Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen OPUS4-57638 Wissenschaftlicher Artikel Chromik, Jonas; Kirsten, Kristina; Herdick, Arne; Kappattanavar, Arpita Mallikarjuna; Arnrich, Bert SensorHub Observational studies are an important tool for determining whether the findings from controlled experiments can be transferred into scenarios that are closer to subjects' real-life circumstances. A rigorous approach to observational studies involves collecting data from different sensors to comprehensively capture the situation of the subject. However, this leads to technical difficulties especially if the sensors are from different manufacturers, as multiple data collection tools have to run simultaneously. We present SensorHub, a system that can collect data from various wearable devices from different manufacturers, such as inertial measurement units, portable electrocardiographs, portable electroencephalographs, portable photoplethysmographs, and sensors for electrodermal activity. Additionally, our tool offers the possibility to include ecological momentary assessments (EMAs) in studies. Hence, SensorHub enables multimodal sensor data collection under real-world conditions and allows direct user feedback to be collected through questionnaires, enabling studies at home. In a first study with 11 participants, we successfully used SensorHub to record multiple signals with different devices and collected additional information with the help of EMAs. In addition, we evaluated SensorHub's technical capabilities in several trials with up to 21 participants recording simultaneously using multiple sensors with sampling frequencies as high as 1000 Hz. We could show that although there is a theoretical limitation to the transmissible data rate, in practice this limitation is not an issue and data loss is rare. We conclude that with modern communication protocols and with the increasingly powerful smartphones and wearables, a system like our SensorHub establishes an interoperability framework to adequately combine consumer-grade sensing hardware which enables observational studies in real life. Basel MDPI 2022 18 Sensors 22 1 10.3390/s22010408 Hasso-Plattner-Institut für Digital Engineering gGmbH OPUS4-58100 Wissenschaftlicher Artikel Hecker, Pascal; Steckhan, Nico; Eyben, Florian; Schuller, Björn Wolfgang; Arnrich, Bert Voice Analysis for Neurological Disorder Recognition - A Systematic Review and Perspective on Emerging Trends Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance. Lausanne, Schweiz Frontiers Media SA 2022 16 Frontiers in Digital Health 10.3389/fdgth.2022.842301 Extern OPUS4-58133 Wissenschaftlicher Artikel Ziegler, Joceline; Pfitzner, Bjarne; Schulz, Heinrich; Saalbach, Axel; Arnrich, Bert Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.940.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}�∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.940.94 for ε=6�=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.760.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training. 14 Basel, Schweiz MDPI 2022 25 Sensors 22 10.3390/s22145195 Extern OPUS4-50628 misc Hernandez, Netzahualcoyotl; Demiray, Burcu; Arnrich, Bert; Favela, Jesus An Exploratory Study to Detect Temporal Orientation Using Bluetooth's sensor Mobile sensing technology allows us to investigate human behaviour on a daily basis. In the study, we examined temporal orientation, which refers to the capacity of thinking or talking about personal events in the past and future. We utilise the mksense platform that allows us to use the experience-sampling method. Individual's thoughts and their relationship with smartphone's Bluetooth data is analysed to understand in which contexts people are influenced by social environments, such as the people they spend the most time with. As an exploratory study, we analyse social condition influence through a collection of Bluetooth data and survey information from participant's smartphones. Preliminary results show that people are likely to focus on past events when interacting with close-related people, and focus on future planning when interacting with strangers. Similarly, people experience present temporal orientation when accompanied by known people. We believe that these findings are linked to emotions since, in its most basic state, emotion is a state of physiological arousal combined with an appropriated cognition. In this contribution, we envision a smartphone application for automatically inferring human emotions based on user's temporal orientation by using Bluetooth sensors, we briefly elaborate on the influential factor of temporal orientation episodes and conclude with a discussion and lessons learned. New York Association for Computing Machinery 2019 6 PervasiveHealth'19: Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare 978-1-4503-6126-2 292 297 10.1145/3329189.3329223 Hasso-Plattner-Institut für Digital Engineering gGmbH OPUS4-48778 Wissenschaftlicher Artikel Konak, Orhan; Wegner, Pit; Arnrich, Bert IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns. Basel MDPI 2020 15 Sensors 20 24 10.3390/s20247179 Hasso-Plattner-Institut für Digital Engineering GmbH OPUS4-57284 Wissenschaftlicher Artikel