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Non-linear analysis of pedaling motion

Work Group Multimedia Signal Processing

Prof. Dietmar Saupe
M.Sc. Juan Carlos Quintana

Summary

Motion capture technologies are every day more popular because the range of methods and applications for processing motion information is increasing and the costs of those technologies are even more lower. Motion data have been used successfully in recent years in the production of special effects in 3D videos, modeling of human movements, motion simulation, and bio-feedback.

Recent empirical studies of human motion (e.g. walking) have revealed complex dynamical structures, even under constant environmental conditions. These structures, also known as variability, have been used to determine disease severity, medication utility, and fall risk. We found a lack of studies about the description of the dynamic structure of pedaling motion and the potential benefits of this to distinguish subtle differences between pedaling motion patterns. We address the question of what algorithms, methods of analysis, and data collection techniques can be implemented to assess the variability and asymmetry present in pedal and leg kinematics during pedaling. An adaptation of algorithms for non-linear time series analysis suited to the characteristics of pedaling motion will be developed. For the data acquisition we use the motion capture system LUKOtronic AS 200 to capture real movements in our laboratory of performers using a bike simulator cyclus2.