Method and system for monitoring exercise load and early warning of exercise fatigue based on exercise training data

Abstract

The invention provides an exercise training data based exercise load monitoring and exercise fatigue warning method and system. Two stages, namely an exercise load monitoring stage and an exercise fatigue warning stage are included. Date applied to the exercise load monitoring stage is easy to acquire, parallel optimization algorithm based on big-data neural network is adopted, high precision estimation of exercise load and intensity is performed according to determined exercise modes and acceleration data, and exercise training is monitored in real time; in the exercise fatigue warning stage,reasonable exercise fatigue time measuring units are divided according to characteristics of different exercises, descending mode of the exercise load in time unit is monitored in real time accordingto different modes of fatigue accumulation, exercise fatigue is warned by applying a Bayesian classification algorithm, over training leading to exercise injuries is prevented, and exercise injuriescaused by over training can be effectively avoided.

Description

technical field [0001] The invention belongs to the field of motion data analysis, and in particular relates to a method and system for exercise load monitoring and exercise fatigue early warning based on exercise training data. Background technique [0002] With the rapid development of computer software and hardware technology and network, the use of cameras, sensors and wireless sensor networks can collect massive amounts of sports training data in real time. In the face of massive sports training data, traditional data processing methods are faced with new severe challenges. The large amount, diversity, rapidity and low value density of sports training big data make traditional data processing methods and tools only limited. Can expect "data" to sigh. How to effectively construct mathematical models and tools suitable for sports data and truly transform massive sports training data into valuable information is an urgent problem to be solved in the field of sports load a...

AI Technical Summary

Problems solved by technology

[0005] 1. The existing exercise load monitoring relies too much on physiological and biochemical data, and the data collection is relatively complicated; 2. The collection of physiological and biochemical data is time-sensitive. If the collection is not timely, the acquired data will have large errors; 3. Existing sports There is lag in the training monitoring method, which cannot reflect the occurrence and development of sports fatigue in real time, and cannot provide early warning of excessive fatigue and sports injuries in time

4. Existing exercise load calculation methods are applied to small-scale exercise data, and are not applicable to the analysis of large-scale exercise data

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