CN-120983013-B - Exercise heart rate monitoring method and device based on intelligent watch

CN120983013BCN 120983013 BCN120983013 BCN 120983013BCN-120983013-B

Abstract

The invention relates to the field of heart rate monitoring, in particular to an exercise heart rate monitoring method and device based on a smart watch. The method comprises the steps of obtaining geographical position information of a user based on an intelligent watch, carrying out user movement scene recognition to generate movement scene types, collecting original heart rate detection parameters, carrying out time sequence heart rate feature analysis to construct a heart rate feature set, carrying out movement pattern depth recognition on the movement scene types and the heart rate feature set to obtain a user movement pattern, carrying out global heart rate situation sensing on the heart rate feature set to construct a global heart rate situation map, and carrying out comprehensive risk assessment and self-adaptive early warning decision on the global heart rate situation map according to the user movement pattern. The invention realizes self-adaptive analysis of the heart rate state of the user, and improves the safety evaluation efficiency and accuracy of the user movement.

Inventors

HU JUN

Assignees

深圳市爱保护科技有限公司
深圳叩鼎科技有限责任公司

Dates

Publication Date: 20260508
Application Date: 20251010

Claims (5)

1. An exercise heart rate monitoring method based on a smart watch is characterized by comprising the following steps: Step S1, acquiring geographical position information of a user based on an intelligent watch, and identifying a user motion scene so as to generate a motion scene type; S2, acquiring original heart rate detection parameters, performing time sequence heart rate characteristic analysis, and constructing a heart rate characteristic set; step S3, performing movement pattern depth recognition on the movement scene type and the heart rate feature set to obtain a user movement pattern; S4, performing global heart rate situation awareness on the heart rate feature set, and constructing a global heart rate situation map; S5, carrying out comprehensive risk assessment and self-adaptive early warning decision on the global heart rate situation map according to a user movement mode; the specific steps of the step S1 are as follows: Acquiring geographical position information and illumination environment signals of a user based on the intelligent watch; calculating the real-time altitude, longitude and latitude coordinates of the user according to the geographic position information of the user; Collecting current position weather parameters according to the geographical position information of the user to obtain the current position weather parameters; Performing environmental weather analysis on the weather parameters of the current position to generate environmental weather characteristics; Calculating the ambient illumination intensity of the illumination ambient signal, and analyzing the intensity variation to obtain an ambient light intensity distribution map; Carrying out user motion scene identification on the real-time altitude, longitude and latitude coordinates, environmental meteorological features and an environmental light intensity distribution map of a user so as to generate a motion scene type; The specific steps of the step S2 are as follows: continuous heart rate acquisition is carried out based on a pulse sensor arranged in the intelligent watch, so that an original heart rate detection parameter is obtained; Acquiring body posture parameters of a user according to an inertial sensing unit of the intelligent watch; Calculating the gesture change rate of the body gesture parameters of the user to generate the gesture change rate; Carrying out gesture interference dynamic gain on the original heart rate detection parameters according to the gesture change rate so as to generate dynamic gain heart rate parameters; carrying out multi-scale time window division and feature statistics on the dynamic gain heart rate parameters to construct a heart rate feature set; the specific steps of the step S3 are as follows: Calculating a user movement speed and a change movement rate based on the user geographical position information; Performing exercise intensity quantitative evaluation based on the heart rate feature set, the user movement speed and the change movement speed to obtain an exercise intensity evaluation value; Performing real-time motion estimation of a user according to the gesture change rate to obtain user motion estimation data; Performing real-time motion pattern depth recognition on the motion scene type, the user motion estimation data and the motion intensity evaluation value to obtain a user motion pattern; The specific steps of the step S4 are as follows: Performing global heart rate situation awareness on the heart rate feature set, and constructing a global heart rate situation map; the global heart rate situation sensing specifically comprises secondary stability analysis of a heart rate characteristic set to obtain heart rate stability characteristics, wherein the secondary stability analysis comprises heart rate rising time, stability duration time and recovery time; carrying out heart rate fluctuation mode identification on the heart rate feature set, analyzing the heart rate peak-valley period, fluctuation frequency and amplitude change rule, and generating heart rate fluctuation features; The heart rate change situation mining is carried out according to the heart rate characteristic set to obtain a heart rate situation mode, wherein the heart rate change situation mining comprises the steps of recognizing rising, falling, stabilizing and oscillating of the heart rate; performing self-adaptive heart rate standard analysis on heart rate stability characteristics, heart rate fluctuation characteristics and heart rate situation modes according to self-adaptive heart rate evaluation standards, and performing heart rate variation sensing to construct a global heart rate situation map; and acquiring skin temperature of the wrist of the user, and carrying out multi-step heart rate prediction of the user based on the global heart rate situation map, so as to construct a heart rate prediction map of the user.
