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CN-121971073-A - Real-time evaluation and early warning system for sports injury risk

CN121971073ACN 121971073 ACN121971073 ACN 121971073ACN-121971073-A

Abstract

The invention discloses a real-time evaluation and early warning system for risk of sports injury, which comprises intelligent wearing equipment for acquiring multidimensional physiological data and a remote analysis server for performing deep analysis; the server analyzes the heart load, the body fluid balance state and the muscle and joint stability characteristics extracted from the kinematic parameters through fusion, carries out real-time risk assessment and grading early warning according to personalized thresholds, and issues specific intervention instructions; according to the invention, through the end cloud cooperative architecture, real-time perception of the risk factors of the sports injury is realized, professional laboratory-level analysis capability is integrated into daily wearable equipment, the contradiction between single early warning dimension of the traditional equipment and incapability of being used in real time and conveniently by a professional system is solved, accurate and prospective injury prevention guidance can be provided for users, and the sports safety is greatly improved.

Inventors

  • XIE WEIFAN
  • DAI CHUNLI
  • WANG WEICHAO
  • YANG LIFANG

Assignees

  • 广东开放大学（广东理工职业学院）

Dates

Publication Date
20260505
Application Date
20260130

Claims (10)

  1. 1. The real-time sports injury risk assessment and early warning system is characterized by comprising intelligent wearing equipment and a remote analysis server in communication connection with the intelligent wearing equipment; the intelligent wearing equipment is worn in user's limbs, includes: The data acquisition module is used for continuously acquiring real-time exercise physiological data of a user, wherein the real-time exercise physiological data at least comprises kinematic parameters from the inertial measurement unit, heart rate data from the optical heart rate sensor and galvanic skin activity data from the bioelectrical impedance or the galvanic skin sensor; the primary processing and communication module is used for carrying out local preprocessing on the acquired real-time motion physiological data and wirelessly transmitting the preprocessed data to the remote analysis server; The early warning execution module is used for receiving and executing early warning instructions from the remote analysis server, wherein the early warning instructions at least comprise visual warning signals corresponding to different risk levels; The remote analysis server includes: a depth analysis engine for receiving the preprocessed data and performing the following analysis: a1, calculating a heart load coefficient based on heart rate data and time sequence change characteristics thereof and combining a resting heart rate of a user and an estimated maximum heart rate of age; a2, estimating a body fluid balance state index of a user through a preset body fluid loss correlation model based on baseline drift characteristics of the skin electric activity data and activity intensity in the kinematic parameters; A3, extracting stability characteristics reflecting a muscle group cooperative activation mode under the current motion gesture of a user and abnormal fluctuation characteristics reflecting the intensity of the change of the joint movement angle through a built-in motion mode recognition algorithm based on the kinematic parameters; A risk decision module for: B1, carrying out multi-mode data fusion on heart load coefficient, body fluid balance state index, muscle collaborative activation mode stability characteristics and joint movement abnormal fluctuation characteristics; B2, inquiring a preset multilevel risk threshold value matched with the personal basic attribute and the motion type of the user according to the fusion result; B3, generating a current exercise risk level judgment according to the query result, wherein the risk level at least comprises a green safety level, a yellow attention level, an orange warning level and a red high risk level from low to high; The strategy issuing module is used for generating corresponding early warning instructions and supplementary suggestions containing text contents of specific intervention measures based on risk level judgment and pushing the corresponding early warning instructions and the supplementary suggestions to the early warning executing module of the intelligent wearable device.
  2. 2. The system for real-time assessment and early warning of risk of athletic injury according to claim 1, wherein the inertial measurement unit in the data acquisition module comprises at least a tri-axial accelerometer and a tri-axial gyroscope, and wherein the kinematic parameters comprise at least limb acceleration, angular velocity and limb posture angle calculated by a sensor fusion algorithm.
  3. 3. The real-time exercise injury risk assessment and early warning system according to claim 1, wherein the construction of the body fluid loss correlation model specifically comprises the steps of establishing a mapping relation between a low-frequency component change trend of skin electric activity data and exercise sweat rate, integrating environment temperature data and exercise intensity, and compensating and correcting the estimated body fluid loss so as to output a body fluid balance state index.
  4. 4. The real-time risk assessment and early warning system for exercise injury according to claim 1, wherein the extraction of the stability characteristics of the muscle cooperative activation mode is specifically that, in a preset standard exercise period, the covariance matrix principal component variation of a specific feature vector composed of the acceleration and the angular velocity of the limb is analyzed, and the degree of deviation from the typical mode is quantified as the stability characteristics.
  5. 5. The system for real-time assessment and early warning of athletic injury risk according to claim 1, wherein the deep analysis engine comprises a deep learning model based on a time-series neural network, wherein the model takes a preprocessed time-series data stream as input, takes heart load factor, body fluid balance state index and muscle collaborative activation mode stability characteristics as intermediate layers for implicit characterization, and directly outputs fusion characteristic vectors for risk decision.
  6. 6. The sports injury risk real-time assessment and early warning system according to claim 1, wherein the remote analysis server further comprises a user portrait database for storing and continuously updating personal basic attributes, historical sports data and personalized risk threshold adjustment parameters of the user, and the risk decision module preferentially invokes a personalized threshold matched with the current user portrait when querying the risk threshold.
  7. 7. The real-time athletic injury risk assessment and early warning system of claim 1, wherein the early warning execution module, when executing the early warning instructions, additionally triggers an audible alarm for the yellow attention level and the higher risk level with haptic vibratory feedback of different frequencies and patterns, for the orange warning level and the red high risk level.
  8. 8. The real-time athletic injury risk assessment and early warning system of claim 1, wherein the supplemental advice generated by the policy issuing module includes at least one of advice to supplement a specific volume of moisture, advice to reduce the rate or magnitude of exercise, advice to adjust a specific limb movement posture, and advice to immediately stop exercise and take a break, depending on the different risk levels and trigger reasons.
  9. 9. The system for real-time risk assessment and early warning of athletic injury according to claim 1, wherein the remote analysis server further comprises a long-term analysis module for statistically analyzing the risk assessment results of single and multiple exercises of the user, generating an athletic safety report reflecting the weak links of the user, and providing periodic training load adjustment advice.
  10. 10. The sports injury risk real-time assessment and early warning system according to claim 1, wherein the intelligent wearable device is an intelligent sports bracelet.

