Non-invasive anaerobic threshold measurement using fuzzy model interpolation

The interface between skeletal muscle activation through aerobic and anaerobic glycolysis is of key interest to sportspeople and athletes who participate in medium to long distance sports, such as middle- and long-distance running, cycling, swimming, rowing, kayaking and a variety of other events. To date, the gold standard for measuring anaerobic threshold (AT) is a structured test to exhaustion where blood lactate concentration is measured at regular intervals. However, the need for invasive testing, requiring trained personnel and specialist equipment, limits the availability of such tests. This paper proposes a non-invasive AT measurement method, which validates well against AT measured using lactate analysis. In addition, the proposed test has a relatively loose set of requirements on the exercise test protocol required and just requires a measure of exercise intensity and heart-rate. While the test is applicable to a range of sports, usage is demonstrated in this paper for a set of cyclists, using velocity as a measure of exercise intensity.

[1]  F Manfredini,et al.  The Conconi Test: Methodology After 12 Years of Application , 1996, International journal of sports medicine.

[2]  J. Michael Textbook of Medical Physiology , 2005 .

[3]  Jorma Rissanen,et al.  Minimum Description Length Principle , 2010, Encyclopedia of Machine Learning.

[4]  L. Ljung Prediction error estimation methods , 2002 .

[5]  J V Ringwood Anaerobic threshold measurement using dynamic neural network models. , 1999, Computers in biology and medicine.

[6]  A. Lucia,et al.  Analysis of the aerobic-anaerobic transition in elite cyclists during incremental exercise with the use of electromyography. , 1999, British journal of sports medicine.

[7]  Sheng Chen,et al.  Orthogonal least squares methods and their application to non-linear system identification , 1989 .

[8]  J. Doust,et al.  Lack of Reliability in Conconi's Heart Rate Deflection Point , 1995, International journal of sports medicine.

[9]  Gearóid ÓLaighin,et al.  The age of the virtual trainer , 2012 .

[10]  W. Marsden I and J , 2012 .

[11]  Yoshiyuki Matsuura,et al.  Relationships of anaerobic threshold and onset of blood lactate accumulation with endurance performance , 2004, European Journal of Applied Physiology and Occupational Physiology.

[12]  Guillaume Py,et al.  The Concept of Maximal Lactate Steady State , 2003, Sports medicine.

[13]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  J. Bourgois,et al.  The Conconi Test: A Controversial Concept for the Determination of the Anaerobic Threshold in Young Rowers , 1998, International journal of sports medicine.

[15]  J. Wilmore,et al.  Physiology of Sport and Exercise , 1995 .

[16]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[17]  Kristopher Mendes de Souza,et al.  Maximal lactate steady state estimated by different methods of anaerobic threshold , 2012 .

[18]  L. Codecá,et al.  Determination of the anaerobic threshold by a noninvasive field test in runners. , 1982, Journal of applied physiology: respiratory, environmental and exercise physiology.

[19]  B. Chernow,et al.  The use and clinical importance of a substrate-specific electrode for rapid determination of blood lactate concentrations. , 1994, JAMA.

[20]  Stanley H. Johnson,et al.  Use of Hammerstein Models in Identification of Nonlinear Systems , 1991 .