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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.

Approach

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).

Publications

  • C. Lins, S. Fudickar, and A. Hein, "XML Skeleton Definitions for Human Posture Assessments," Studies in Health Technology and Informatics, 2018.
    @article{Lins2018d, abstract = {In this paper, we show how the XML dialect SKAML (Skeletal Assessment Markup Language) can be used to use data from one or more Motion Capture systems to perform human posture assessments with multiple assessment methods. We show an implementation example using an inertial measuring suit and both OWAS and REBA assessment methods. SKAML makes it possible to implement classifiers for a Motion Capture system once and adapt the classifier by-configuration to various ergonomics assessment methods. We anticipate our work as help for researchers and developers that implement new assessment methods or motion capture systems.},
      author = {Lins, Christian and Fudickar, Sebastian and Hein, Andreas},
      file = {:home/s/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Lins, Fudickar, Hein - 2018 - XML Skeleton Definitions for Human Posture Assessments.pdf:pdf},
      journal = {Studies in Health Technology and Informatics},
      keywords = {Motion Capture,Musculoskeletal Disorders,OWAS,Posture Assessment,Posture Classification,REBA,SKAML,Skeleton Markup,UNIAMT,Unpublished},
      title = {{XML Skeleton Definitions for Human Posture Assessments}},
      year = {2018},
      url = {http://doi.org/10.3233/978-1-61499-896-9-225} }
  • C. Lins, D. Eckhoff, A. Klausen, S. Hellmers, A. Hein, and S. Fudickar, "Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids," Applied Soft Computing, 2019.
    @article{Lins2019, title = "Cardiopulmonary resuscitation quality parameters from motion capture data using Differential Evolution fitting of sinusoids", journal = "Applied Soft Computing", year = "2019", issn = "1568-4946", doi = "https://doi.org/10.1016/j.asoc.2019.03.023", url = "http://www.sciencedirect.com/science/article/pii/S1568494619301413",
      author = "Christian Lins and Daniel Eckhoff and Andreas Klausen and Sandra Hellmers and Andreas Hein and Sebastian Fudickar", keywords = "CPR training, Resuscitation, Differential Evolution, Cardiac arrest, Motion capture, Kinect, Sinusoid regression model", abstract = "Cardiopulmonary resuscitation (CPR) is alongside electrical defibrillation the most crucial countermeasure for sudden cardiac arrest, which affects thousands of individuals every year. In this paper, we present a novel approach including sinusoid models that use skeletal motion data from an RGB-D (Kinect) sensor and the Differential Evolution (DE) optimization algorithm to dynamically fit sinusoidal curves to derive frequency and depth parameters for cardiopulmonary resuscitation training. It is intended to be part of a robust and easy-to-use feedback system for CPR training, allowing its use for unsupervised training. The accuracy of this DE-based approach is evaluated in comparison with data of 28 participants recorded by a state-of-the-art training mannequin. We optimized the DE algorithm hyperparameters and showed that with these optimized parameters the frequency of the CPR is recognized with a median error of ±2.9 compressions per minute compared to the reference training mannequin." }
  • [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," BioMed Research International Hindawi, vol. 2017, p. 12, 2017.
    @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," in Ambient Assisted Living: 8. AAL-Kongress 2015,Frankfurt/M, April 29-30. April, 2015, Wichert, R. and Klausing, H., Eds., Frankfurt/M: Springer International Publishing, 2016, 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},
      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 = {http://www.springer.com/de/book/9783319263434 http://link.springer.com/10.1007/978-3-319-26345-8{\_}3},
      year = {2016} }
  • [inproceedings] bibtex | Go to document Go to document
    C. Lins, S. Fudickar, A. Gerka, and A. Hein, "A Wearable Vibrotactile Interface for Unfavorable Posture Awareness Warning," in Proc. in Proc. 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2018), Funchal/Madeira, Portugal, 2018.
    @inproceedings{Lins2018, abstract = {We present the concept of a vibrotactile interface with up to 13 tactors (vibration motors) that are distributed over the full body to warn industry workers when taking unfavorable postures. The developed system is to be integrated into a motion capture workwear for industry workers to serve as posture feedback system to prevent unfavorable or even harmful postures. Such postures are a risk factor for musculoskeletal disorders (MSD), especially among older adults. We evaluated the vibrotactile system with 11 subjects to identify the optimal notification vibration sequences (regarding pulse length and repetition) and the accuracy of the location-dependent perception. Results indicate that the optimal pulse length is about 150 ms and is repeated 2 or 3 times within the sequence for maximum attention.},
      address = {Funchal/Madeira, Portugal},
      author = {Lins, Christian and Fudickar, Sebastian and Gerka, Alexander and Hein, Andreas},
      booktitle = {in Proc. 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2018)},
      keywords = {Ergonomics Feedback,Haptics,Informationenfehlen,Occupational Ergonomics,Posture Warning,UNIAMT,UNILLM,Unpublished,Vibrotactile Interface,Wearables},
