DEEP LEARNING ANALYSIS ON THE RESULTING IMPACTS OF WEEKLY LOAD TRAINING ON STUDENTS’ BIOLOGICAL SYSTEM

ANÁLISE DO APRENDIZADO PROFUNDO NOS IMPACTOS RESULTANTES DOS TREINOS SEMANAIS DE CARGA SOBRE O SISTEMA BIOLÓGICO DOS ESTUDANTES

ANÁLISIS DE APRENDIZAJE PROFUNDO SOBRE LOS IMPACTOS RESULTANTES DE LOS ENTRENAMIENTOS SEMANALES DE CARGA EN EL SISTEMA BIOLÓGICO DE LOS ESTUDIANTES

ABSTRACT

Introduction

The recent development of the deep learning algorithm as a new multilayer network machine learning algorithm has reduced the problem of traditional training algorithms easily falling into minimal places, becoming a recent direction in the learning field.

Objective

Design and validate an artificial intelligence model for deep learning of the resulting impacts of weekly load training on students’ biological system.

Methods

According to the physiological and biochemical indices of athletes in the training process, this paper analyzes the actual data of athletes’ training load in the annual preparation period. The characteristics of athletes’ training load in the preparation period were discussed. The value, significance, composition factors, arrangement principle and method of calculation, and determination of weekly load density using the deep learning algorithm are discussed.

Results

The results showed that the daily 24-hour random sampling load was moderate intensity, low and high-intensity training, and enhanced the physical-motor system and neural reactivity.

Conclusion

The research shows that there can be two activities of “teaching” and “training” in physical education and sports training. The sports biology monitoring research proves to be a growth point of sports training research with great potential for expansion for future research. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

Deep Learning; Physical Education and Training; Biology; Athletic Performance