Critical Systems Engineering Living Lab - Medical Process Modeling (CSE LL-MPM)

Researchers in the Living Lab - Medical Process Modeling develop research infrastructures for medical-specific questions. The Living Lab allows the acquisition and modeling of standardized time-critical processes, e.g. prehospital resuscitation (the Mega Code Training). The aim is the analysis of the quality of individual performance and the prediction of human behavior in a defined socio-technical system to optimize processes through human-machine interaction. The Living Lab CMP makes a significant contribution to the quantification, optimization, and standardization of medical processes.


In a first step, the necessary infrastructure is developed to model medical workflow (such as prehospital resuscitation) in typical safety-critical situations with a focus on functional properties such as timing, workloads, and attention.

For this purpose, relevant motion sequences of the participants must be monitored very precisely via both environmental sensors and inertial sensors (motion capture). The combination of these two different motion capture approaches avoids the weaknesses of the individual systems: Optical motion capture methods are, e.g. very sensitive to sunlight and inertial sensors become imprecise at the end of the kinematic tree (e.g. towards the hands). Furthermore, the cognitive workload and the viewing angles of the participants are measured and modeled.

Also, all activities that can be related to patients are recorded using an A(C)LS simulator and ECG and merged into an evaluation platform. Further, it will be investigated which sensor system is suitable for future decision support systems that can be utilized for field use. Based on the context information, processes are modeled about timing, workloads, and attention. In a second step, simulations will be designed to optimize the Mega Codes Training.

Grants and cooperations

The interdisciplinary research center for the design of safety-critical socio-technical systems investigates the role of humans in the control of complex transport systems on land and water. Cooperation partners are OFFIS e.V. in Oldenburg, DLR Institute for Traffic Systems Engineering in Braunschweig and the network SafeTRANS. Currently, the project is funded in the second phase by the state of Lower Saxony with EUR 2 million. The project runtime was extended by additional 18 months (2017-2018).


