МОДЕЛИРОВАНИЕ ФИЗИОЛОГИЧЕСКИХ ПОКАЗАТЕЛЕЙ НА УРОВНЕ ПОРОГА АНАЭРОБНОГО ОБМЕНА С ИСПОЛЬЗОВАНИЕМ МЕТОДОВ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА
Ключевые слова:
порог анаэробного обмена, машинное обучение, ступенчато-возрастающее тестирование, модель
Аннотация
Цель исследования – разработка модели предсказания количественных значений физиологических показателей работы организма на уровне порога анаэробного обмена при выполнении эргоспирометрического нагрузочного тестирования до отказа с использованием алгоритмов машинного обучения. Материалы и методы. Обработаны результаты 1273 наблюдений спортсменов в возрасте 18–35 лет. Спортсмены выполняли ступенчато-возрастающую нагрузку на беговой дорожке до отказа. Для определения физиологических показателей на уровне порога анаэробного обмена были разработаны модели на основе алгоритмов машинного обучения из библиотеки Scikit-learn: линейная регрессия, регрессионный случайный лес, градиентный бустинг, регрессионные вектора. Результаты. Модель на основе алгоритма регрессионных векторов показывает наилучшие метрики по показателям ЧСС, МОД, O2/ЧСС (r2 составляет 0,82; 0,90; 0,91 соответственно), а по показателям VO2, VCO2 несколько уступает (–0,76 % и –0,16 % соответственно) алгоритму линейной регрессии (r2 составляет 0,87; 0,86 соответственно). Заключение. Количественные значения показателей точности разработанной модели на основе алгоритма регрессионных векторов сопоставимы с результатами аналогичных работ, что дает возможность использования ее в качестве рекомендательной системы для помощи специалистам в исследованиях, целью которых является определение количественных значений показателей на уровне ПАНО. Слабым местом модели является использование признаков, зависящих от протокола нагрузки, поэтому дальнейшее совершенствование модели будет направлено на нивелирование этого недостатка.
Литература
1. Carvalho D.D., Soares S., Zacca R. et al. Anaerobic Threshold Biophysical Characterisation of the Four Swimming Techniques. Int Journal Sports Medical, 2020, vol. 41 (5), pp. 318–327. DOI: 10.1055/a-0975-9532
2. Chih-Wei Lin, Chun-Feng Huang, Jong-Shyan Wang et al. Detection of Ventilatory Thresholds Using Near-Infrared Spectroscopy with a Polynomial Regression Model. Saudi Journal of Biological Sciences, 2020, vol. 27, pp. 1637–1642. DOI: 10.1016/j.sjbs.2020.03.005
3. Conconi F., Grazze G., Casoni I. et al. The Conconi Test: Methodology After 12 Years of Application. Int. Journal Sports Medical, 1996, vol. 17, pp. 509–519. DOI: 10.1055/s-2007-972887
4. Douglas C.G. Coordination of the Respiration and Circulation with Variation in Bodily Activity. Lancet, 1927, vol. 312, pp. 213–218. DOI: 10.1016/S0140-6736(01)30762-6
5. Etxegaraia U., Portilloa E., Irazustab J. et al. Estimation of Lactate Threshold with Machine Learning Techniques in Recreational Runners. Applied Soft Computing, 2018, vol. 63, pp. 181–196. DOI: 10.1016/j.asoc.2017.11.036
6. Geir S., Robstad B., Skjønsberg O.H., Borchsenius F. Respiratory Gas Exchange Indices for Estimating the Threshold. Journal of Sports Science and Medicine, 2005, vol. 4, pp. 29–36.
