CN-121641322-B - Intelligent Internet of things-based old people physical exercise health monitoring data processing method and system

CN121641322BCN 121641322 BCN121641322 BCN 121641322BCN-121641322-B

Abstract

The invention discloses a physical exercise health monitoring data processing method and system for old people based on an intelligent Internet of things, which relate to the technical field of health monitoring data processing and comprise the following steps: and acquiring light intensity data, surface reflectivity data and acceleration data in a health monitoring data processing link, and performing time alignment processing on the acquired light intensity data, surface reflectivity data and acceleration data to generate time sequence characteristics of an exercise scene. According to the invention, the time sequence characteristics of the exercise scene are constructed through synchronous acquisition and time alignment of multi-source data, the illumination and motion interference distinction is realized, the signal continuity is kept by combining the reverse phase sampling and position marking, the light source is dynamically regulated and controlled through the intermittent shading rhythm linkage sampling rhythm and the brightness threshold value, the blood flow signal is corrected in real time, and the stable and reliable health monitoring data is ensured.

Inventors

ZHANG DONG
Fang Yanting
ZHENG LIJIE
YANG RAN
ZHOU QIXING

Assignees

闽南理工学院

Dates

Publication Date: 20260512
Application Date: 20260205

Claims (9)

1. The physical exercise health monitoring data processing method for the old based on the intelligent Internet of things is characterized by comprising the following steps of: Collecting light intensity data, surface reflectivity data and acceleration data in a health monitoring data processing link, and performing time alignment processing on the collected light intensity data, surface reflectivity data and acceleration data to generate exercise scene time sequence characteristics; extracting an illumination intensity change section based on the time sequence features of the exercise scene, and distinguishing a motion factor and an illumination factor in the illumination intensity change section by combining a gesture parameter and a step frequency parameter to obtain an interference false peak feature set; Setting an inverse sampling window according to the interference false peak feature set, performing inverse sampling operation on the optical monitoring signal in the inverse sampling window to inhibit the same-frequency flicker interference, and generating a position mark point; The position mark point generation steps are as follows: Determining the time range and the duration period of an inverted sampling window according to the interference false peak feature set, taking the interference duration time in the illumination interference feature as the basic boundary of the time window, and adding buffer intervals at two ends of a time axis to envelope the interference peak change; the sampling points of the optical monitoring signal are selected and matched with the phases in the reverse sampling window, so that each pair of sampling points are respectively positioned at the ascending section and the descending section of the interference peak value change, and the reverse offset of the signal at the time level is realized; Performing time positioning on the optical monitoring signal subjected to the inverse sampling processing in the inverse sampling window to generate a traceable position marking point carrying time index and interference type information; Performing boundary smoothing on the optical monitoring signals in the inverted sampling window, and incorporating the position mark points into the time sequence characteristics to form an optical signal time line; Performing time resampling and synchronous alignment on heart rate curve data and step frequency curve data based on the position mark points, generating a short-time shading control instruction in the time resampling process, and forming an intermittent shading rhythm according to the short-time shading control instruction; Based on the intermittent shading rhythm, the linkage optical sensing sampling rhythm and the ambient brightness threshold, the operations of advanced backspacing sampling, time-staggered sampling and sectional closing of the light source are executed according to the intermittent shading rhythm, and the blood flow trend data is corrected on line.
2. The method for processing the physical exercise health monitoring data of the elderly based on the intelligent internet of things according to claim 1, wherein the exercise scene time series feature generation step is as follows: setting optical sensing units and acceleration sensing units distributed at key movement parts of the old people in a health monitoring data acquisition stage, and respectively acquiring light intensity data, surface reflectivity data and acceleration data; Generating a time index table according to the collected light intensity data, surface reflectivity data and acceleration data, and establishing a time corresponding relation of three types of data by taking the optical data sampling moment as a time reference; Performing time alignment processing on the light intensity data, the surface reflectivity data and the acceleration data based on the time index table, so that each time node comprises a light intensity change feature, a reflectivity change feature and an acceleration change feature; And performing feature integration on the time-aligned light intensity data, the surface reflectivity data and the acceleration data to generate time sequence features of the exercise scene, and reflecting the time correlation between the motion state and the illumination change.
3. The method for processing physical exercise health monitoring data of the elderly based on the intelligent internet of things according to claim 2, wherein the time alignment process uses the optical data sampling time as a unified time reference, a matched time point set is generated by comparing the time stamps of the surface reflectivity data and the acceleration data, and the time distribution of the acceleration data is adjusted according to the optical signal sampling rhythm, so that each light intensity change point corresponds to one reflectivity change point and acceleration change point, thereby forming a continuous exercise scene time sequence feature on the unified time axis.
4. The method for processing the physical exercise health monitoring data of the elderly based on the intelligent internet of things according to claim 2, wherein the interference false peak feature set generating step is as follows: continuously scanning light intensity change characteristics in the time sequence characteristics of the exercise scene, determining the initial range of the illumination intensity change section according to the amplitude trend of the light intensity change, and determining the reflection response accompanied by the illumination change by combining the surface reflectivity change characteristics; After the illumination intensity change section is determined, synchronously extracting acceleration change characteristics in the same time period to form time corresponding data of the gesture parameters and the step frequency parameters; According to the time association relation of the light intensity change characteristics, the reflectivity change characteristics, the gesture parameters and the step frequency parameters, the factor discrimination is carried out on the illumination intensity change section so as to distinguish the movement factors and the illumination factors; and carrying out structural integration on the characteristic data of the illumination factor section and the motion factor section to generate an interference false peak characteristic set.