Description

Real-time evaluation and early warning system for sports injury risk Technical Field The invention relates to the technical field of sports health monitoring, in particular to a sports injury risk real-time assessment and early warning system. Background In the prior art, the intelligent monitoring equipment can track basic physiological and activity indexes such as heart rate, step number, calorie consumption and the like of a user, part of advanced products can also provide single threshold reminding of overhigh heart rate or excessive exercise, and other professional exercise analysis systems can carry out more complex offline analysis on technical actions and muscle loads of athletes in a laboratory or in a specific training scene by combining a plurality of inertial sensors or surface myoelectric sensors so as to evaluate the injury risk of the athletes. Most of the existing consumer-level-oriented wearable devices have single functions, and usually only perform isolated, post-statistics type recording and simple warning aiming at individual indexes such as heart rate, so that multiple potential risk factors (such as dehydration, muscle fatigue accumulation, movement deformation and the like) in movement cannot be comprehensively and real-time perceived and judged. For example, a runner may be in a "safe" range of heart rates, but may have a body fluid imbalance (pre-dehydration) due to sustained perspiration or reduced running posture stability due to fatigue, where a single heart rate monitor would ignore these important risk of injury signals altogether. In addition, while specialized laboratory analysis systems can provide more insight, they rely on complex and expensive equipment, require specialized personnel to operate and interpret, and have significant hysteresis in the analysis results, and are not able to provide immediate feedback and intervention during exercise, thus making them difficult to apply to real-time protection of general day-to-day exercise. Therefore, in order to solve the above-mentioned problems, the present invention provides a real-time risk assessment and early warning system for sports injury. Disclosure of Invention In order to solve the problems that the risk perception dimension of the conventional consumer-level exercise monitoring equipment is single and early warning is lagged, and the professional analysis system cannot realize real-time and convenient daily application, the invention provides a real-time exercise injury risk assessment and early warning system which is used for realizing prepositive and active prevention of exercise injury. The technical scheme of the invention is that the real-time risk assessment and early warning system for sports injury comprises an intelligent sports bracelet and a remote analysis server in communication connection with the intelligent sports bracelet; The intelligent exercise bracelet is worn in user's limbs, includes: The data acquisition module is used for continuously acquiring real-time exercise physiological data of a user, wherein the real-time exercise physiological data at least comprises kinematic parameters from the inertial measurement unit, heart rate data from the optical heart rate sensor and galvanic skin activity data from the bioelectrical impedance or the galvanic skin sensor; the primary processing and communication module is used for carrying out local preprocessing on the acquired real-time motion physiological data and wirelessly transmitting the preprocessed data to the remote analysis server; The early warning execution module is used for receiving and executing early warning instructions from the remote analysis server, wherein the early warning instructions at least comprise visual warning signals corresponding to different risk levels; The remote analysis server includes: a depth analysis engine for receiving the preprocessed data and performing the following analysis: a1, calculating a heart load coefficient based on heart rate data and time sequence change characteristics thereof and combining a resting heart rate of a user and an estimated maximum heart rate of age; a2, estimating a body fluid balance state index of a user through a preset body fluid loss correlation model based on baseline drift characteristics of the skin electric activity data and activity intensity in the kinematic parameters; A3, extracting stability characteristics reflecting a muscle group cooperative activation mode under the current motion gesture of a user and abnormal fluctuation characteristics reflecting the intensity of the change of the joint movement angle through a built-in motion mode recognition algorithm based on the kinematic parameters; A risk decision module for: B1, carrying out multi-mode data fusion on heart load coefficient, body fluid balance state index, muscle collaborative activation mode stability characteristics and joint movement abnormal fluctuation characteristics; B2, inquiring a preset mult