7. Giovanelli N., Scaini S., Billat V., Lazzer S. A New Field Test to Estimate the Aerobic and Anaerobic Thresholds and Maximum Parameters. European Journal of Sport Science, 2020, vol. 20 (4), pp. 437–443. DOI: 10.1080/17461391.2019.1640289
8. Joo-ho Ham, Hun-Young Park, Youn-ho Kim et al. Development of an Anaerobic Thresh-old (HRLT, HRVT) Estimation Equation Using the Heart Rate Threshold (HRT) During the Treadmill Incremental Exercise Test. Journal Exercise Nutrition Biochemical, 2017, vol. 21 (3), pp. 43–49. DOI: 10.20463/jenb.2017.0016
9. Kozlov A.V., Vavaev A.V., Yurikov R.V. et al. A Method for the Evaluation of Anaerobic Threshold Based on Heart Rate Dynamics During Incremental Exercise Test and Recovery. Hu-man Physiology, 2019, vol. 45 (2), pp. 180–187. DOI: 10.1134/S0362119719020038
10. McGehee J.C., Tanner C.J., Houmard J.A. A Comparison of Methods for Estimating the Lactate Threshold. Journal of Strength and Conditioning Research, 2005, vol. 19 (3), pp. 553–558. DOI: 10.1519/15444.1
11. Messias L.H.D., Polisel E.E.C., Manchado-Gobatto F.B. Advances of the Reverse Lactate Threshold Test: Non-Invasive Proposal Based on Heart Rate and Effect of Previous Cycling Experience. Plos ONE, 2018, vol. 13, pp. 1–20. DOI: 10.1371/journal.pone.0194313
12. Moxnes J.F., Sandbakk Ø. Mathematical Modelling of the Oxygen Uptake Kinetics During Whole-Body Endurance Exercise and Recovery. Mathematical and Computer Modelling of Dynamical Systems, 2018, vol. 24 (1), pp. 76–86. DOI: 10.1080/13873954.2017.1348364
13. Najera J., Ortiz G., Lopez A. et al. Non Spirographic or Noninvasive Methods to Estimate Anaerobic Threshold. Physical Culture, 2017, vol. 71 (1), pp. 55–62. DOI: 10.5937/fizkul1701055N
14. Nakamura K., Nagasawa Y., Sawaki S., Yokokawa Y. Comparison of Original and Alter-native Incremental Sit-to-Stand Exercise Protocol for Anaerobic Threshold Assessment. Journal Physical Fitness Sports Medical, 2020, vol. 9 (2), pp. 83–88. DOI: 10.7600/jpfsm.9.83
15. Pallares J.G., Moran-Navarro R., Ortega1 J.F. et al. Validity and Reliability of Ventilatory and Blood Lactate Thresholds in Well-Trained Cyclists. Plos ONE, 2016, vol. 11 (9), pp. 1–16. DOI: 10.1371/journal.pone.0163389
16. Piucco T., Diefenthaeler F., Prosser A., Bini R. Validity of Different EMG Analysis Methods to Identify Aerobic and Anaerobic Thresholds in Speed Skaters. Journal of Electromyography and Kinesiology, 2020, vol. 52, 102422. DOI: 10.1016/j.jelekin.2020.102425
17. Proshin A.P., SolodyannikovYu.V. Mathematical Modeling of Lactate Metabolism with Applications to Sports. Automation and Remote Control, 2013, vol. 6, pp. 133–152. DOI: 10.1134/ S0005117913060106
18. Ringwood J.V. Anaerobic Threshold Measurement Using Dynamic Neural Network Models Author Links Open Overlay Panel. Computers in Biology and Medicine, 1999, vol. 29, pp. 259–271. DOI: 10.1016/S0010-4825(99)00008-6
19. Ringwood J.V., O'Neill J., Tallon P. et al. Non-Invasive Anaerobic Threshold Measurement Using Fuzzy Model Interpolation. 2014 IEEE Conference on Control Applications, 2014, pp. 1711–1715. DOI: 10.1109/CCA.2014.6981559
20. Vasconcelos G., Canestri R., Prado R.C.R. et al. A Comprehensive Integrative Perspective of the Anaerobic Threshold Engine. Physiology & Behavior, 2019, vol. 15, pp. 1–3. DOI: 10.1016/j.physbeh.2019.01.019
21. Wasserman K., Hansen J.E., Sue D.Y. et al. Principles of Exercise Testing and Interpretation, 3rd ed. Baltimore: Lipincott Williams & Wilkins, 1999. 551 p.
