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.}, language = {en} } @article{HelmerHottenrottRodemersetal.2022, author = {Helmer, Philipp and Hottenrott, Sebastian and Rodemers, Philipp and Leppich, Robert and Helwich, Maja and Pryss, R{\"u}diger and Kranke, Peter and Meybohm, Patrick and Winkler, Bernd E. and Sammeth, Michael}, title = {Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial}, series = {Journal of Medical Internet Research}, volume = {24}, journal = {Journal of Medical Internet Research}, number = {12}, doi = {10.2196/42359}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-299679}, year = {2022}, abstract = {Background: Over the recent years, technological advances of wrist-worn fitness trackers heralded a new era in the continuous monitoring of vital signs. So far, these devices have primarily been used for sports. Objective: However, for using these technologies in health care, further validations of the measurement accuracy in hospitalized patients are essential but lacking to date. Methods: We conducted a prospective validation study with 201 patients after moderate to major surgery in a controlled setting to benchmark the accuracy of heart rate measurements in 4 consumer-grade fitness trackers (Apple Watch 7, Garmin Fenix 6 Pro, Withings ScanWatch, and Fitbit Sense) against the clinical gold standard (electrocardiography). Results: All devices exhibited high correlation (r≥0.95; P<.001) and concordance (rc≥0.94) coefficients, with a relative error as low as mean absolute percentage error <5\% based on 1630 valid measurements. We identified confounders significantly biasing the measurement accuracy, although not at clinically relevant levels (mean absolute error<5 beats per minute). Conclusions: Consumer-grade fitness trackers appear promising in hospitalized patients for monitoring heart rate.}, language = {en} } @article{BeierlePryssAizawa2023, author = {Beierle, Felix and Pryss, R{\"u}diger and Aizawa, Akiko}, title = {Sentiments about mental health on Twitter — before and during the COVID-19 pandemic}, series = {Healthcare}, volume = {11}, journal = {Healthcare}, number = {21}, issn = {2227-9032}, doi = {10.3390/healthcare11212893}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-355192}, year = {2023}, abstract = {During the COVID-19 pandemic, the novel coronavirus had an impact not only on public health but also on the mental health of the population. Public sentiment on mental health and depression is often captured only in small, survey-based studies, while work based on Twitter data often only looks at the period during the pandemic and does not make comparisons with the pre-pandemic situation. We collected tweets that included the hashtags \#MentalHealth and \#Depression from before and during the pandemic (8.5 months each). We used LDA (Latent Dirichlet Allocation) for topic modeling and LIWC, VADER, and NRC for sentiment analysis. We used three machine-learning classifiers to seek evidence regarding an automatically detectable change in tweets before vs. during the pandemic: (1) based on TF-IDF values, (2) based on the values from the sentiment libraries, (3) based on tweet content (deep-learning BERT classifier). Topic modeling revealed that Twitter users who explicitly used the hashtags \#Depression and especially \#MentalHealth did so to raise awareness. We observed an overall positive sentiment, and in tough times such as during the COVID-19 pandemic, tweets with \#MentalHealth were often associated with gratitude. Among the three classification approaches, the BERT classifier showed the best performance, with an accuracy of 81\% for \#MentalHealth and 79\% for \#Depression. Although the data may have come from users familiar with mental health, these findings can help gauge public sentiment on the topic. The combination of (1) sentiment analysis, (2) topic modeling, and (3) tweet classification with machine learning proved useful in gaining comprehensive insight into public sentiment and could be applied to other data sources and topics.}, language = {en} } @article{WinterPryssProbstetal.2021, author = {Winter, Michael and Pryss, R{\"u}diger and Probst, Thomas and Reichert, Manfred}, title = {Applying Eye Movement Modeling Examples to guide novices' attention in the comprehension of process models}, series = {Brain Sciences}, volume = {11}, journal = {Brain Sciences}, number = {1}, issn = {2076-3425}, doi = {10.3390/brainsci11010072}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-222966}, year = {2021}, abstract = {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.}, language = {en} } @article{SommerAmrBavendieketal.2022, author = {Sommer, Kim K. and Amr, Ali and Bavendiek, Udo and Beierle, Felix and Brunecker, Peter and Dathe, Henning and Eils, J{\"u}rgen and Ertl, Maximilian and Fette, Georg and Gietzelt, Matthias and Heidecker, Bettina and Hellenkamp, Kristian and Heuschmann, Peter and Hoos, Jennifer D. E. and Keszty{\"u}s, Tibor and Kerwagen, Fabian and Kindermann, Aljoscha and Krefting, Dagmar and Landmesser, Ulf and Marschollek, Michael and Meder, Benjamin and Merzweiler, Angela and Prasser, Fabian and Pryss, R{\"u}diger and Richter, Jendrik and Schneider, Philipp and St{\"o}rk, Stefan and Dieterich, Christoph}, title = {Structured, harmonized, and interoperable integration of clinical routine data to compute heart failure risk scores}, series = {Life}, volume = {12}, journal = {Life}, number = {5}, issn = {2075-1729}, doi = {10.3390/life12050749}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-275239}, year = {2022}, abstract = {Risk prediction in patients with heart failure (HF) is essential to improve the tailoring of preventive, diagnostic, and therapeutic strategies for the individual patient, and effectively use health care resources. Risk scores derived from controlled clinical studies can be used to calculate the risk of mortality and HF hospitalizations. However, these scores are poorly implemented into routine care, predominantly because their calculation requires considerable efforts in practice and necessary data often are not available in an interoperable format. In this work, we demonstrate the feasibility of a multi-site solution to derive and calculate two exemplary HF scores from clinical routine data (MAGGIC score with six continuous and eight categorical variables; Barcelona Bio-HF score with five continuous and six categorical variables). Within HiGHmed, a German Medical Informatics Initiative consortium, we implemented an interoperable solution, collecting a harmonized HF-phenotypic core data set (CDS) within the openEHR framework. Our approach minimizes the need for manual data entry by automatically retrieving data from primary systems. We show, across five participating medical centers, that the implemented structures to execute dedicated data queries, followed by harmonized data processing and score calculation, work well in practice. In summary, we demonstrated the feasibility of clinical routine data usage across multiple partner sites to compute HF risk scores. This solution can be extended to a large spectrum of applications in clinical care.}, language = {en} } @article{PryssSchleeHoppenstedtetal.2020, author = {Pryss, R{\"u}diger and Schlee, Winfried and Hoppenstedt, Burkhard and Reichert, Manfred and Spiliopoulou, Myra and Langguth, Berthold and Breitmayer, Marius and Probst, Thomas}, title = {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}, series = {Journal of Medical Internet Research}, volume = {22}, journal = {Journal of Medical Internet Research}, number = {6}, doi = {10.2196/15547}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229517}, year = {2020}, abstract = {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.}, language = {en} }