Chromik, Jonas; Pirl, Lukas; Beilharz, Jossekin Jakob; Arnrich, Bert; Polze, Andreas Certainty in QRS detection with artificial neural networks Detection of the QRS complex is a long-standing topic in the context of electrocardiography and many algorithms build upon the knowledge of the QRS positions. Although the first solutions to this problem were proposed in the 1970s and 1980s, there is still potential for improvements. Advancements in neural network technology made in recent years also lead to the emergence of enhanced QRS detectors based on artificial neural networks. In this work, we propose a method for assessing the certainty that is in each of the detected QRS complexes, i.e. how confident the QRS detector is that there is, in fact, a QRS complex in the position where it was detected. We further show how this metric can be utilised to distinguish correctly detected QRS complexes from false detections. Oxford Elsevier 2021 12 Biomedical signal processing and control 68 10.1016/j.bspc.2021.102628 Hasso-Plattner-Institut für Digital Engineering gGmbH OPUS4-58082 Wissenschaftlicher Artikel Pfitzner, Bjarne; Steckhan, Nico; Arnrich, Bert Federated learning in a medical context Data privacy is a very important issue. Especially in fields like medicine, it is paramount to abide by the existing privacy regulations to preserve patients' anonymity. However, data is required for research and training machine learning models that could help gain insight into complex correlations or personalised treatments that may otherwise stay undiscovered. Those models generally scale with the amount of data available, but the current situation often prohibits building large databases across sites. So it would be beneficial to be able to combine similar or related data from different sites all over the world while still preserving data privacy. Federated learning has been proposed as a solution for this, because it relies on the sharing of machine learning models, instead of the raw data itself. That means private data never leaves the site or device it was collected on. Federated learning is an emerging research area, and many domains have been identified for the application of those methods. This systematic literature review provides an extensive look at the concept of and research into federated learning and its applicability for confidential healthcare datasets. New York Association for Computing Machinery 2021 31 ACM transactions on internet technology : TOIT / Association for Computing 21 2 1 31 10.1145/3412357 Hasso-Plattner-Institut für Digital Engineering gGmbH OPUS4-48779 misc Konak, Orhan; Wegner, Pit; Arnrich, Bert IMU-Based Movement Trajectory Heatmaps for Human Activity Recognition Recent trends in ubiquitous computing have led to a proliferation of studies that focus on human activity recognition (HAR) utilizing inertial sensor data that consist of acceleration, orientation and angular velocity. However, the performances of such approaches are limited by the amount of annotated training data, especially in fields where annotating data is highly time-consuming and requires specialized professionals, such as in healthcare. In image classification, this limitation has been mitigated by powerful oversampling techniques such as data augmentation. Using this technique, this work evaluates to what extent transforming inertial sensor data into movement trajectories and into 2D heatmap images can be advantageous for HAR when data are scarce. A convolutional long short-term memory (ConvLSTM) network that incorporates spatiotemporal correlations was used to classify the heatmap images. Evaluation was carried out on Deep Inertial Poser (DIP), a known dataset composed of inertial sensor data. The results obtained suggest that for datasets with large numbers of subjects, using state-of-the-art methods remains the best alternative. However, a performance advantage was achieved for small datasets, which is usually the case in healthcare. Moreover, movement trajectories provide a visual representation of human activities, which can help researchers to better interpret and analyze motion patterns. 2021 17 Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät 4 urn:nbn:de:kobv:517-opus4-487799 10.25932/publishup-48779 Hasso-Plattner-Institut für Digital Engineering GmbH OPUS4-58132 misc Ziegler, Joceline; Pfitzner, Bjarne; Schulz, Heinrich; Saalbach, Axel; Arnrich, Bert Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-ray Data Privacy regulations and the physical distribution of heterogeneous data are often primary concerns for the development of deep learning models in a medical context. This paper evaluates the feasibility of differentially private federated learning for chest X-ray classification as a defense against data privacy attacks. To the best of our knowledge, we are the first to directly compare the impact of differentially private training on two different neural network architectures, DenseNet121 and ResNet50. Extending the federated learning environments previously analyzed in terms of privacy, we simulated a heterogeneous and imbalanced federated setting by distributing images from the public CheXpert and Mendeley chest X-ray datasets