  • [article] bibtex
    S. Blum, S. Debener, R. Emkes, N. Volkening, S. Fudickar, and M. G. Bleichner, "EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone," , p. 12.
    @article{Blum2017, abstract = {Objective. Our aim was the development and validation of a modular signal processing and classification application enabling online electroencephalography (EEG) signal processing on off-the-shelf mobile Android devices. The software application SCALA (Signal ProCessing and CLassification on Android) supports a standardized communication interface to exchange information with external software and hardware. Approach. In order to implement a closed-loop brain-computer interface (BCI) on the smartphone, we used a multiapp framework, which integrates applications for stimulus presentation, data acquisition, data processing, classification, and delivery of feedback to the user. Main Results. We have implemented the open source signal processing application SCALA. We present timing test results supporting sufficient temporal precision of audio events. We also validate SCALA with a well-established auditory selective attention paradigm and report above chance level classification results for all participants. Regarding the 24-channel EEG signal quality, evaluation results confirm typical sound onset auditory evoked potentials as well as cognitive event-related potentials that differentiate between correct and incorrect task performance feedback. Significance. We present a fully smartphone-operated, modular closed-loop BCI system that can be combined with different EEG amplifiers and can easily implement other paradigms.},
      author = {Blum, Sarah and Debener, Stefan and Emkes, Reiner and Volkening, Nils and Fudickar, Sebastian and Bleichner, Martin G},
      doi = {10.1155/2017/3072870},
      file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Blum et al. - Unknown - EEG Recording and Online Signal Processing on Android A Multiapp Framework for Brain-Computer Interfaces on Smar.pdf:pdf},
      journal = {BioMed Research International Hindawi},
      keywords = {AMTCSE,AMTUNI,accepted,full paper},
      mendeley-tags = {AMTCSE,AMTUNI,accepted,full paper},
      pages = {12},
      title = {{EEG Recording and Online Signal Processing on Android: A Multiapp Framework for Brain-Computer Interfaces on Smartphone}},
      volume = {2017},
      year = {2017} }
  • [incollection] bibtex | Go to document Go to document
    C. Lins, S. M. Müller, and A. Hein, "Model-Based Approach for Posture and Movement Classification in Working Environments," , Wichert, R. and Klausing, H., Eds., , pp. 25-33.
    @incollection{Lins.2016b, abstract = {In this paper, we present an approach for model-based movement and posture classification in working environments. The approach presented here is designed for long-term in-situ observations of and by workers in their workplaces. The proposed model is adaptable to different input data, e.g., skeleton data from either an Inertial Measurement Unit (IMU) or a skeleton derived from an optical sensor such as Kinect. We present a preliminary design of the model and suggest algorithms suitable for real-time usage of the model in an IMU-based motion capture suite. In an experiment we measured the weight on the knee while performing different kneeing postures to show the dependence of posture angles on the knee load.},
      address = {Frankfurt/M},
      author = {Lins, Christian and M{\"{u}}ller, Sebastian Matthias and Hein, Andreas},
      booktitle = {Ambient Assisted Living: 8. AAL-Kongress 2015,Frankfurt/M, April 29-30. April, 2015},
      doi = {10.1007/978-3-319-26345-8_3},
      editor = {Wichert, Reiner and Klausing, Helmut},
      file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lins, M{\"{u}}ller, Hein - 2016 - Model-Based Approach for Posture and Movement Classification in Working Environments.pdf:pdf},
      isbn = {978-3-8007-3901-1},
      keywords = {Working environment Model Posture Classification K,accepted,full paper},
      mendeley-tags = {accepted,full paper},
      pages = {25--33},
      publisher = {Springer International Publishing},
      series = {Advanced Technologies and Societal Change},
      title = {{Model-Based Approach for Posture and Movement Classification in Working Environments}},
      url = {{\_}3},
      year = {2016} }
  • [inproceedings] bibtex
    N. Volkening, A. Unni, B. S. Löffler, S. Fudickar, J. W. Rieger, and A. Hein, "Characterizing the Influence of Muscle Activity in fNIRS Brain Activation Measurements." , pp. 84-88.
    @inproceedings{Volkening2016, abstract = {Driving is a complex and cognitively demanding task. It is essential to assess the cognitive state of the driver in order to design cognitive technical systems that can adapt to different driver cognitive states. Our research attempts to assess these states using functional Near Infrared Spectroscopy (fNIRS) by measuring brain activity in a virtual reality driving simulator. However, the fNIRS brain activation measurements could be influenced by muscle activity and we wanted to investigate this phenomenon. For this, we designed a paradigm with two conditions (listen, teeth clench) which show a significant contrast in the influence of muscle activity. We observed that the muscle hemodynamic response can show a higher magnitude of signal change compared to brain hemodynamic response. The muscle hemodynamic response showed an increase in deoxygenated hemoglobin (HbR) whereas the brain hemodynamic response showed a decrease in HbR. Moreover, the dynamics of the brain and muscle hemodynamic response differed. The brain response showed the same latency for oxygenated hemoglobin (HbO) and HbR while the muscle HbR response had a slower latency compared to HbO. We concluded that the fNIRS brain activation measurements could indeed be influenced by muscle activity. We were also able to determine some characteristics of the muscle hemodynamic response.},
