#Ученичество

| #Ученичество. 2026. Вып. 1 | #Apprenticeship. 2026. Issue 1 20 DIGITAL MANAGEMENT OF FINSWIMMERS’ ADAPTATION TO IMPULSE-VARIABLE LOADS BASED ON NEURAL NETWORK MONITORING OF FUNCTIONAL STATE Abstract. This paper presents a model fo r the digital managem ent of adaptation in flipper swimmers to impulse-variable loads, based on neural network monitoring of their functional state and competition performance parameters. An eight-week program was implemented on a limited sample of 34 athletes aged 13–15 (with at least 6 years of systematic training experience and qualifications of 1st-class athlete or Candidate Master of Sports). The participants were divided into a control group (CG, n=17) and an experimental group (EG, n=17). The EG underwent a specialized impulse-variable training module twice a week, with the dosing and correction of the training load being guided by data from wearable sensors and neural network analytics. The CG followed a traditional training program for the stage of in-depth specialization without any digital management loop for load correction. Assessments before and after the program included: performance in the 100 m surface flipper swim, time for the final segment (last 25 m), a speed maintenance index in a 6x25 m test with fixed rest intervals, as well as recovery indicators based on heart rate variability and subjective ratings of perceived exertion. The EG showed statistically significant improvements: a 3.4% reduction in 100 m time, a 5.6% acceleration in the final segment, and a simultaneous 13.2% decrease in the speed decline index (p<0.05). No statistically significant changes were observed in the CG. Analysis of the obtained data indicates that integrating neural network monitoring into the training management cycle allows for the refinement of training load parameters according to the athletes' current functional state. This helps prevent adaptive responses from crossing into the zone of latent fatigue, which is particularly detrimental to maintaining competitive speed during the final part of the race. Keywords: Finswimming, digital monitoring, neural network technologies, impulse-variable training, special endurance, final segment, load personalization © Дудченко П. П., Хмылова Д. А. 2026 © Dudchenko P. P., Khmylova D. A. 2026 Плавание в ластах относится к видам спорта, где соревновательный результат определяется не только предельной скоростью, но и устойчивостью реализации скоростно-силового потенциала на фоне нарастающего утомления, особенно в заключительной части дистанции [4, c. 88]. Именно финишный сегмент в возрастной группе 13–15 лет часто становится зоной наибольших потерь времени, потому что у спортсменов еще не завершено формирование устойчивых механизмов поддержания темпа и эффективной координации при высокой интенсивности работы [3, с. 141]. Современная практика подготовки на этапе углубленной специализации все чаще опирается на краткосрочные высокоинтенсивные блоки, однако их педагогическая результативность резко снижается при недостаточно точном дозировании, поскольку формально одинаковая по плану тренировочная нагрузка вызывает неодинаковую физиологическую и психофизиологическую цену у разных спортсменов [7]. Dudchenko Pavel Pavlovich Candidate of Pedagogical Sciences Tula State Lev T olstoy Pedagogical University Tula, Russia pasith@mail.ru Khmylova Dina Alekseevna Senior Lecturer Herzen Univers ity St. Petersburg, Russia khmylova99@mail.ru