22. Zignoli A., Fornasiero A., Stella F. et al. Expert-Level Classification of Ventilatory Thresholds From Cardiopulmonary Exercising Test data with Recurrent Neural Networks. European Journal of Sport Science, 2019, vol. 19 (9), pp. 1221–1229. DOI: 10.1080/17461391.2019.1587523
2. Chih-Wei Lin, Chun-Feng Huang, Jong-Shyan Wang et al. Detection of Ventilatory Thresholds Using Near-Infrared Spectroscopy with a Polynomial Regression Model. Saudi Journal of Biological Sciences, 2020, vol. 27, pp. 1637–1642. DOI: 10.1016/j.sjbs.2020.03.005
3. Conconi F., Grazze G., Casoni I. et al. The Conconi Test: Methodology After 12 Years of Application. Int. Journal Sports Medical, 1996, vol. 17, pp. 509–519. DOI: 10.1055/s-2007-972887
4. Douglas C.G. Coordination of the Respiration and Circulation with Variation in Bodily Activity. Lancet, 1927, vol. 312, pp. 213–218. DOI: 10.1016/S0140-6736(01)30762-6
5. Etxegaraia U., Portilloa E., Irazustab J. et al. Estimation of Lactate Threshold with Machine Learning Techniques in Recreational Runners. Applied Soft Computing, 2018, vol. 63, pp. 181–196. DOI: 10.1016/j.asoc.2017.11.036
6. Geir S., Robstad B., Skjønsberg O.H., Borchsenius F. Respiratory Gas Exchange Indices for Estimating the Threshold. Journal of Sports Science and Medicine, 2005, vol. 4, pp. 29–36.
7. Giovanelli N., Scaini S., Billat V., Lazzer S. A New Field Test to Estimate the Aerobic and Anaerobic Thresholds and Maximum Parameters. European Journal of Sport Science, 2020, vol. 20 (4), pp. 437–443. DOI: 10.1080/17461391.2019.1640289
8. Joo-ho Ham, Hun-Young Park, Youn-ho Kim et al. Development of an Anaerobic Thresh-old (HRLT, HRVT) Estimation Equation Using the Heart Rate Threshold (HRT) During the Treadmill Incremental Exercise Test. Journal Exercise Nutrition Biochemical, 2017, vol. 21 (3), pp. 43–49. DOI: 10.20463/jenb.2017.0016
9. Kozlov A.V., Vavaev A.V., Yurikov R.V. et al. A Method for the Evaluation of Anaerobic Threshold Based on Heart Rate Dynamics During Incremental Exercise Test and Recovery. Hu-man Physiology, 2019, vol. 45 (2), pp. 180–187. DOI: 10.1134/S0362119719020038
10. McGehee J.C., Tanner C.J., Houmard J.A. A Comparison of Methods for Estimating the Lactate Threshold. Journal of Strength and Conditioning Research, 2005, vol. 19 (3), pp. 553–558. DOI: 10.1519/15444.1
11. Messias L.H.D., Polisel E.E.C., Manchado-Gobatto F.B. Advances of the Reverse Lactate Threshold Test: Non-Invasive Proposal Based on Heart Rate and Effect of Previous Cycling Experience. Plos ONE, 2018, vol. 13, pp. 1–20. DOI: 10.1371/journal.pone.0194313
12. Moxnes J.F., Sandbakk Ø. Mathematical Modelling of the Oxygen Uptake Kinetics During Whole-Body Endurance Exercise and Recovery. Mathematical and Computer Modelling of Dynamical Systems, 2018, vol. 24 (1), pp. 76–86. DOI: 10.1080/13873954.2017.1348364
13. Najera J., Ortiz G., Lopez A. et al. Non Spirographic or Noninvasive Methods to Estimate Anaerobic Threshold. Physical Culture, 2017, vol. 71 (1), pp. 55–62. DOI: 10.5937/fizkul1701055N
