Monitoring local muscle load in football

Use leg acceleration, processed with a big data analysis approach, as an indication of the local muscle load to accurately represent the players’ experienced load

Master thesis (2019)

Authors

R.B.W. Schotel Mechanical Engineering

Contributors

F.C.T. van der Helm Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering (supervisor 1)
D.J.J. Bregman Research Funding National (supervisor 1)
Edwin Goedhart (supervisor 1)
K.M.B. Jansen Emerging Materials - IDE (supervisor 2)
A.S.M. Steijlen Electronic Instrumentation - EEMCS (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2019 Rozemarijn Schotel
Football Muscle load Muscle prevention Local accelerations Big data analysis approach
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Published Date

28-06-2019

Language

English

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Faculty

Mechanical Engineering

Abstract

In football, a lot of hip and thigh muscle injuries occur as a result of high muscular loads due to accelerative leg movements. To prevent muscle damage and optimise performance, it is essential to continuously identify when and how frequent local hip and thigh muscular loads develop in the explosive and dynamic football environment. The currently used method is an acceleration index based on two-dimensional position data of the whole global body measured by the Local Positioning Measurement system. The problem is that this system does not correspond with the experienced load of players because leg movements are excluded. Therefore, this study introduces a new local concept of gathering local three-dimensional leg acceleration data by inertial measurement units.
This pilot study aims to use a big data analysis approach to translate leg acceleration data into a measure to indicate local muscle load and compare this new local and the current global method to the players’ experienced load. Five participants performed specific football drills with an intensity increase from jogging to sprinting and by adding a pass and shot. Measures are developed, based on the pelvis, upper leg, and lower leg accelerations, by a peak and cumulative data analysis approach. By evaluating trend percentages of the intensity increase, it is obtained that a local acceleration measure is comparable to the players’ experienced load if it considers the sum of normal or peak data points weighted per zone and per travelled distance. Furthermore, a similar result is obtained when only the upper leg or lower leg accelerations are considered.
It can be concluded that local three-dimensional acceleration of the lower extremities, processed with a big data analysis approach, represent the football players’ experienced muscular load more accurate than the current global method. Further research, including a higher number of participants, should prove the significance.