CN116649940B - Remote monitoring system and method for wearable equipment - Google Patents
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Abstract
The invention relates to the technical field of remote monitoring of wearable equipment, in particular to a remote monitoring system and a remote monitoring method for wearable equipment. On the one hand, through monitoring the heart rate of the target old people corresponding to the current monitoring period, and intercepting walking data corresponding to the abnormal heart rate period, and analyzing in a targeted manner, the error early warning caused by abnormal heart rate of the target old people in the walking process is avoided, and the accuracy and scientificity of remote monitoring of the wearable equipment are further improved. On the other hand, the blood pressure and the environmental data of the target old people corresponding to the current monitoring period are monitored, and therefore the blood pressure state of the target old people corresponding to the current monitoring period is analyzed, the influence of the motion state and the environmental data on the blood pressure of the target old people is comprehensively considered, the pertinence of the blood pressure health analysis of the target old people is realized, and the analysis result is more scientific, representative and comprehensive.
Description
Technical Field
The invention relates to the technical field of remote monitoring of wearable equipment, in particular to a remote monitoring system and a remote monitoring method for wearable equipment.
Background
With the continuous increase of the aging degree and the gradual increase of the number of the solitary old persons, the problem of the solitary old persons' risk of the senior citizens in society is increasingly outstanding. The problem that communication between a part of solitary old people and children is difficult is solved by the advent of the wearable device, meanwhile, people can better perceive external and own information, and information can be processed more efficiently under the assistance of a computer, a network and even other people, so that the significance of remote monitoring of the wearable device is highlighted.
The current wearable equipment generally monitors and analyzes the body data of the human body or monitors and analyzes the movement state of the human body, and performs corresponding display instead of comprehensively analyzing the body data and the movement data, so that the health state of the human body is analyzed correspondingly, the method is unfavorable for analyzing the body state of the old, the reliability and the effectiveness of the health analysis result of the old in the living process cannot be ensured, and meanwhile, the error processing caused by the heart rate and the blood pressure change of the old in the movement process cannot be avoided.
Disclosure of Invention
The invention aims to provide a remote monitoring system and a remote monitoring method for wearable equipment.
The aim of the invention can be achieved by the following technical scheme:
the equipment wearing state monitoring module is used for monitoring and analyzing the wearing state of the wearable equipment corresponding to the target old people, if the wearing state of the wearable equipment corresponding to the target old people is the unworn state, the remote early warning terminal is started, otherwise, the using state monitoring and analyzing module is executed;
the use state monitoring and analyzing module is used for monitoring and analyzing the use state of the target old people corresponding to the current monitoring period, if the use state of the target old people corresponding to the current monitoring period is a sleep state, the intelligent camera is started to monitor the sleep state of the target old people corresponding to the current monitoring period and send the sleep state to the remote display terminal, otherwise, the body data monitoring module is started;
the body data monitoring module is used for monitoring the body health data of the target old people corresponding to the current monitoring period in real time and sending the body health data to the remote display terminal;
the activity data monitoring module is used for monitoring the activity data of the target old people corresponding to the current monitoring period in real time and sending the activity data to the remote display terminal;
the body state analysis module is used for analyzing the body state of the target old people corresponding to the current monitoring period in real time, wherein the body state analysis module comprises a heart rate analysis unit and a blood pressure analysis unit;
the health state analysis module is used for analyzing the health state of the target old people corresponding to the current monitoring period, if the health state of the target old people corresponding to the current monitoring period is abnormal, the abnormal parameter analysis module is executed, otherwise, the health state of the target old people corresponding to the current monitoring period is sent to the remote display terminal;
the abnormal parameter analysis module is used for analyzing the abnormal type and the abnormal grade of the target old corresponding to the current monitoring period and sending the abnormal type and the abnormal grade to the remote early warning terminal;
the remote early warning terminal is used for sending the remote early warning information to the appointed equipment of the corresponding guardian of the target old people and carrying out corresponding early warning operation at the same time;
and the remote display terminal is used for sending the remote display information to the appointed equipment of the corresponding guardian of the target old man and simultaneously carrying out corresponding display.
Preferably, the wearing state of the wearable device corresponding to the target old people is monitored and analyzed, and the specific monitoring and analyzing process is as follows:
detecting the limb distance between the wearable device and the target old people through a human body infrared sensor in the wearable device to obtain the limb distance of the wearable device corresponding to the target old people, and marking the limb distance as a marking distance;
comparing the marking distance of the wearable device corresponding to the target old person with the set reference marking distance, if the marking distance of the wearable device corresponding to the target old person is greater than or equal to the set reference marking distance, judging that the wearing state of the wearable device corresponding to the target old person is an unworn state, otherwise, judging that the wearing state of the wearable device corresponding to the target old person is a worn state.