14. Nakamura K., Nagasawa Y., Sawaki S., Yokokawa Y. Comparison of Original and Alter-native Incremental Sit-to-Stand Exercise Protocol for Anaerobic Threshold Assessment. Journal Physical Fitness Sports Medical, 2020, vol. 9 (2), pp. 83–88. DOI: 10.7600/jpfsm.9.83
15. Pallares J.G., Moran-Navarro R., Ortega1 J.F. et al. Validity and Reliability of Ventilatory and Blood Lactate Thresholds in Well-Trained Cyclists. Plos ONE, 2016, vol. 11 (9), pp. 1–16. DOI: 10.1371/journal.pone.0163389
16. Piucco T., Diefenthaeler F., Prosser A., Bini R. Validity of Different EMG Analysis Methods to Identify Aerobic and Anaerobic Thresholds in Speed Skaters. Journal of Electromyography and Kinesiology, 2020, vol. 52, 102422. DOI: 10.1016/j.jelekin.2020.102425
17. Proshin A.P., SolodyannikovYu.V. Mathematical Modeling of Lactate Metabolism with Applications to Sports. Automation and Remote Control, 2013, vol. 6, pp. 133–152. DOI: 10.1134/ S0005117913060106
18. Ringwood J.V. Anaerobic Threshold Measurement Using Dynamic Neural Network Models Author Links Open Overlay Panel. Computers in Biology and Medicine, 1999, vol. 29, pp. 259–271. DOI: 10.1016/S0010-4825(99)00008-6
19. Ringwood J.V., O'Neill J., Tallon P. et al. Non-Invasive Anaerobic Threshold Measurement Using Fuzzy Model Interpolation. 2014 IEEE Conference on Control Applications, 2014, pp. 1711–1715. DOI: 10.1109/CCA.2014.6981559
20. Vasconcelos G., Canestri R., Prado R.C.R. et al. A Comprehensive Integrative Perspective of the Anaerobic Threshold Engine. Physiology & Behavior, 2019, vol. 15, pp. 1–3. DOI: 10.1016/j.physbeh.2019.01.019
21. Wasserman K., Hansen J.E., Sue D.Y. et al. Principles of Exercise Testing and Interpretation, 3rd ed. Baltimore: Lipincott Williams & Wilkins, 1999. 551 p.
22. Zignoli A., Fornasiero A., Stella F. et al. Expert-Level Classification of Ventilatory Thresholds From Cardiopulmonary Exercising Test data with Recurrent Neural Networks. European Journal of Sport Science, 2019, vol. 19 (9), pp. 1221–1229. DOI: 10.1080/17461391.2019.1587523
2. Chih-Wei Lin, Chun-Feng Huang, Jong-Shyan Wang et al. Detection of Ventilatory Thresholds Using Near-Infrared Spectroscopy with a Polynomial Regression Model. Saudi Journal of Biological Sciences, 2020, vol. 27, pp. 1637–1642. DOI: 10.1016/j.sjbs.2020.03.005
3. Conconi F., Grazze G., Casoni I. et al. The Conconi Test: Methodology After 12 Years of Application. Int. Journal Sports Medical, 1996, vol. 17, pp. 509–519. DOI: 10.1055/s-2007-972887
4. Douglas C.G. Coordination of the Respiration and Circulation with Variation in Bodily Activity. Lancet, 1927, vol. 312, pp. 213–218. DOI: 10.1016/S0140-6736(01)30762-6
5. Etxegaraia U., Portilloa E., Irazustab J. et al. Estimation of Lactate Threshold with Machine Learning Techniques in Recreational Runners. Applied Soft Computing, 2018, vol. 63, pp. 181–196. DOI: 10.1016/j.asoc.2017.11.036
6. Geir S., Robstad B., Skjønsberg O.H., Borchsenius F. Respiratory Gas Exchange Indices for Estimating the Threshold. Journal of Sports Science and Medicine, 2005, vol. 4, pp. 29–36.