      title = {{A Wearable Vibrotactile Interface for Unfavorable Posture Awareness Warning}},
      year = {2018},
      url = {https://scholar.google.de/scholar?cluster=96480978161803230&hl=de&as_sdt=0,5} }
  • [inproceedings] bibtex | Go to document Go to document
    C. Lins, S. Fudickar, and A. Hein, "SKAML: An XML Markup Language for Abstract Skeleton Definitions in the Context of Human Posture Assessments," in Proc. in Proc. 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2018), Funchal - Madeira, Portugal, 2018.
    @inproceedings{Lins2018a, abstract = {An XML-dialect for the description and configuration of abstract human skeletons for ergonomics assessments with motion capture (MoCap) systems -the Skeletal Assessment Markup Language (SKAML) -is presented. A SKAML document is the semantic description of the MoCap system and assessment method conjunction. It describes the skeletal system of a human body as a system of rigid bones and joints from a MoCap observer perspective and allows straightforward combinations of MoCap data and digitized methods for ergonomics assessments. We anticipate our work as help for researchers and developers that implement new assessment methods or MoCap systems.},
      address = {Funchal - Madeira, Portugal},
      author = {Lins, Christian and Fudickar, Sebastian and Hein, Andreas},
      booktitle = {in Proc. 4th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2018)},
      keywords = {Ergonomics Assessment,Informationenfehlen,Motion Capture,Musculoskeletal Disorders,Skeleton Markup,UNIAMT,UNILLM,Unpublished},
      title = {{SKAML: An XML Markup Language for Abstract Skeleton Definitions in the Context of Human Posture Assessments}},
      year = {2018},
      url ={https://www.researchgate.net/profile/Christian_Lins/publication/325596419_SKAML_An_XML_Markup_Language_for_Abstract_Skeleton_Definitions_in_the_Context_of_Human_Posture_Assessments/links/5ba9ff27a6fdccd3cb70e564/SKAML-An-XML-Markup-Language-for-Abstract-Skeleton-Definitions-in-the-Context-of-Human-Posture-Assessments.pdf} }
  • [inproceedings] bibtex | Go to document Go to document
    C. Lins, S. M. Müller, M. Pfingsthorn, M. Eichelberg, A. Gerka, and A. Hein, "Unsupervised Temporal Segmentation of Skeletal Motion Data using Joint Distance Representation," in Proc. Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, Funchal - Madeira, Portugal, 2018, pp. 478-485.
    @inproceedings{Lins2018b, abstract = {In this paper, we present an online method for the unsupervised segmentation of skeletal motion capture data for the assessment of unfavorable or harmful postures in the context of musculoskeletal disorders. The long-time motion capture data is segmented into short motion sequences using joint distances of the captured skeleton. We use the difference between joint distance matrices to detect variances in motion dynamics in which the motion is separated into either a dynamic motion or a static posture. Then, the static posture can be evaluated using well-known posture assessment methods such as the Ovako Working postures Analysing System (OWAS) to derive risk factors for musculoskeletal disorders. The algorithm works in real-time so that it can be incorporated in live warning systems for unfavorable or harmful postures. We evaluated the segmentation algorithm by comparing it with results from state-of-the-art offline motion segmentation algorithms as gold standard. Results show that the algorithm approaches the performance of state-of-the-art offline segmentation algorithms.},
      address = {Funchal - Madeira, Portugal},
      author = {Lins, Christian and M{\"{u}}ller, Sebastian M and Pfingsthorn, Max and Eichelberg, Marco and Gerka, Alexander and Hein, Andreas},
      booktitle = {Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies},
      doi = {10.5220/0006598904780485},
      keywords = {Ergonomics Assessment,Human Motion Analysis,Joint Distance Matrices,Musculoskeletal Disorders,Temporal Segmentation,UNIAMT,UNILLM},
      pages = {478--485},
      publisher = {SCITEPRESS},
      title = {{Unsupervised Temporal Segmentation of Skeletal Motion Data using Joint Distance Representation}},
      year = {2018},
      url ={https://www.researchgate.net/profile/Christian_Lins/publication/321162000_Unsupervised_Temporal_Segmentation_of_Skeletal_Motion_Data_using_Joint_Distance_Representation/links/5a7aeeefa6fdcc772b0955a0/Unsupervised-Temporal-Segmentation-of-Skeletal-Motion-Data-using-Joint-Distance-Representation.pdf?origin=publication_detail} }
  • [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," in Proc. Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies, Funchal - Madeira, Portugal, 2018, 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},
      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 = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0006732806650670},
      year = {2018} }
  • [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," in Proc. IFAC-PapersOnLine, 2016, 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. Hein, L. Halder, and P. Gronotte, "Still in flow — long-term usage of an activity motivating app for seniors," in Proc. 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom), Munich, 2016, 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 = {http://ieeexplore.ieee.org/document/7749476/},
      year = {2016}
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