unevenly among 36 clients. Both non-private baseline models achieved an area under the receiver operating characteristic curve (AUC) of 0.940.94 on the binary classification task of detecting the presence of a medical finding. We demonstrate that both model architectures are vulnerable to privacy violation by applying image reconstruction attacks to local model updates from individual clients. The attack was particularly successful during later training stages. To mitigate the risk of a privacy breach, we integrated Rényi differential privacy with a Gaussian noise mechanism into local model training. We evaluate model performance and attack vulnerability for privacy budgets ε∈{1,3,6,10}�∈{1,3,6,10}. The DenseNet121 achieved the best utility-privacy trade-off with an AUC of 0.940.94 for ε=6�=6. Model performance deteriorated slightly for individual clients compared to the non-private baseline. The ResNet50 only reached an AUC of 0.760.76 in the same privacy setting. Its performance was inferior to that of the DenseNet121 for all considered privacy constraints, suggesting that the DenseNet121 architecture is more robust to differentially private training. 2022 25 Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät 14 urn:nbn:de:kobv:517-opus4-581322 10.25932/publishup-58132 Hasso-Plattner-Institut für Digital Engineering GmbH OPUS4-57860 Bericht Döllner, Jürgen Roland Friedrich; Friedrich, Tobias; Arnrich, Bert; Hirschfeld, Robert; Lippert, Christoph; Meinel, Christoph Abschlussbericht KI-Labor ITSE Der Abschlussbericht beschreibt Aufgaben und Ergebnisse des KI-Labors "ITSE". Gegenstand des KI-Labors bildeten Methodik, Technik und Ausbildung in der IT-Systemtechnik zur Analyse, Planung und Konstruktion KI-basierter, komplexer IT-Systeme. 2022 60 urn:nbn:de:kobv:517-opus4-578604 10.25932/publishup-57860 Hasso-Plattner-Institut für Digital Engineering GmbH OPUS4-58101 misc Hecker, Pascal; Steckhan, Nico; Eyben, Florian; Schuller, Björn Wolfgang; Arnrich, Bert Voice Analysis for Neurological Disorder Recognition - A Systematic Review and Perspective on Emerging Trends Quantifying neurological disorders from voice is a rapidly growing field of research and holds promise for unobtrusive and large-scale disorder monitoring. The data recording setup and data analysis pipelines are both crucial aspects to effectively obtain relevant information from participants. Therefore, we performed a systematic review to provide a high-level overview of practices across various neurological disorders and highlight emerging trends. PRISMA-based literature searches were conducted through PubMed, Web of Science, and IEEE Xplore to identify publications in which original (i.e., newly recorded) datasets were collected. Disorders of interest were psychiatric as well as neurodegenerative disorders, such as bipolar disorder, depression, and stress, as well as amyotrophic lateral sclerosis amyotrophic lateral sclerosis, Alzheimer's, and Parkinson's disease, and speech impairments (aphasia, dysarthria, and dysphonia). Of the 43 retrieved studies, Parkinson's disease is represented most prominently with 19 discovered datasets. Free speech and read speech tasks are most commonly used across disorders. Besides popular feature extraction toolkits, many studies utilise custom-built feature sets. Correlations of acoustic features with psychiatric and neurodegenerative disorders are presented. In terms of analysis, statistical analysis for significance of individual features is commonly used, as well as predictive modeling approaches, especially with support vector machines and a small number of artificial neural networks. An emerging trend and recommendation for future studies is to collect data in everyday life to facilitate longitudinal data collection and to capture the behavior of participants more naturally. Another emerging trend is to record additional modalities to voice, which can potentially increase analytical performance. 2022 16 Zweitveröffentlichungen der Universität Potsdam : Reihe der Digital Engineering Fakultät 13 urn:nbn:de:kobv:517-opus4-581019 10.25932/publishup-58101 Hasso-Plattner-Institut für Digital Engineering GmbH OPUS4-52201 Wissenschaftlicher Artikel Trautmann, Justin; Zhou, Lin; Brahms, Clemens Markus; Tunca, Can; Ersoy, Cem; Granacher, Urs; Arnrich, Bert TRIPOD Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data. Basel MDPI 2021 19 Data : open access ʻData in scienceʼ journal 6 9 10.3390/data6090095 Department Sport- und Gesundheitswissenschaften OPUS4-52202 misc Trautmann, Justin; Zhou, Lin; Brahms, Clemens Markus; Tunca, Can; Ersoy, Cem; Granacher, Urs; Arnrich, Bert TRIPOD - A Treadmill Walking Dataset with IMU, Pressure-distribution and Photoelectric Data for Gait Analysis Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data. 2021 21 Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät 6 urn:nbn:de:kobv:517-opus4-522027 10.25932/publishup-52202 Hasso-Plattner-Institut für Digital Engineering GmbH OPUS4-48413 misc Albert, Justin Amadeus; Owolabi, Victor; Gebel, Arnd; Brahms, Clemens Markus; Granacher, Urs; Arnrich, Bert Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people. 