Measurement of Energy Expenditure in Elite Athletes Using MEMS-Based Triaxial Accelerometers

Fitness development and performance assessment of elite athletes requires an understanding of many physiological factors, many of these are direct and indirect measures of athlete energy expenditure. Many methods are physiological factor assessments and require the athlete to be constrained by labor...

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Bibliographic Details
Published in:IEEE sensors journal 2007-04, Vol.7 (4), p.481-488
Main Authors: Wixted, A.J., Thiel, D.V., Hahn, A.G., Gore, C.J., Pyne, D.B., James, D.A.
Format: Article
Language:English
Subjects:
Accelerometer
Accelerometers
biomechanics
Blood
energy expenditure
Energy measurement
Frequency estimation
Frequency measurement
gait analysis
Kinematics
Laboratories
Manufacturing
Microelectromechanical systems
Micromechanical devices
signal processing
sports engineering
Studies
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Summary:Fitness development and performance assessment of elite athletes requires an understanding of many physiological factors, many of these are direct and indirect measures of athlete energy expenditure. Many methods are physiological factor assessments and require the athlete to be constrained by laboratory equipment or periodic interruption of activity to take measurements such as blood samples are required to be taken. This paper presents a method that is entirely ambulatory and noninvasive, using microelectromechanical systems (MEMS) accelerometers. The commonly used output of commercial accelerometer-based devices (known as "counts") cannot discriminate activity intensity for the activities of interest. This, in conjunction with variability in output from different systems and lack of commonality across manufacturers, limits the usefulness of commercial devices. This paper identifies anthropometric and kinematic sources of inter-athlete variability in accelerometer output, leading to an alternate energy expenditure estimator based mainly on step frequency modified by anthropometric measures. This energy expenditure estimator is more robust and not influenced by many sources of variability that affect the currently used estimator. In this system, low-power signal processing was implemented to extract both the energy estimator and other information of physiological and statistical interest
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2007.891947