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Projects

Biomechanical laboratory (MotionLab)
  • The MotionLab is located at the Institute for Sport Science and Sports. It is a joint project of the Department of Trauma and Orthopaedic Surgery of the University Clinic Erlangen, the Institute for Sport Science and Sport and the Digital Sports Group of the Pattern Recognition Lab.

    The laboratory is equipped with a eight-camera infrared motion capturing system, a split-belt treadmill and a wireless EMG-system. Besides the acquisition of kinematic motion capture and kinetic data, physiological data such as electromyographic (EMG), electrocardiographic (ECG) and electroencephalographic (EEG) can be measured.

    The methods are applied to several different movements such as gait, running, jumping and cycling.


Multimodal gait analysis of osteoarthritis patients
  • So far, standardized quantitative gait analysis based is not part of the clinical decision process for the treatment of advanced knee osteoarthritis. In this project we focus on the multimodal analysis of objective and subjective variables before and after knee replacement surgery (total knee arthroplasty, TKA) to identify relevant variables that may be used to determine outcome of the surgery.

    We include mobile sensor-based systems, 3D cinematography including instrumented treadmills, radiography, functional testing as well as standardized interviews.

    The goal of our research is the analysis of pathological human gait. Applications will be the quantitative and individualized assessment of movement disorders, interventions and rehabilitation progress. This project is funded by the Emerging Fields Initiative (Project EFI Moves)

       

                 

MotionLab@Home
  • A current challenge is the transfer of mobile sensor-based gait analysis systems from clinical settings to the home environment to capture movement parameters and their changes (for example due to medication) in everyday life.

    The goal of this project which is conducted in the scope of the EFI Moves project is to build a complementary, video and sensor based system which can be used to evaluate long-term monitoring of interventions in Parkinson's Disease in the home-environment. Ethical aspects of home-monitoring are especially taken into consideration in the development phase.

    The sensor system will be based on the eGaIT system ("embedded Gait analysis using Intelligent Technology) which has been developed by the partners of the Pattern Recognition Lab, the Molecular Neurology department and Astrum IT GmbH and a markerless video-capturing system of Simi Reality Motion Systems GmbH. 

     

Biomechanical investigation of gait phases
  • The analysis of spatiotemporal parameters of gait is important in different contexts such as classification of diseases or for monitoring of treatment outcomes. Spatiotemporal parameters used in gait analyses do usually not include the durations of all distinct gait sub phases such as loading response, mid stance, terminal stance and pre swing. In this study we focused on the dependency of the gait sub phase durations on speed.

    The data of this study can be found on www.activitynet.org


     

     


    Journal Articles
    Kluge, Felix; Leibold, Andreas; Krinner, Sebastian; Welsch, Götz; Lochmann, Matthias; Eskofier, Björn
    Effect of walking speed on gait sub phase durations
    Human Movement Science, vol. 43, pp. 118-124, 2015 (BiBTeX, Who cited this?)
    Articles in Conference Proceedings
    Kluge, Felix; Leibold, Andreas; Krinner, Sebastian; Welsch, Götz; Lochmann, Matthias; Eskofier, Björn
    Speed modelling of relative gait phase durations
    XXV Congress of the International Society of Biomechanics, Glasgow, UK, 12.-16. July 2015, pp. 1835-1836, 2015 (BiBTeX, Who cited this?)
Statistical analysis of biomechanical data
  • Modern motion capture laboratories allow the acquisition of a large amount of data arising from many modalities (kinematics, kinetics, electromyography, electroencephalography, etc.). We developed a tool to statistically compare two or more conditions of cyclic data. After pre-processing, segmentation and time normalization, statistical differences between the signals of multiple conditions can be found at each time step. The methodology can not only be applied to different measurement modalities but also to all kinds of cycling movements such as gait, running, cycling etc.

     


    Articles in Conference Proceedings
    Neue Ansätze in der Bewegungsforschung aft, Sektion Biomechanik (Tagung Deutsche Vereinigung für Sportwissenschaft, Sektion Biomechanik), Technische Universität Chemnitz, 13.-15.03.2013, pp. 7, 2013 (BiBTeX, Who cited this?)
Automatic segmentation of biomechanical data

  • The evaluation of vertical jumps is an established method in sports performance and medical diagnostics. To simplify data analysis such jumping series need to be segmented automatically. In the present study, we investigated two algorithms (threshold based; dynamic time warping based) regarding their performance to segment vertical jumps on the basis of biomechanical data. The data was acquired in a motion capture laboratory using 8 infrared cameras and 2 force plates.

     


    Articles in Conference Proceedings
    Kluge, Felix; Merklein, Sascha; Welsch, Götz; Lochmann, Matthias; Eskofier, Björn
    13th International Symposium on 3D Analysis of Human Movement (3D-AHM) (3D Analysis of Human Movement), Lausanne, Switzerland, 14.07.2014-17.07.2014, pp. 128-131, 2014, ISBN 9782880748562 (BiBTeX, Who cited this?)
EEG and motion
  • Electroencephalography (EEG) is a popular method of studying and understanding processes that underly behavior. Using this methodology during movement tasks poses challenges to the setup, the equipment and algorithms for removing movement artifacts. We integrate electroencephalographic measurements into the MotionLab in order to investigate correlations between brain activity and the corresponding movement.


    Journal Articles
    Reis, Pedro; Kluge, Felix; Gabsteiger, Florian; von Tscharner, Vinzenz; Lochmann, Matthias
    Methodological aspects of EEG and body dynamics measurements during motion
    Frontiers in Human Neuroscience, vol. 1, no. 8, pp. 156-175, 2014 (BiBTeX, Who cited this?)

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