- Susanne Blex1, 2
- Tim Sziburis1, 3, 4
- Ioannis Iossifidis1
- Robotics and BCI Laboratory, Institute of Computer Science, Ruhr West University of Applied Sciences, Mülheim an der Ruhr, Germany
- Astronomical Institute, Ruhr University Bochum, Bochum, Germany
- Institute for Neural Computation, Ruhr University Bochum, Bochum, Germany
- Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum, Bochum, Germany
Variability analysis bears the potential to differentiate between healthy and pathological human movements . Our study is conducted in the context of developing a portable glove for the diagnosis of movement disorders. This proposal has methodical as well as technical requirements. Generally, the identification of movement disorders via an analysis of motion data needs to be confirmed within the given setup. Typically, rhythmic movements like gait or posture control are examined for their variability, but here, the characteristic pathological traits of arm movement like tremors are under observation. In addition, the usability of a portable sensor instead of a stationary tracking system has to be validated.
In this part of the project, human motion data are recorded redundantly by both an optical tracking system and an IMU. In our setup, a small cylinder is transported in three-dimensional space from a unified start position to one of nine target positions, which are equidistantly aligned on a semicircle. 10 trials are performed per target and hand, resulting in 180 trials per participant in total. 31 participants (11 female and 20 male) without known movement disorders, aged between 21 and 78 years, took part in the study. In addition, the 10-item EHI is used.
The purpose of the analysis is to compare different variability measures to uncover differences between trials (intra-subject variability) and participants (inter-subject variability), especially in terms of age and handedness effects.
Particularly, a novel variability measure is introduced which makes use of the characteristic planarity of the examined hand paths . For this, the angle of the plane which best fits the travel phase of the trajectory is determined. In addition to neurological motivation, the advantage of this measure is that it allows the comparison of trials of different time spans and to different target directions without depending on trajectory warping.
In the future, measurements of the same experimental setup with patients experiencing movement disorders are planned. For the subsequent pathological analysis, this study provides a basis in terms of methodological considerations and ground truth data of healthy participants. In parallel, the captured motion data are modelled utilizing dynamical systems (extended attractor dynamics approach). For this approach, the recorded and modelled data can be compared by the variability measures examined in this study.
Figure 1: Analyzing the variability of human trajectories recorded during the center-out task
This work is supported by the Ministry of Economics, Innovation, Digitization and Energy of the State of North Rhine-Westphalia and the European Union, grants GE-2-2-023A (REXO) and IT-2-2-023 (VAFES).
- Nicholas Stergiou and Leslie M. Decker (2011). “Human movement variability, nonlinear dynamics, and pathology: Is there a connection?” In: Human Movement Science 30.5. EWOMS 2009: The European Workshop on Movement Science, pp. 869–888. ISSN: 0167-9457
- John F. Soechting and Carlo A. Terzuolo (1987). “Organization of arm movements in three-dimensional space. Wrist motion is piecewise planar”. In: Neuroscience 23.1, pp. 53–61. ISSN: 0306-4522.