      author = {Volkening, Nils and Unni, Anirudh and L{\"{o}}ffler, Birte Sofie and Fudickar, Sebastian and Rieger, Jochen W. and Hein, Andreas},
      booktitle = {IFAC-PapersOnLine},
      doi = {10.1016/j.ifacol.2016.08.013},
      file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Volkening et al. - 2016 - Characterizing the Influence of Muscle Activity in fNIRS Brain Activation Measurements.pdf:pdf},
      issn = {24058963},
      keywords = {OFFIS=G-AIT/AHT/CSE,UNIAMT,UNICSE,accepted,full paper},
      mendeley-tags = {OFFIS=G-AIT/AHT/CSE,UNIAMT,UNICSE,accepted,full paper},
      number = {11},
      pages = {84--88},
      title = {{Characterizing the Influence of Muscle Activity in fNIRS Brain Activation Measurements}},
      volume = {49},
      year = {2016} }
  • [inproceedings] bibtex | Go to document Go to document
    C. Lins, A. Klausen, S. Fudickar, S. Hellmers, M. Lipprandt, R. Röhrig, and A. Hein, "Determining Cardiopulmonary Resuscitation Parameters with Differential Evolution Optimization of Sinusoidal Curves." , pp. 665-670.
    @inproceedings{Lins2018c, abstract = {In this paper, we present a robust sinusoidal curve fitting method based on the Differential Evolution (DE) algorithm for determining cardiopulmonary resuscitation (CPR) parameters – naming chest compression fre-quency and depth – from skeletal motion data. Our implementation uses skeletal data from the RGB-D (RGB + Depth) Kinect v2 sensor and works without putting non-sensor related constraints such as specific view an-gles or distance to the system. Our approach is intended to be part of a robust and easy-to-use feedback system for CPR training, allowing its unsupervised training. We compare the sensitivity of our DE implementation with data recorded by a Laerdal Resusci Anne mannequin. Results show that the frequency of the DE-based CPR is recognized with a variance of ±4.4 bpm (4.1{\%}) in comparison to the reference of the Resusci Anne mannequin.},
      address = {Funchal - Madeira, Portugal},
      author = {Lins, Christian and Klausen, Andreas and Fudickar, Sebastian and Hellmers, Sandra and Lipprandt, Myriam and R{\"{o}}hrig, Rainer and Hein, Andreas},
      booktitle = {Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies},
      doi = {10.5220/0006732806650670},
      file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lins et al. - 2018 - Determining Cardiopulmonary Resuscitation Parameters with Differential Evolution Optimization of Sinusoidal Curves.pdf:pdf},
      isbn = {978-989-758-281-3},
      keywords = {CPR Training,Cardiac Massage,Curve Fitting,Evolutionary Algorithm,UNIAMT,UNILLM},
      mendeley-tags = {UNIAMT,UNILLM},
      pages = {665--670},
      publisher = {SCITEPRESS - Science and Technology Publications},
      title = {{Determining Cardiopulmonary Resuscitation Parameters with Differential Evolution Optimization of Sinusoidal Curves}},
      url = {},
      year = {2018} }
  • [inproceedings] bibtex | Go to document Go to document
    C. Lins, A. Hein, L. Halder, and P. Gronotte, "Still in flow — long-term usage of an activity motivating app for seniors." , pp. 1-4.
    @inproceedings{Lins2016, abstract = {In this paper, results from the long-term usage of a mobile application (app) for seniors that encourages physical and mental activity are presented. The application was designed for elderly inhabitants of senior residences to motivate them to increase their physical and mental activity in everyday life. Usage statistics of 82 users for about two years were processed and show that the active elderly users can be clustered in two groups with either increasing or decreasing and very little constant activity. Users with decreasing activity have also shown decreasing usage errors with the app's user interface which may indicate that they are growing out of the app. The results show insight view about the usage and suggest that the Concept of Flow can be applied here.},
      address = {Munich},
      author = {Lins, Christian and Hein, Andreas and Halder, Luca and Gronotte, Philipp},
      booktitle = {2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom)},
      doi = {10.1109/HealthCom.2016.7749476},
      file = {:C$\backslash$:/Users/NilsV/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Lins et al. - 2016 - Still in flow — long-term usage of an activity motivating app for seniors.pdf:pdf},
      isbn = {978-1-5090-3370-6},
      keywords = {accepted},
      mendeley-tags = {accepted},
      month = {sep},
      pages = {1--4},
      publisher = {IEEE},
      title = {{Still in flow — long-term usage of an activity motivating app for seniors}},
      url = {},
      year = {2016}
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