5. The method for processing the physical exercise health monitoring data of the elderly based on the intelligent Internet of things according to claim 4 is characterized in that when an interference false peak feature set is generated, the illumination interference feature is identified according to the light intensity variation amplitude, the reflectivity synchronous response degree and the interference duration time, the movement interference feature is classified according to the acceleration variation peak value, the gesture angle variation range and the step frequency synchronous deviation, and the corresponding relation between the illumination interference feature and the movement interference feature is established by taking a time index as a core.
6. The method for processing physical exercise health monitoring data of the elderly based on the intelligent internet of things according to claim 1, wherein the inverted sampling window is based on the start and stop time of the illumination interference characteristic when the time range is determined, buffer intervals are respectively arranged at two ends of the interference duration to cover the rising and attenuation stages of the interference peak change, position mark points are generated in the middle period of the illumination interference section, and the balance time of the optical monitoring signal after the inverted sampling is identified and used as a reference node for resampling the subsequent time.
7. The method for processing physical exercise health monitoring data of the elderly based on the intelligent internet of things according to claim 1, wherein the intermittent shading rhythm forming step is as follows: Using the position mark points as time anchor points, performing time range matching on heart rate curve data and step frequency curve data to correspond to the nodes, so that two data sequences have uniform time references at each mark point position; taking the position mark point as an initial node, and performing time resampling on the heart rate curve data and the step frequency curve data to ensure that the two types of signals are consistent in time distribution and trackable time node information is obtained; generating a short-time shading control instruction according to the synchronous state of the heart rate curve and the step frequency curve in the time resampling process, wherein the short-time shading control instruction is used for controlling the illumination period and the exposure rhythm in the optical sensing acquisition process; And (3) integrating time sequencing and rhythms of the short-time shading control instructions to form an intermittent shading rhythm with a periodic rhythm so as to realize dynamic coordination of the optical acquisition process.
8. The method for processing the physical exercise health monitoring data of the elderly based on the intelligent internet of things according to claim 7, wherein the steps of performing sampling and light source control based on the intermittent shading rhythm linkage optical sensing sampling rhythm and the ambient brightness threshold value are as follows: establishing a time linkage relation according to the intermittent shading rhythm and the optical sensing sampling rhythm, and introducing an ambient brightness threshold value to judge the illumination state, so that the optical signal sampling and the illumination control are synchronously associated in time; executing the advanced back-off sampling operation according to the intermittent shading rhythm, so that the sampling window finishes signal acquisition before illumination mutation to keep sampling stability; Performing time-staggered sampling operation after the advanced backspacing sampling, and enabling the optical sampling point to generate time offset with the ambient light change period by adjusting the sampling triggering interval so as to inhibit the same-frequency flicker effect; and executing the operation of turning off the light source in a subsection way according to the intermittent shading rhythm, and carrying out on-line correction on blood flow trend data by utilizing a time anchor point obtained by the advanced back-off sampling and the time-staggered sampling so as to maintain time continuity and data stability.
9. The intelligent Internet of things-based old people physical exercise health monitoring data processing system for realizing the intelligent Internet of things-based old people physical exercise health monitoring data processing method according to any one of claims 1-8 is characterized by comprising a multi-source data synchronous acquisition module, an interference feature recognition module, an anti-phase sampling suppression module, a time resampling and shading control module and a rhythm linkage correction module: the multi-source data synchronous acquisition module acquires light intensity data, surface reflectivity data and acceleration data in a health monitoring data processing link, performs time alignment processing on the acquired light intensity data, surface reflectivity data and acceleration data, and generates exercise scene time sequence characteristics; The interference feature recognition module is used for extracting an illumination intensity change section based on the time sequence features of the exercise scene, and distinguishing the motion factors and the illumination factors in the illumination intensity change section by combining the gesture parameters and the step frequency parameters to obtain an interference false peak feature set; The anti-phase sampling suppression module is used for setting an anti-phase sampling window according to the interference false peak characteristic set, executing anti-phase sampling operation on the optical monitoring signal in the anti-phase sampling window so as to suppress the same-frequency flicker interference, and generating a position mark point; the time resampling and shading control module is used for executing time resampling and synchronous alignment on the heart rate curve data and the step frequency curve data based on the position mark points, generating a short-time shading control instruction in the time resampling process, and forming an intermittent shading rhythm according to the short-time shading control instruction; And the rhythm linkage correction module is used for carrying out advanced backset sampling, time-staggered sampling and sectional light source closing operation according to the intermittent shading rhythm based on the intermittent shading rhythm linkage optical sensing sampling rhythm and the environmental brightness threshold value, and carrying out online correction on blood flow trend data.