7. Giovanelli N., Scaini S., Billat V., Lazzer S. A New Field Test to Estimate the Aerobic and Anaerobic Thresholds and Maximum Parameters. European Journal of Sport Science, 2020, vol. 20 (4), pp. 437–443. DOI: 10.1080/17461391.2019.1640289
8. Joo-ho Ham, Hun-Young Park, Youn-ho Kim et al. Development of an Anaerobic Thresh-old (HRLT, HRVT) Estimation Equation Using the Heart Rate Threshold (HRT) During the Treadmill Incremental Exercise Test. Journal Exercise Nutrition Biochemical, 2017, vol. 21 (3), pp. 43–49. DOI: 10.20463/jenb.2017.0016
9. Kozlov A.V., Vavaev A.V., Yurikov R.V. et al. A Method for the Evaluation of Anaerobic Threshold Based on Heart Rate Dynamics During Incremental Exercise Test and Recovery. Hu-man Physiology, 2019, vol. 45 (2), pp. 180–187. DOI: 10.1134/S0362119719020038
10. McGehee J.C., Tanner C.J., Houmard J.A. A Comparison of Methods for Estimating the Lactate Threshold. Journal of Strength and Conditioning Research, 2005, vol. 19 (3), pp. 553–558. DOI: 10.1519/15444.1
11. Messias L.H.D., Polisel E.E.C., Manchado-Gobatto F.B. Advances of the Reverse Lactate Threshold Test: Non-Invasive Proposal Based on Heart Rate and Effect of Previous Cycling Experience. Plos ONE, 2018, vol. 13, pp. 1–20. DOI: 10.1371/journal.pone.0194313
12. Moxnes J.F., Sandbakk Ø. Mathematical Modelling of the Oxygen Uptake Kinetics During Whole-Body Endurance Exercise and Recovery. Mathematical and Computer Modelling of Dynamical Systems, 2018, vol. 24 (1), pp. 76–86. DOI: 10.1080/13873954.2017.1348364
13. Najera J., Ortiz G., Lopez A. et al. Non Spirographic or Noninvasive Methods to Estimate Anaerobic Threshold. Physical Culture, 2017, vol. 71 (1), pp. 55–62. DOI: 10.5937/fizkul1701055N
14. Nakamura K., Nagasawa Y., Sawaki S., Yokokawa Y. Comparison of Original and Alter-native Incremental Sit-to-Stand Exercise Protocol for Anaerobic Threshold Assessment. Journal Physical Fitness Sports Medical, 2020, vol. 9 (2), pp. 83–88. DOI: 10.7600/jpfsm.9.83
15. Pallares J.G., Moran-Navarro R., Ortega1 J.F. et al. Validity and Reliability of Ventilatory and Blood Lactate Thresholds in Well-Trained Cyclists. Plos ONE, 2016, vol. 11 (9), pp. 1–16. DOI: 10.1371/journal.pone.0163389
16. Piucco T., Diefenthaeler F., Prosser A., Bini R. Validity of Different EMG Analysis Methods to Identify Aerobic and Anaerobic Thresholds in Speed Skaters. Journal of Electromyography and Kinesiology, 2020, vol. 52, 102422. DOI: 10.1016/j.jelekin.2020.102425
17. Proshin A.P., SolodyannikovYu.V. Mathematical Modeling of Lactate Metabolism with Applications to Sports. Automation and Remote Control, 2013, vol. 6, pp. 133–152. DOI: 10.1134/ S0005117913060106
18. Ringwood J.V. Anaerobic Threshold Measurement Using Dynamic Neural Network Models Author Links Open Overlay Panel. Computers in Biology and Medicine, 1999, vol. 29, pp. 259–271. DOI: 10.1016/S0010-4825(99)00008-6
19. Ringwood J.V., O'Neill J., Tallon P. et al. Non-Invasive Anaerobic Threshold Measurement Using Fuzzy Model Interpolation. 2014 IEEE Conference on Control Applications, 2014, pp. 1711–1715. DOI: 10.1109/CCA.2014.6981559
20. Vasconcelos G., Canestri R., Prado R.C.R. et al. A Comprehensive Integrative Perspective of the Anaerobic Threshold Engine. Physiology & Behavior, 2019, vol. 15, pp. 1–3. DOI: 10.1016/j.physbeh.2019.01.019
21. Wasserman K., Hansen J.E., Sue D.Y. et al. Principles of Exercise Testing and Interpretation, 3rd ed. Baltimore: Lipincott Williams & Wilkins, 1999. 551 p.