Preferably, the usage state of the target old people corresponding to the current monitoring period is monitored and analyzed, and the specific monitoring and analyzing process is as follows:
monitoring the action amplitude of each detection point in the current monitoring period corresponding to the target old people through the action sensor to obtain action amplitude detection values of each detection point in the current monitoring period corresponding to the target old people, and constructing an action amplitude waveform diagram corresponding to the current monitoring period corresponding to the target old people;
and matching the action amplitude waveform diagram of the target old people corresponding to the current monitoring period with each action amplitude waveform diagram corresponding to each set use state to obtain the use state of the target old people corresponding to the current monitoring period.
Preferably, the heart rate analysis unit is used for analyzing the heart rate health assessment index of the target old people corresponding to the current monitoring period, and the specific analysis process is as follows:
extracting heart rate of the target old people corresponding to each monitoring time point in the current monitoring period from the body health data of the target old people corresponding to the current monitoring period, and constructing a heart rate change curve graph of the target old people corresponding to the current monitoring period;
extracting each heart rate abnormal period from a heart rate variation graph of the target old people corresponding to the current monitoring period, acquiring the time length of each heart rate abnormal period in the target old people corresponding to the current monitoring period, acquiring the previous period of each heart rate abnormal period, enabling the time length of the previous period to be equal to the time length of the corresponding heart rate abnormal period, further forming an integral period by each heart rate abnormal period and the corresponding previous period, and marking the integral period as a heart rate marking period, thereby obtaining each heart rate marking period of the target old people corresponding to the current monitoring period;
extracting the step number of each heart rate marking period in the current monitoring period corresponding to the target old people from the activity data of the current monitoring period corresponding to the target old peopleDistance to walk->Foot speed->I is denoted as the number of each heart rate marking period,；
obtaining the heart rate influence value of the target old corresponding to each heart rate abnormality period by calculating the walking state influence index of the target old corresponding to each heart rate abnormality period and matching the walking state influence index with the heart rate influence value corresponding to each set walking state influence index；
From the target oldExtracting heart rate of the target old people corresponding to each time point in each heart rate marking period from a heart rate change curve graph of the person corresponding to the current monitoring period, and recording the heart rate asF is expressed as the number of each time point, f=1, 2, …, g;
according to the formulaCalculating heart rate health evaluation index of the target old corresponding to the current monitoring period>，/>Denoted as the set reference heart rate, e is denoted as a natural constant.
Preferably, the blood pressure analysis unit is configured to analyze a blood pressure health evaluation index of a target old person corresponding to a current monitoring period, and the specific analysis mode is as follows:
extracting blood pressure of the target old people corresponding to each monitoring time point in the current monitoring period from the body health data of the target old people corresponding to the current monitoring period, and constructing a blood pressure change curve graph of the target old people corresponding to the current monitoring period;
extracting each abnormal blood pressure period from a blood pressure change graph of the target old people corresponding to the current monitoring period, acquiring the time length of each abnormal blood pressure period in the target old people corresponding to the current monitoring period, acquiring the previous time length of each abnormal blood pressure period, enabling the time length of the previous time length to be equal to the time length of the corresponding abnormal blood pressure period, further forming an integral time length by each abnormal blood pressure period and the corresponding previous time length, and marking the integral time length as a blood pressure marking time length, thereby obtaining each marked blood pressure period of the target old people corresponding to the current monitoring period;
extracting the step number of each blood pressure marking period in the current monitoring period corresponding to the target old people from the activity data of the current monitoring period corresponding to the target old peopleDistance to walk->Foot speed->J is denoted as the number of each blood pressure mark period,；
calculating to obtain blood pressure health evaluation index of the target old corresponding to the current monitoring period, and recording as。
Preferably, the health state of the target old people corresponding to the current monitoring period is analyzed, and the specific analysis process is as follows:
comparing the heart rate health evaluation index of the target old people corresponding to the current monitoring period with a set heart rate health evaluation index threshold, if the heart rate health evaluation index of the target old people corresponding to the current monitoring period is smaller than the heart rate health evaluation index threshold, judging that the heart rate health state of the target old people corresponding to the current monitoring period is abnormal, otherwise, judging that the heart rate health state of the target old people corresponding to the current monitoring period is normal;
comparing the blood pressure health evaluation index of the target old person corresponding to the current monitoring period with a set blood pressure health evaluation index threshold, if the blood pressure health evaluation index of the target old person corresponding to the current monitoring period is smaller than the blood pressure health evaluation index threshold, judging that the blood pressure health state of the target old person corresponding to the current monitoring period is abnormal, otherwise, judging that the blood pressure health state of the target old person corresponding to the current monitoring period is normal.
