european college of sport science

OP-CS01 Computer Sciences and Statistics
The windows show a virtual athlete based on a 15 segment lattice model in virtual space. The virtual room can be exactly adapted to the
real dimensions of the athlete’s environment. Gaze lines and gaze spots are integrated into the virtual space. The spots represent the
resulting coordinates, calculated by the head related eye data and the heads position in space. The diameter of the spots correlates with
the visual fixation time on theese gaze spots. Occulomotor gain of nystagmus can also be calculated. In the model, all muscles being
registered by the EMG are depicted using coloured matrices. The higher the activation level, the darker the colour with which they are
visualized. Onset threshold can be defined for all muscles separately. The virtual model and room can be zoomed or moved along and
around all axes. Vectors of vestibular load can be visualized within the head-model, separately. Optionally the subjective visual view of
the athlete in the virtual room can be visualised and screened in helmet mounted displays for the use in further research concerning
visuo-mental training methods. All data plots calculated by the software are visualized synchroneously in a separate window: The plots
can be selected as required and be exported to other programs (e.g. for further statistical analysis).
Conclusion
To better understand the complexity of human movement, it is necessary to have a deeper insight into human control processes. The
benefit of this software is the ability to combine the extrinsic view with intrinsic data of movement control to better understand their relationships
in sports and daily life.
A SIMULATOR FOR RACE-BIKE TRAINING ON REAL TRACKS
DAHMEN, T., SAUPE, D.
UNIVERSITY OF KONSTANZ
We develop methods for data acquisition, analysis, modeling and visualization of performance parameters in endurance sports with
emphasis on competitive cycling. For this purpose, we designed a simulator to facilitate the measurement of training parameters in a
laboratory environment, to familiarize cyclists with unknown tracks, and to develop models for training control and performance prediction.
The simulator is based on a Cyclus2 ergometer (RBM Elektronik-Automation GmbH, Leipzig, Germany), which provides a realistic cycling
experience since one can mount arbitrary bikes and its elastic suspension even allows for a sway pedal stroke. The eddy current brake
guarantees non-slipping transmission of a braking resistance up to 3000 W.
Operating the Cyclus2 in the gradient mode, we impose arbitrary slopes by our own platform independent PC-based control software at
a sampling rate of 2 Hz. The height profiles for various tracks were recorded using a commercial GPS device.
The Cyclus2 has two major constraints with respect to simulating real tracks: We must focus on tracks without downhill accelerations
since it has no engine and the eddy current brake requires a minimum rotation velocity of the flywheel to accurately generate the brake
force. Therefore, we fixed the derailleurs to a heavy gear and mounted four electronic buttons to the handlebar which act like shift levers
of virtual gears. Our software incorporates the virtual gear into the gradient so that the cyclist feels a correct resistance while the flywheel
exceeds the minimum rotation velocity in all realistic uphill scenarios. Moreover, we can simulate arbitrary gears easily and record them
over time. As the physical flywheel rotation is faster than in the simulation, our software must correct the related performance data.
The simulation includes a video playback that is synchronized with the cyclist’s current position on the track. In addition, time, distance,
speed, cadence, heart rate, power and gears are monitored, a 2D-projection of the course gives feedback on the progress and a gradient
profile indicates the slope in the surrounding of the current position.
Comparative outdoor tests with an SRM power meter (Schoberer Rad Messtechnik, Welldorf, Germany) show that the simulator gives
reasonable estimates for different pacing strategies (constant power/speed/heart rate).
In future, we strive to integrate a more precise mechanical model (Martin et al., 1998), extend the palette of physiological measurements
(oxygen consumption, ECG, lactate etc) and implement models for these parameters. The whole system shall indicate the optimum
pacing strategy as Gordon derived in 2005 for simple models and synthetic data. Using sophisticated biofeedback visualization, cyclists
shall be able to optimally prepare themselves even for unfamiliar tracks on our simulator.
Martin JC, Milliken DL, Cobb JE, McFadden KL, Coggan AR. (1998). J Appl Biomech, 14, 276-291.
Gordon S. (2005). J Sport Sci, 8, 81-90.
ANALYZING THE AIMING PROCESS IN BIATHLON SHOOTING USING SELF-ORGANIZING MAPS
BACA, A., PERL, J., KORNFEIND, P., BÖCSKÖR, M.
1. UNIVERSITY OF VIENNA, 2. UNIVERSITY OF MAINZ
Introduction: The aiming strategy in biathlon shooting is a crucial factor for success. Because of the preceding high exertions of the athletes
a well controlled motion of the barrel just before shooting is essential (Zatsiorsky and Aktov, 1990). Methods are required for analyzing
the stability of the aiming process with a special focus on exertion. The aim of this study was to investigate the applicability of special
self-organizing maps to identify and compare patterns in the aiming motion in standing shooting.
Methods: A video based system (Baca and Kornfeind, 2006) was used to track the motion of the muzzle of the barrel in two dimensions
(left-right, up-down) automatically. Six parameters were calculated describing the motion in ten time intervals of 0.2 s length before the
shot. Four athletes (I, II, III, IV) (I, III, IV: Austrian national “B” team, II: “C” team) participated in the study. Each athlete performed four series
of five shots before and after exertion making 160 shots altogether.
Based on these data a special self organizing map (Perl et al., 2006) consisting of 400 neurons was trained and data sets were generated
on the neurons. The attribute values of those data sets represent the six components describing a motion of a muzzle in a 0.2 s time
interval. Similar neurons were combined to clusters.
The ten successive data-sets describing each shot were then mapped to the corresponding neurons of the net. The sequence of the
related clusters in the respective succession was then used as 1-dimensional representation (a pattern) of the complex aiming motion.
Results: Regarding intra-individual stability, some peculiarities were found. The number of a shot within a series of five shots clearly
influenced the pattern observed. This was particularly the case after exertion. Moreover, in this condition less stable shot types were
found. Subjects were able to maintain their pattern before the exertion to a different degree. Subject II, who showed the largest deviations,
scored worst.
Although inter-individual variability was difficult to assess, some similarities (e.g. in timing) could be identified.
Discussion: The method is promising to analyze inter- and inter-individual similarities and differences. One shortfall might be that only a
restricted set of parameter values originating from a 2-dimensional recording of the muzzle has been considered. Time series data
126 14 TH
ANNUAL CONGRESS OF THE EUROPEAN COLLEGE OF SPORT SCIENCE