Anaerobic threshold measurement using dynamic neural network models.
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.
Ringwood
J V
JV
School of Electronic Engineering, Dublin City University, Glasnevin, Ireland. ringwoodj@eeng.dcu.ie
D015308:Anaerobic Threshold; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D016018:Least-Squares Analysis; D008297:Male; D008955:Models, Cardiovascular; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012420:Running / Q000502:physiology*; D013997:Time Factors; D014937:Work / Q000502:physiology