Summary
Research article - Report
See the full content of this document
Extract
Functional model of monofin swimming technique based on the construction of neural networks.
Abstract
In this study we employed an Artificial Neuronal Network to analyze the forces flexing the monofin in reaction to water resistance. In addition we selected and characterized key kinematic parameters of leg and monofin movements that define how to use a monofin efficiently and economically to achieve maximum swimming speed. By collecting the data recorded by strain gauges placed throughout the monofin, we were able to demonstrate the distribution of forces flexing the monofin in a single movement cycle. Kinematic and dynamic data were synchronized and used as entry variable to build up a Multi-Layer Perception Network. The horizontal velocity of the swimmer's center of body mass was used as an output variable. The network response graphs indicated the criteria for achieving maximum swimming speed. Our results pointed out the need to intensify the angular velocity of thigh extension and dorsal flexion of the feet, to strengthen velocity of attack of the tail and to accelerate the attack of the distal part of the fin. The other two parameters which should be taken into account are dynamics of tail flexion change in downbeat and dynamics of the change in angle of attack in upbeat. Key words: Kinematics, dynamics, leg and fin movements, modeling. Introduction The information flow between swimmer and coach must be objective and of a precisely determined quality. Such criteria may be assessed by applying biomechanical methods, including modeling. Modeling is a process related to the description of a technique, which based on physical or mathematical correlations, reflects the subject of the study and thus creates the possibility to optimize the technique. The concept of the functional model of swimming is based on the development of a deterministic model, i.e. the correlation between measurable performance and the features determining the outcome (Hay, 1985; Reischle...See the full content of this document
Sponsored links