Description

Intelligent Internet of things-based old people physical exercise health monitoring data processing method and system Technical Field The invention relates to the technical field of health monitoring data processing, in particular to a physical exercise health monitoring data processing method and system for old people based on an intelligent Internet of things. Background The utility model provides a old person's physical training health monitoring data processing based on wisdom thing networking, refers to under the thing networking environment, relies on multiple class perception equipment such as wearable sensor, intelligent bracelet, gesture recognition equipment, environmental monitoring node, carries out continuous collection, stable transmission and the overall process of fusion processing to old person's multidimensional health data in physical training in-process. The acquired data comprise heart rate, blood pressure, body temperature, step frequency, movement posture, environment temperature and humidity and the like, and are converged to the cloud platform through a wireless communication network. On the cloud side, by combining a big data processing technology, cleaning, time alignment, feature extraction and association analysis are performed on the original data, and a monitoring curve and a behavior portrait which reflect the movement state and the health change trend of an individual are constructed. On the basis, historical health data and group statistical characteristics are further fused, and sign abnormality, fatigue accumulation and potential risk in the movement process are continuously evaluated and early-warned, so that a closed-loop management flow integrating acquisition, analysis, judgment and intervention is formed, and safer and more scientific physical exercise support is provided for the old. The prior art has the following defects: In the prior art, in the physical exercise health monitoring data processing of the elderly, skin blood flow monitoring based on optical sensing, blood flow change is generally judged by detecting the reflection intensity of incident light on the skin surface. However, under the environments of abrupt illumination, specular reflection, strong light interference and the like, the optical sensor is easily affected by external light, and random high-stroboscopic signals are generated. Such signals are not truly physiological changes, but are easily mistaken for a sharp rise in blood flow, thereby causing distortion in blood flow trend determination. If the optical false peaks are not distinguished and removed in time, an incorrect cardiovascular risk prompt or physical overload alarm is caused, so that the old people exercise process is abnormally interrupted, and the accuracy and the continuity of the health monitoring data processing are affected. The above information disclosed in the background section is only for enhancement of understanding of the background of the disclosure and therefore it may include information that does not form the prior art that is already known to a person of ordinary skill in the art. Disclosure of Invention The invention aims to provide a physical exercise health monitoring data processing method and system for old people based on an intelligent Internet of things, so as to solve the problems in the background technology. In order to achieve the purpose, the invention provides the technical scheme that the method for processing the physical exercise health monitoring data of the old based on the intelligent Internet of things comprises the following steps: Collecting light intensity data, surface reflectivity data and acceleration data in a health monitoring data processing link, and performing time alignment processing on the collected light intensity data, surface reflectivity data and acceleration data to generate exercise scene time sequence characteristics comprising light intensity change characteristics, reflectivity change characteristics and acceleration change characteristics; Extracting an illumination intensity change section based on the time sequence features of the exercise scene, and distinguishing the motion factors and the illumination factors in the illumination intensity change section by combining the gesture parameters and the step frequency parameters to obtain an interference false peak feature set containing illumination interference features and motion interference features; Setting an inverse sampling window according to the interference false peak feature set, performing an inverse sampling operation on the optical monitoring signal in the inverse sampling window to inhibit co-frequency flicker interference, and generating a traceable position mark point for time positioning; performing time resampling and synchronous alignment on heart rate curve data and step frequency curve data based on the position mark points, generating a short-time shading control instruction in the time resamplin