2. The exercise heart rate monitoring method based on the smart watch according to claim 1, wherein the steps of performing multi-scale time window division and feature statistics on the dynamic gain heart rate parameter to construct a heart rate feature set are as follows: Dividing a multi-scale time window of the dynamic gain heart rate parameter to generate heart rate monitoring parameters of a plurality of time windows, wherein the heart rate monitoring parameters of the time windows comprise a15 second short period window, a 60 second medium period window and a 300 second long period window; Calculating a heart rate average value, a standard deviation and a variation coefficient of the heart rate monitoring parameters to obtain basic heart rate statistics; detecting heart rate monitoring parameters of a plurality of time windows window by window heart rate maximum values, and extracting heart rate peaks and valleys of different periods; calculating the heart rate dynamic range and fluctuation amplitude according to the heart rate peak value and the heart rate valley value; Performing time-frequency decomposition on the heart rate monitoring parameters, and performing power spectrum density calculation to obtain power spectrum densities of multiple scales; carrying out power fluctuation difference analysis according to the power spectral density to obtain heart rate variability characteristics; And performing time sequence distribution fitting on the basic heart rate statistics, heart rate variability characteristics, heart rate dynamic range and fluctuation amplitude to construct a heart rate characteristic set.
3. The exercise heart rate monitoring method based on the smart watch according to claim 1, wherein the specific steps of acquiring the skin temperature of the wrist of the user and performing multi-step heart rate prediction of the user based on the global heart rate situation map, thereby constructing the user heart rate prediction map are as follows: Acquiring the skin temperature of the wrist of the user based on the intelligent watch; body surface temperature change analysis is carried out on the wrist skin temperature of the user so as to obtain a temperature change characteristic curve; According to the temperature change characteristic curve and the global heart rate situation map, body temperature-heart rate coupling analysis is carried out, and body temperature-heart rate change association is generated; And performing multi-step heart rate prediction of the user based on the body temperature-heart rate variation association, so as to construct a heart rate prediction graph of the user.
4. The exercise heart rate monitoring method based on the smart watch according to claim 1, wherein the specific steps of step S5 are as follows: analyzing the age, physical energy level and health condition of the user based on the personal information of the user to obtain personalized user characteristics; Performing self-adaptive heart rate evaluation standard analysis based on the user movement mode and personalized user characteristics to obtain self-adaptive heart rate evaluation standards; Performing comprehensive risk assessment on the heart rate prediction graph of the user based on the self-adaptive heart rate assessment standard, so as to obtain a heart rate risk assessment result; And carrying out self-adaptive early warning decision based on the heart rate risk assessment result, and constructing a self-adaptive early warning strategy.
5. An exercise heart rate monitoring device based on a smart watch, for performing the exercise heart rate monitoring method based on a smart watch as claimed in claim 1, comprising: the scene recognition module is used for acquiring geographical position information of the user based on the intelligent watch and recognizing the motion scene of the user so as to generate a motion scene type; the chip analysis module is used for collecting original heart rate detection parameters, carrying out time sequence heart rate characteristic analysis and constructing a heart rate characteristic set; the motion recognition module is used for carrying out motion pattern depth recognition on the motion scene type and the heart rate feature set to obtain a user motion pattern; the heart rate situation sensing module is used for performing global heart rate situation sensing on the heart rate feature set and constructing a global heart rate situation map; And the heart rate risk assessment module is used for carrying out comprehensive risk assessment and self-adaptive early warning decision on the global heart rate situation map according to the user movement mode.