Preferably, the abnormality type and abnormality grade of the target old people corresponding to the current monitoring period are analyzed, and the specific analysis mode is as follows:
if the heart rate health state of the target old people corresponding to the current monitoring period is an abnormal state or the blood pressure health state is an abnormal state, judging that the abnormal type of the target old people corresponding to the current monitoring period is heart rate or blood pressure, and if the heart rate health state of the target old people corresponding to the current monitoring period is an abnormal state and the blood pressure health state is an abnormal state, judging that the abnormal type of the target old people corresponding to the current monitoring period is heart rate and blood pressure;
if the abnormal type of the target old people corresponding to the current monitoring period is heart rate or blood pressure, the corresponding health state evaluation index is differed from the corresponding health state evaluation index threshold value to obtain health state evaluation index differences corresponding to the heart rate or the blood pressure, and the health state evaluation index differences corresponding to the set abnormal grades are matched with the health state evaluation index differences corresponding to the different abnormal grades to obtain the abnormal grade of the abnormal type of the target old people corresponding to the current monitoring period.
A second aspect of the present invention provides a remote monitoring method for a wearable device, comprising:
monitoring and analyzing the wearing state of the wearable equipment corresponding to the target old people, if the wearing state of the wearable equipment corresponding to the target old people is the unworn state, sending remote early warning information to the appointed equipment of the guardian corresponding to the target old people, and simultaneously carrying out corresponding early warning operation, otherwise, carrying out real-time monitoring on the body health data and the activity data of the target old people corresponding to the current monitoring period;
analyzing heart rate health evaluation indexes and blood pressure health evaluation indexes of the target old people corresponding to the current monitoring period, wherein the specific analysis process comprises the following steps:
extracting heart rate of the target old people corresponding to each monitoring time point in the current monitoring period from the body health data of the target old people corresponding to the current monitoring period, and constructing a heart rate change curve graph of the target old people corresponding to the current monitoring period;
extracting each heart rate abnormal period from a heart rate variation graph of the target old people corresponding to the current monitoring period, acquiring the time length of each heart rate abnormal period in the target old people corresponding to the current monitoring period, acquiring the previous period of each heart rate abnormal period, enabling the time length of the previous period to be equal to the time length of the corresponding heart rate abnormal period, further forming an integral period by each heart rate abnormal period and the corresponding previous period, and marking the integral period as a heart rate marking period, thereby obtaining each heart rate marking period of the target old people corresponding to the current monitoring period;
extracting the step number of each heart rate marking period in the current monitoring period corresponding to the target old people from the activity data of the current monitoring period corresponding to the target old peopleDistance to walk->Foot speed->I is denoted as the number of each heart rate marking period,；
obtaining the heart rate influence value of the target old corresponding to each heart rate abnormality period by calculating the walking state influence index of the target old corresponding to each heart rate abnormality period and matching the walking state influence index with the heart rate influence value corresponding to each set walking state influence index；
Extracting heart rate of the target old people corresponding to each time point in each heart rate marking period from the heart rate change curve graph of the target old people corresponding to the current monitoring period, and recording asF is expressed as the number of each time point, f=1, 2, …, g;
according to the formulaCalculating heart rate health evaluation index of the target old corresponding to the current monitoring period>，/>Denoted as the set reference heart rate, e is denoted as a natural constant.
Extracting blood pressure of the target old people corresponding to each monitoring time point in the current monitoring period from the body health data of the target old people corresponding to the current monitoring period, and constructing a blood pressure change curve graph of the target old people corresponding to the current monitoring period;
extracting each abnormal blood pressure period from a blood pressure change graph of the target old people corresponding to the current monitoring period, acquiring the time length of each abnormal blood pressure period in the target old people corresponding to the current monitoring period, acquiring the previous time length of each abnormal blood pressure period, enabling the time length of the previous time length to be equal to the time length of the corresponding abnormal blood pressure period, further forming an integral time length by each abnormal blood pressure period and the corresponding previous time length, and marking the integral time length as a blood pressure marking time length, thereby obtaining each marked blood pressure period of the target old people corresponding to the current monitoring period;
extracting the step number of each blood pressure marking period in the current monitoring period corresponding to the target old people from the activity data of the current monitoring period corresponding to the target old peopleDistance to walk->Foot speed->J is denoted as the number of each blood pressure mark period,；
calculating to obtain blood pressure health evaluation index of the target old corresponding to the current monitoring period, and recording as。
Comparing the heart rate health evaluation index of the target old people corresponding to the current monitoring period with a set heart rate health evaluation index threshold, and if the heart rate health evaluation index of the target old people corresponding to the current monitoring period is smaller than the heart rate health evaluation index threshold, judging that the heart rate health state of the target old people corresponding to the current monitoring period is abnormal;
comparing the blood pressure health evaluation index of the target old people corresponding to the current monitoring period with a set blood pressure health evaluation index threshold, and judging that the blood pressure health state of the target old people corresponding to the current monitoring period is abnormal if the blood pressure health evaluation index of the target old people corresponding to the current monitoring period is smaller than the blood pressure health evaluation index threshold;
and analyzing the abnormal grade of the abnormal type in the current monitoring period corresponding to the target old people based on the heart rate health state and the blood pressure health state of the target old people corresponding to the current monitoring period.
