Anaerobic threshold measurement using dynamic neural network models.

J V Ringwood

doi:10.1016/s0010-4825(99)00008-6

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Anaerobic threshold measurement using dynamic neural network models.

J V Ringwood

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Abstract

This paper deals with the subject of anaerobic threshold measurements for athletes involved in aerobic or aerobic/anaerobic sports. Traditionally, anaerobic threshold has been determined using invasive tests or using a non-invasive technique using steady-state heart-rate/work rate data. Non-invasive tests have the advantage of not requiring specialised equipment, but the acquisition of steady-state information can be problematic. This paper demonstrates how dynamical data can be used to accurately determine the steady-state heart-rate/work-rate curve (SSHW curve) using neural network dynamic models.

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Anaerobic Threshold Heart Rate Humans Least-Squares Analysis Male Models, Cardiovascular Neural Networks, Computer Nonlinear Dynamics Running Time Factors Work Case Reports Journal Article

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J V Ringwood. 1999. Anaerobic threshold measurement using dynamic neural network models.. https://doi.org/10.1016/s0010-4825(99)00008-6

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