22. Zignoli A., Fornasiero A., Stella F. et al. Expert-Level Classification of Ventilatory Thresholds From Cardiopulmonary Exercising Test data with Recurrent Neural Networks. European Journal of Sport Science, 2019, vol. 19 (9), pp. 1221–1229. DOI: 10.1080/17461391.2019.1587523
References
1. Carvalho D.D., Soares S., Zacca R. et al. Anaerobic Threshold Biophysical Characterisation of the Four Swimming Techniques. Int Journal Sports Medical, 2020, vol. 41 (5), pp. 318–327. DOI: 10.1055/a-0975-95322. Chih-Wei Lin, Chun-Feng Huang, Jong-Shyan Wang et al. Detection of Ventilatory Thresholds Using Near-Infrared Spectroscopy with a Polynomial Regression Model. Saudi Journal of Biological Sciences, 2020, vol. 27, pp. 1637–1642. DOI: 10.1016/j.sjbs.2020.03.005
3. Conconi F., Grazze G., Casoni I. et al. The Conconi Test: Methodology After 12 Years of Application. Int. Journal Sports Medical, 1996, vol. 17, pp. 509–519. DOI: 10.1055/s-2007-972887
4. Douglas C.G. Coordination of the Respiration and Circulation with Variation in Bodily Activity. Lancet, 1927, vol. 312, pp. 213–218. DOI: 10.1016/S0140-6736(01)30762-6
5. Etxegaraia U., Portilloa E., Irazustab J. et al. Estimation of Lactate Threshold with Machine Learning Techniques in Recreational Runners. Applied Soft Computing, 2018, vol. 63, pp. 181–196. DOI: 10.1016/j.asoc.2017.11.036
6. Geir S., Robstad B., Skjønsberg O.H., Borchsenius F. Respiratory Gas Exchange Indices for Estimating the Threshold. Journal of Sports Science and Medicine, 2005, vol. 4, pp. 29–36.
7. Giovanelli N., Scaini S., Billat V., Lazzer S. A New Field Test to Estimate the Aerobic and Anaerobic Thresholds and Maximum Parameters. European Journal of Sport Science, 2020, vol. 20 (4), pp. 437–443. DOI: 10.1080/17461391.2019.1640289
8. Joo-ho Ham, Hun-Young Park, Youn-ho Kim et al. Development of an Anaerobic Thresh-old (HRLT, HRVT) Estimation Equation Using the Heart Rate Threshold (HRT) During the Treadmill Incremental Exercise Test. Journal Exercise Nutrition Biochemical, 2017, vol. 21 (3), pp. 43–49. DOI: 10.20463/jenb.2017.0016
9. Kozlov A.V., Vavaev A.V., Yurikov R.V. et al. A Method for the Evaluation of Anaerobic Threshold Based on Heart Rate Dynamics During Incremental Exercise Test and Recovery. Hu-man Physiology, 2019, vol. 45 (2), pp. 180–187. DOI: 10.1134/S0362119719020038
10. McGehee J.C., Tanner C.J., Houmard J.A. A Comparison of Methods for Estimating the Lactate Threshold. Journal of Strength and Conditioning Research, 2005, vol. 19 (3), pp. 553–558. DOI: 10.1519/15444.1
11. Messias L.H.D., Polisel E.E.C., Manchado-Gobatto F.B. Advances of the Reverse Lactate Threshold Test: Non-Invasive Proposal Based on Heart Rate and Effect of Previous Cycling Experience. Plos ONE, 2018, vol. 13, pp. 1–20. DOI: 10.1371/journal.pone.0194313
12. Moxnes J.F., Sandbakk Ø. Mathematical Modelling of the Oxygen Uptake Kinetics During Whole-Body Endurance Exercise and Recovery. Mathematical and Computer Modelling of Dynamical Systems, 2018, vol. 24 (1), pp. 76–86. DOI: 10.1080/13873954.2017.1348364