The invention has the beneficial effects that:
according to the wearable device for the old people, the wearing state of the wearable device corresponding to the target old people is monitored and analyzed, and corresponding operation is started through the wearing state, so that the standardization of wearing the wearable device by the old people is greatly improved, and reliable guarantee is provided for the health state monitoring and analysis of the target old people.
According to the invention, the current use state of the target old people is judged by analyzing the current use state of the target old people, so that corresponding operation is performed, the error easily generated in the monitoring process is greatly avoided, and a powerful data support is provided for the follow-up health state monitoring analysis result.
According to the invention, the heart rate of the target old people corresponding to the current monitoring period is monitored, meanwhile, the walking data corresponding to the abnormal heart rate period is intercepted, and the specific analysis is carried out, so that the false early warning caused by abnormal heart rate of the target old people in the walking process is avoided, and the accuracy and the scientificity of remote monitoring of the wearable equipment are further improved.
According to the invention, the blood pressure and the environmental data of the target old people corresponding to the current monitoring period are monitored, and the blood pressure state of the target old people corresponding to the current monitoring period is analyzed, so that the analysis result is more targeted, the influence of the motion state and the environmental data on the blood pressure of the target old people is comprehensively considered, the pertinence of the blood pressure health analysis of the target old people is realized, and the analysis result is more scientific, representative and comprehensive.
According to the invention, through analyzing the corresponding health state of the target old and carrying out corresponding display processing and early warning processing, the response efficiency of the dangerous state of the target old is greatly improved, the solitary safety of the solitary old is greatly improved, and the solitary risk of the solitary old is reduced to a certain extent.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a system block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a first aspect of the present invention provides a remote monitoring system for a wearable device, comprising: the system comprises a device wearing state monitoring module, a using state monitoring and analyzing module, a body data monitoring module, an activity data monitoring module, a body state analyzing module, a health state analyzing module, an abnormal parameter analyzing module, a remote early warning terminal, a remote display terminal and a database.
The equipment wearing state monitoring module is respectively connected with the using state monitoring analysis module and the remote early warning terminal, the using state monitoring analysis module is respectively connected with the body data monitoring module, the activity data monitoring module and the database, the body data monitoring module and the activity data monitoring module are both connected with the remote display terminal, the body state analysis module is respectively connected with the body data monitoring module and the activity data monitoring module, the body state analysis module is connected with the health state analysis module, the health state analysis module is respectively connected with the abnormal parameter analysis module and the remote display terminal, and the abnormal parameter analysis module is connected with the remote early warning terminal.
Sensors in wearable devices include, but are not limited to: human body infrared sensor, action sensor, noise sensor.
The equipment wearing state monitoring module is used for monitoring and analyzing the wearing state of the wearable equipment corresponding to the target old people, and the specific monitoring and analyzing process is as follows:
detect the limb distance of wearable equipment and target old man through human infrared sensor in the wearable equipment, wherein, the limb distance of wearable equipment and target old man specifically is: the distance between the wearable equipment and each part of the limb corresponding to the target old man is acquired through the human body infrared sensor, and the shortest distance is screened out from the distance to be used as the limb distance. And obtaining the limb distance of the wearable equipment corresponding to the target old people, and marking the limb distance as a marking distance.
Comparing the marking distance of the wearable device corresponding to the target old person with the set reference marking distance, if the marking distance of the wearable device corresponding to the target old person is greater than or equal to the set reference marking distance, judging that the wearing state of the wearable device corresponding to the target old person is an unworn state, otherwise, judging that the wearing state of the wearable device corresponding to the target old person is a worn state.
And if the wearing state of the wearable equipment corresponding to the target old man is the unworn state, starting the remote early warning terminal, otherwise, executing the using state monitoring and analyzing module if the wearing state of the wearable equipment corresponding to the target old man is the worn state.
