Human-in-loop intelligent training load curve optimization algorithm based on evaluation indexes

Abstract

The invention relates to a training load curve optimization algorithm, in particular to a human-in-loop intelligent training load curve optimization algorithm based on evaluation indexes, which adoptsa parameterization model of training loads, a motion phase synchronization system, an intelligent evaluation index planning system, a physiological signal measurement system and a master control system. In the training process, physiological signals of a human body are monitored in real time to obtain evaluation indexes of the current human body, a parameterized model of a training load is changed in real time under the control of a master control system, and a training load curve corresponding to the model is applied to a trainee through a motion phase synchronization system. Therefore, thereal-time human body evaluation indexes of the trainee can accord with the evaluation index optimal change curve generated based on the intelligent evaluation index planning system in the training process. According to the invention, the corresponding optimal evaluation index curve can be generated for different training purposes, and compared with a single constant evaluation index, the time-varying curve can better conform to the complex characteristic of the system of the human body.

Description

technical field [0001] The invention relates to a training load curve optimization algorithm, in particular to an evaluation index-based human-in-the-loop intelligent training load curve optimization algorithm. Background technique [0002] From 2010 to 2020, the number of people participating in physical exercise in my country has increased significantly. According to the National Bureau of Statistics, it is estimated that by 2020, the total output value of my country's sports industry will exceed 3 trillion yuan, the number of people who regularly participate in exercise will reach 435 million, and the per capita sports field area will reach 1.8 square meters. . As the number of trainees increases, there is a common problem that people exercise for a variety of purposes. The general public needs to learn a lot of training knowledge by themselves and under the guidance of personal trainers or health coaches, in order to effectively achieve their goals. This kind of trainin...

AI Technical Summary

Problems solved by technology

If the indicators are time-varying during the training process, it is difficult for trainers and coaches alone to achieve

[0008] Secondly, smart fitness proposes more complex fitness indicators that are closer to the real situation of the human body, such as fatigue, energy consumption, blood oxygen content, etc. Trainers can only roughly achieve them based on their own experience. The current smart fitness system control loop It also does not include people, so it is impossible to accurately realize the precise training method based on fitness indicators

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