13. Najera J., Ortiz G., Lopez A. et al. Non Spirographic or Noninvasive Methods to Estimate Anaerobic Threshold. Physical Culture, 2017, vol. 71 (1), pp. 55–62. DOI: 10.5937/fizkul1701055N
14. Nakamura K., Nagasawa Y., Sawaki S., Yokokawa Y. Comparison of Original and Alter-native Incremental Sit-to-Stand Exercise Protocol for Anaerobic Threshold Assessment. Journal Physical Fitness Sports Medical, 2020, vol. 9 (2), pp. 83–88. DOI: 10.7600/jpfsm.9.83
15. Pallares J.G., Moran-Navarro R., Ortega1 J.F. et al. Validity and Reliability of Ventilatory and Blood Lactate Thresholds in Well-Trained Cyclists. Plos ONE, 2016, vol. 11 (9), pp. 1–16. DOI: 10.1371/journal.pone.0163389
16. Piucco T., Diefenthaeler F., Prosser A., Bini R. Validity of Different EMG Analysis Methods to Identify Aerobic and Anaerobic Thresholds in Speed Skaters. Journal of Electromyography and Kinesiology, 2020, vol. 52, 102422. DOI: 10.1016/j.jelekin.2020.102425
17. Proshin A.P., SolodyannikovYu.V. Mathematical Modeling of Lactate Metabolism with Applications to Sports. Automation and Remote Control, 2013, vol. 6, pp. 133–152. DOI: 10.1134/ S0005117913060106
18. Ringwood J.V. Anaerobic Threshold Measurement Using Dynamic Neural Network Models Author Links Open Overlay Panel. Computers in Biology and Medicine, 1999, vol. 29, pp. 259–271. DOI: 10.1016/S0010-4825(99)00008-6
19. Ringwood J.V., O'Neill J., Tallon P. et al. Non-Invasive Anaerobic Threshold Measurement Using Fuzzy Model Interpolation. 2014 IEEE Conference on Control Applications, 2014, pp. 1711–1715. DOI: 10.1109/CCA.2014.6981559
20. Vasconcelos G., Canestri R., Prado R.C.R. et al. A Comprehensive Integrative Perspective of the Anaerobic Threshold Engine. Physiology & Behavior, 2019, vol. 15, pp. 1–3. DOI: 10.1016/j.physbeh.2019.01.019
21. Wasserman K., Hansen J.E., Sue D.Y. et al. Principles of Exercise Testing and Interpretation, 3rd ed. Baltimore: Lipincott Williams & Wilkins, 1999. 551 p.
22. Zignoli A., Fornasiero A., Stella F. et al. Expert-Level Classification of Ventilatory Thresholds From Cardiopulmonary Exercising Test data with Recurrent Neural Networks. European Journal of Sport Science, 2019, vol. 19 (9), pp. 1221–1229. DOI: 10.1080/17461391.2019.1587523
Опубликован
2022-12-29
Как цитировать
Чиков, А., Павлов, Е., Егоров, Н., Медведев, Д., Чикова, С., & Дробинцев, П. (2022). МОДЕЛИРОВАНИЕ ФИЗИОЛОГИЧЕСКИХ ПОКАЗАТЕЛЕЙ НА УРОВНЕ ПОРОГА АНАЭРОБНОГО ОБМЕНА С ИСПОЛЬЗОВАНИЕМ МЕТОДОВ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА. Человек. Спорт. Медицина, 22(S2), 46-53. https://doi.org/10.14529/hsm22s206
Выпуск
Раздел
Физиология
Copyright (c) 2022 Человек. Спорт. Медицина
Это произведение доступно по лицензии Creative Commons «Attribution-NonCommercial-NoDerivatives» («Атрибуция — Некоммерческое использование — Без производных произведений») 4.0 Всемирная.
