@article{ChromikKirstenHerdicketal.2022,
  author    = {Chromik, Jonas and Kirsten, Kristina and Herdick, Arne and Kappattanavar, Arpita Mallikarjuna and Arnrich, Bert},
  title     = {SensorHub},
  series = {Sensors},
  volume    = {22},
  journal   = {Sensors},
  number    = {1},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {1424-8220},
  doi       = {10.3390/s22010408},
  pages     = {18},
  year      = {2022},
  abstract  = {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.},
  language  = {en}
}
@book{WeyandChromikWolfetal.2017,
  author    = {Weyand, Christopher and Chromik, Jonas and Wolf, Lennard and K{\"o}tte, Steffen and Haase, Konstantin and Felgentreff, Tim and Lincke, Jens and Hirschfeld, Robert},
  title     = {Improving hosted continuous integration services},
  number    = {108},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-377-0},
  issn      = {1613-5652},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-94251},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {viii, 114},
  year      = {2017},
  abstract  = {Developing large software projects is a complicated task and can be demanding for developers. Continuous integration is common practice for reducing complexity. By integrating and testing changes often, changesets are kept small and therefore easily comprehensible. Travis CI is a service that offers continuous integration and continuous deployment in the cloud. Software projects are build, tested, and deployed using the Travis CI infrastructure without interrupting the development process. This report describes how Travis CI works, presents how time-driven, periodic building is implemented as well as how CI data visualization can be done, and proposes a way of dealing with dependency problems.},
  language  = {en}
}
@article{ChromikPirlBeilharzetal.2021,
  author    = {Chromik, Jonas and Pirl, Lukas and Beilharz, Jossekin Jakob and Arnrich, Bert and Polze, Andreas},
  title     = {Certainty in QRS detection with artificial neural networks},
  series = {Biomedical signal processing and control},
  volume    = {68},
  journal   = {Biomedical signal processing and control},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {1746-8094},
  doi       = {10.1016/j.bspc.2021.102628},
  pages     = {12},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}