Description

Exercise heart rate monitoring method and device based on intelligent watch Technical Field The invention relates to the field of heart rate monitoring, in particular to an exercise heart rate monitoring method and device based on a smart watch. Background Heart rate monitoring plays a vital role as one of the core functions of smart watches, especially in the sports field. The heart rate not only reflects the instant health condition of the user, but also accurately reflects key information such as exercise intensity, physical stamina, fatigue degree, recovery condition and the like. Through real-time monitoring and analysis of heart rate data of a user, the intelligent watch can provide state feedback in the exercise process for the user, helps the user avoid excessive exercise, timely adjusts an exercise plan, and provides important basis for evaluation and optimization of exercise effect. However, with the wide application of smartwatches in sports health fields, conventional heart rate monitoring methods have failed to meet multiple demands of individuality, real-time performance, accuracy and the like. The existing heart rate monitoring technology based on the optical sensor can provide certain real-time data, but is limited by factors such as accuracy, external interference, wearing position and the like of the sensor, and stable and accurate heart rate data cannot be provided in a complex exercise environment. In addition, most of the existing intelligent watch heart rate monitoring systems can only provide single heart rate data, cannot analyze multidimensional information such as exercise intensity, fatigue degree and the like in real time, and also lack dynamic adjustment and personalized suggestions for different exercise scenes, so that monitoring results are often inaccurate, and comprehensive and scientific exercise health guidance is difficult to provide for users. Disclosure of Invention The invention provides a exercise heart rate monitoring method and device based on a smart watch to solve at least one technical problem. In order to achieve the above purpose, the invention provides an exercise heart rate monitoring method based on a smart watch, which comprises the following steps: Step S1, acquiring geographical position information of a user based on an intelligent watch, and identifying a user motion scene so as to generate a motion scene type; S2, acquiring original heart rate detection parameters, performing time sequence heart rate characteristic analysis, and constructing a heart rate characteristic set; step S3, performing movement pattern depth recognition on the movement scene type and the heart rate feature set to obtain a user movement pattern; S4, performing global heart rate situation awareness on the heart rate feature set, and constructing a global heart rate situation map; and S5, carrying out comprehensive risk assessment and self-adaptive early warning decision on the global heart rate situation map according to the user movement mode. In this specification, there is provided a smart watch based exercise heart rate monitoring device for performing the smart watch based exercise heart rate monitoring method as described above, including: the scene recognition module is used for acquiring geographical position information of the user based on the intelligent watch and recognizing the motion scene of the user so as to generate a motion scene type; the chip analysis module is used for collecting original heart rate detection parameters, carrying out time sequence heart rate characteristic analysis and constructing a heart rate characteristic set; the motion recognition module is used for carrying out motion pattern depth recognition on the motion scene type and the heart rate feature set to obtain a user motion pattern; the heart rate situation sensing module is used for performing global heart rate situation sensing on the heart rate feature set and constructing a global heart rate situation map; And the heart rate risk assessment module is used for carrying out comprehensive risk assessment and self-adaptive early warning decision on the global heart rate situation map according to the user movement mode. The intelligent watch has the advantages that the intelligent watch can identify sports scenes (such as running, road riding, mountain riding, walking and the like) in real time by acquiring the geographic position information (GPS data) of the user, so that more accurate background information is provided for subsequent heart rate analysis. The influence of each sport scene on the heart rate is different, so that the accurate identification of the scenes is beneficial to the customization of subsequent analysis. Different sports scenarios (e.g., running and riding) may react differently to heart rate. While running, heart rate fluctuations may be related to running speed and grade, while riding, heart rate may be closely related to leg strength and riding posture. By ac