TY - JOUR A1 - Kammerer, Klaus A1 - Hoppenstedt, Burkhard A1 - Pryss, Rüdiger A1 - Stökler, Steffen A1 - Allgaier, Johannes A1 - Reichert, Manfred T1 - Anomaly Detections for Manufacturing Systems Based on Sensor Data—Insights into Two Challenging Real-World Production Settings JF - Sensors N2 - 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. KW - anomaly detection KW - sensor data KW - machine learning KW - production machines Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193885 SN - 1424-8220 VL - 19 IS - 24 ER - TY - JOUR A1 - Beierle, Felix A1 - Schobel, Johannes A1 - Vogel, Carsten A1 - Allgaier, Johannes A1 - Mulansky, Lena A1 - Haug, Fabian A1 - Haug, Julian A1 - Schlee, Winfried A1 - Holfelder, Marc A1 - Stach, Michael A1 - Schickler, Marc A1 - Baumeister, Harald A1 - Cohrdes, Caroline A1 - Deckert, Jürgen A1 - Deserno, Lorenz A1 - Edler, Johanna-Sophie A1 - Eichner, Felizitas A. A1 - Greger, Helmut A1 - Hein, Grit A1 - Heuschmann, Peter A1 - John, Dennis A1 - Kestler, Hans A. A1 - Krefting, Dagmar A1 - Langguth, Berthold A1 - Meybohm, Patrick A1 - Probst, Thomas A1 - Reichert, Manfred A1 - Romanos, Marcel A1 - Störk, Stefan A1 - Terhorst, Yannik A1 - Weiß, Martin A1 - Pryss, Rüdiger T1 - Corona Health — A Study- and Sensor-Based Mobile App Platform Exploring Aspects of the COVID-19 Pandemic JF - International Journal of Environmental Research and Public Health N2 - 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. KW - mobile health KW - ecological momentary assessment KW - digital phenotyping KW - longitudinal studies KW - mobile crowdsensing Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-242658 SN - 1660-4601 VL - 18 IS - 14 ER - TY - JOUR A1 - Allgaier, Johannes A1 - Schlee, Winfried A1 - Langguth, Berthold A1 - Probst, Thomas A1 - Pryss, Rüdiger T1 - Predicting the Gender of Individuals with Tinnitus based on Daily Life Data of the TrackYourTinnitus mHealth Platform JF - Scientific Reports N2 - 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. KW - computer science KW - machine learning KW - psychology KW - signs and symptoms Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-261753 VL - 11 IS - 1 ER - TY - JOUR A1 - Allgaier, Johannes A1 - Schlee, Winfried A1 - Probst, Thomas A1 - Pryss, Rüdiger T1 - Prediction of tinnitus perception based on daily life mHealth data using country origin and season JF - Journal of Clinical Medicine N2 - Tinnitus is an auditory phantom perception without external sound stimuli. This chronic perception can severely affect quality of life. Because tinnitus symptoms are highly heterogeneous, multimodal data analyses are increasingly used to gain new insights. MHealth data sources, with their particular focus on country- and season-specific differences, can provide a promising avenue for new insights. Therefore, we examined data from the TrackYourTinnitus (TYT) mHealth platform to create symptom profiles of TYT users. We used gradient boosting engines to classify momentary tinnitus and regress tinnitus loudness, using country of origin and season as features. At the daily assessment level, tinnitus loudness can be regressed with a mean absolute error rate of 7.9% points. In turn, momentary tinnitus can be classified with an F1 score of 93.79%. Both results indicate differences in the tinnitus of TYT users with respect to season and country of origin. The significance of the features was evaluated using statistical and explainable machine learning methods. It was further shown that tinnitus varies with temperature in certain countries. The results presented show that season and country of origin appear to be valuable features when combined with longitudinal mHealth data at the level of daily assessment. KW - tinnitus KW - gradient boosting machine KW - mobile health KW - machine learning KW - multimodal data KW - explainable machine learning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281812 SN - 2077-0383 VL - 11 IS - 15 ER -