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.
© Copyright 1999 Computers in Biology and Medicine. Elsevier. All rights reserved.
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| Notations: | biological and medical sciences technical and natural sciences |
| Published in: | Computers in Biology and Medicine |
| Language: | English |
| Published: |
1999
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| Online Access: | https://doi.org/10.1016/s0010-4825(99)00008-6 |
| Volume: | 29 |
| Issue: | 4 |
| Pages: | 259-271 |
| Document types: | electronical publication |
| Level: | advanced |