2020 24 Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät 3 urn:nbn:de:kobv:517-opus4-484130 10.25932/publishup-48413 Department Sport- und Gesundheitswissenschaften OPUS4-48412 Wissenschaftlicher Artikel Albert, Justin Amadeus; Owolabi, Victor; Gebel, Arnd; Brahms, Clemens Markus; Granacher, Urs; Arnrich, Bert Evaluation of the Pose Tracking Performance of the Azure Kinect and Kinect v2 for Gait Analysis in Comparison with a Gold Standard Gait analysis is an important tool for the early detection of neurological diseases and for the assessment of risk of falling in elderly people. The availability of low-cost camera hardware on the market today and recent advances in Machine Learning enable a wide range of clinical and health-related applications, such as patient monitoring or exercise recognition at home. In this study, we evaluated the motion tracking performance of the latest generation of the Microsoft Kinect camera, Azure Kinect, compared to its predecessor Kinect v2 in terms of treadmill walking using a gold standard Vicon multi-camera motion capturing system and the 39 marker Plug-in Gait model. Five young and healthy subjects walked on a treadmill at three different velocities while data were recorded simultaneously with all three camera systems. An easy-to-administer camera calibration method developed here was used to spatially align the 3D skeleton data from both Kinect cameras and the Vicon system. With this calibration, the spatial agreement of joint positions between the two Kinect cameras and the reference system was evaluated. In addition, we compared the accuracy of certain spatio-temporal gait parameters, i.e., step length, step time, step width, and stride time calculated from the Kinect data, with the gold standard system. Our results showed that the improved hardware and the motion tracking algorithm of the Azure Kinect camera led to a significantly higher accuracy of the spatial gait parameters than the predecessor Kinect v2, while no significant differences were found between the temporal parameters. Furthermore, we explain in detail how this experimental setup could be used to continuously monitor the progress during gait rehabilitation in older people. Basel MDPI 2020 22 Sensors 20 18 10.3390/s20185104 Hasso-Plattner-Institut für Digital Engineering GmbH OPUS4-48162 misc Zhou, Lin; Fischer, Eric; Tunca, Can; Brahms, Clemens Markus; Ersoy, Cem; Granacher, Urs; Arnrich, Bert How We Found Our IMU Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data. 2020 31 Postprints der Universität Potsdam : Reihe der Digital Engineering Fakultät 2 urn:nbn:de:kobv:517-opus4-481628 10.25932/publishup-48162 Hasso-Plattner-Institut für Digital Engineering GmbH OPUS4-48161 Wissenschaftlicher Artikel Zhou, Lin; Fischer, Eric; Tunca, Can; Brahms, Clemens Markus; Ersoy, Cem; Granacher, Urs; Arnrich, Bert How We Found Our IMU Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data. Basel MDPI 2020 29 Sensors 20 15 10.3390/s20154090 Hasso-Plattner-Institut für Digital Engineering GmbH OPUS4-50413 Buch (Monographie) Meinel, Christoph; Döllner, Jürgen Roland Friedrich; Weske, Mathias; Polze, Andreas; Hirschfeld, Robert; Naumann, Felix; Giese, Holger; Baudisch, Patrick; Friedrich, Tobias; Böttinger, Erwin; Lippert, Christoph; Dörr, Christian; Lehmann, Anja; Renard, Bernhard; Rabl, Tilmann; Uebernickel, Falk; Arnrich, Bert; Hölzle, Katharina Proceedings of the HPI Research School on Service-oriented Systems Engineering 2020 Fall Retreat Design and Implementation of service-oriented architectures imposes a huge number of research questions from the fields of software engineering, system analysis and modeling, adaptability, and application integration. Component orientation and web services are two approaches for design and realization of complex web-based system. Both approaches allow for dynamic application adaptation as well as integration of enterprise application. Service-Oriented Systems Engineering represents a symbiosis of best practices in object-orientation, component-based development, distributed computing, and business process management. It provides integration of business and IT concerns. The annual Ph.D. Retreat of the Research School provides each member the opportunity to present his/her current state of their research and to give an outline of a prospective Ph.D. thesis. Due to the interdisciplinary structure of the research school, this technical report covers a wide range of topics. These include but are not limited to: Human Computer Interaction and Computer Vision as Service; Service-oriented Geovisualization Systems; Algorithm Engineering for Service-oriented Systems; Modeling and Verification of Self-adaptive Service-oriented Systems; Tools and Methods for Software Engineering in Service-oriented Systems; Security Engineering of Service-based IT Systems; Service-oriented Information Systems; Evolutionary Transition of Enterprise Applications to Service Orientation; Operating System Abstractions for Service-oriented Computing; and Services Specification, Composition, and Enactment. Potsdam Universitätsverlag Potsdam 2021 vi, 144 978-3-86956-513-2 138 urn:nbn:de:kobv:517-opus4-504132 10.25932/publishup-50413 Hasso-Plattner-Institut für Digital Engineering GmbH