Athlete physical condition state multi-parameter dynamic monitoring and early warning method and system thereof

By simultaneously acquiring multi-channel electromyography and kinematic data, performing two-level correction processing, and calculating the neuromuscular activation efficiency index, the problem of distortion in the calculation of total muscle activation intensity in existing technologies is solved, enabling precise monitoring and early warning of athletes' physical condition.

CN122229436APending Publication Date: 2026-06-19JIAXING NANYANG POLYTECHNIC INST
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Patent Information

Application Number
CN202610323021.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-03-17
Publication Date
2026-06-19

AI Technical Summary

Technical Problem

Existing methods for monitoring athletes' physical condition cannot identify poor muscle coordination, excessive co-activation, or disordered timing, leading to distorted calculations of total muscle activation intensity and an inability to accurately assess the fatigue level of the neuromuscular system.

Method used

By simultaneously acquiring multi-channel electromyography and kinematic raw data, muscle coordination and movement efficiency parameters are extracted, two-level correction processing is performed, the neuromuscular activation efficiency index is calculated, and a dual threshold is used for early warning.

Benefits of technology

It enables precise monitoring and early warning of neuromuscular activation efficiency, eliminates interference from ineffective activation in the assessment, and improves the accuracy and repeatability of the assessment.

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Abstract

This invention discloses a method and system for dynamic monitoring and early warning of athletes' physical fitness status using multiple parameters, relating to the field of sports training monitoring technology. The method includes the following steps: simultaneously acquiring multi-channel electromyography (EMG) raw signals and kinematic raw data generated by the target athlete during the execution of maximal voluntary contraction movements; processing the multi-channel EMG raw signals to obtain the initial total muscle activation intensity; and extracting muscle coordination characteristic parameters from the multi-channel EMG raw signals and movement efficiency parameters from the kinematic raw data. This invention achieves precise and layered removal of ineffective activation components from the initial total muscle activation intensity through a two-stage progressive correction, solving the core problem of activation intensity calculation distortion in traditional methods.
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