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Biosignal Analysis

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?)
Sensor-based mobile functional movement screening
  • The Functional Movement Screen (FMS) is a useful tool to assess functional abilities in a pre-participation screening. Its seven dynamic movement tests reveal shortcomings in stability and mobility and screen the whole body. However, the current test protocol delivers results that are subjective, qualitative and have to be manually processed. This article presents a semi-automatic system to overcome these limitations for the Deep Squat test. The system consists of four wireless inertial sensors and a central Android-based processing node for data analysis and result storage. We developed our system based on data from ten subjects and evaluated the results with the FMS scoring guidelines. The sensor-based scoring system completely agreed with the manual scoring in eight out of ten subjects. In addition, quantitative information in case of compensation movements was logged. Thus, our system is capable of simplifying the FMS test and enhances the score with objective, quantitive and automatic results.

    Articles in Conference Proceedings
    Jensen, Ulf; Weilbrenner, Fabian; Rott, Franz; Eskofier, Björn
    Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (Wireless Mobile Communication and Healthcare), Paris, November 21-23, vol. 61, pp. 215-223, 2012, ISBN 978-3-642-37892-8 (BiBTeX, Who cited this?)