The wearable device and the method have the advantages that the wearing state of the wearable device corresponding to the target old people is monitored and analyzed, and corresponding operation is started through the wearing state, so that the standardization of wearing the wearable device by the old people is greatly improved, and the reliable guarantee is provided for the health state monitoring and analysis of the target old people.
The use state monitoring and analyzing module is used for monitoring and analyzing the use state of the target old people corresponding to the current monitoring period, wherein the use state comprises a walking state and a sleep state, and the specific monitoring and analyzing process is as follows:
the motion sensor is used for monitoring the motion amplitude of each detection point in the current monitoring period corresponding to the target old people, so as to obtain the motion amplitude detection value of each detection point in the current monitoring period corresponding to the target old people, and a motion amplitude waveform diagram corresponding to the current monitoring period corresponding to the target old people is constructed.
And performing coincidence comparison between the action amplitude waveform diagram of the target old person corresponding to the current monitoring period and the action amplitude waveform diagram of each set use state corresponding to each monitoring period to obtain the coincidence length of each action amplitude waveform diagram of each use state of the target old person corresponding to each monitoring period, and performing average value calculation on the coincidence length to obtain the action amplitude waveform coincidence length of each use state of the target old person corresponding to each monitoring period.
Comparing the overlapping length of the action amplitude waveforms corresponding to the use states in each monitoring period of the target old people, wherein the specific analysis steps are as follows:
(1) If the overlapping length of the action amplitude waveforms of the walking state of the target old people corresponding to the current monitoring period is larger than that of the action amplitude waveforms of the sleeping state of the target old people corresponding to the current monitoring period, judging that the using state of the target old people corresponding to the current monitoring period is the walking state.
(2) If the overlapping length of the action amplitude waveforms of the walking state in the current monitoring period corresponding to the target old people is smaller than the overlapping length of the action amplitude waveforms of the sleeping state in the current monitoring period corresponding to the target old people, judging that the using state of the current monitoring period corresponding to the target old people is the sleeping state.
(3) If the overlapping length of the action amplitude waveform of the walking state in the current monitoring period corresponding to the target old man is equal to the overlapping length of the action amplitude waveform of the sleeping state in the current monitoring period corresponding to the target old man, extracting the actual time period corresponding to the current monitoring period, extracting each actual time period corresponding to the sleeping state in the history period corresponding to the target old man from the database, simultaneously matching the actual time period corresponding to the current monitoring period with each actual time period corresponding to the sleeping state in the history period corresponding to the target old man, judging that the use state of the target old man corresponding to the current monitoring period is the sleeping state if the matching is successful, otherwise, judging that the use state of the target old man corresponding to the current monitoring period is the walking state.
(4) Thereby obtaining the use state of the target old people corresponding to the current monitoring period.
If the use state of the target old person corresponding to the current monitoring period is a sleep state, the intelligent camera is started to monitor the sleep posture of the target old person corresponding to the current monitoring period and send the sleep posture to the remote display terminal, otherwise, the body data monitoring module is started.
It should be noted that, the present invention analyzes the current usage state corresponding to the target old people to determine the current usage state corresponding to the target old people, so as to perform corresponding operation, greatly avoid error easily generated in the monitoring process, and provide powerful data support for the subsequent health status monitoring analysis result.
The body data monitoring module is used for monitoring the body health data of the target old people corresponding to the current monitoring period in real time, wherein the body health data are specifically heart rate, blood pressure and blood sugar. A detection method for measuring heart rate, blood pressure and blood sugar has been disclosed in patent CN111643064a , a health data statistics monitoring device. And transmitted to the remote display terminal.
The activity data monitoring module is used for monitoring the activity data of the target old people corresponding to the current monitoring period in real time, wherein the activity data comprises walking data and environment data, and the walking data specifically comprises the step number, the walking distance and the walking speed of the target old people corresponding to the current monitoring period. The environmental data is specifically atmospheric temperature, ambient noise. It should be noted that ambient noise is monitored by a noise sensor. And transmitted to the remote display terminal.
The body state analysis module is used for analyzing the body state of the target old people corresponding to the current monitoring period in real time, wherein the body state analysis module comprises a heart rate analysis unit and a blood pressure analysis unit.
The heart rate analysis unit is used for analyzing heart rate health assessment indexes of the target old people corresponding to the current monitoring period, and the specific analysis process is as follows:
and extracting the heart rate of the target old people corresponding to each monitoring time point in the current monitoring period from the body health data of the target old people corresponding to the current monitoring period, and constructing a heart rate change curve graph of the target old people corresponding to the current monitoring period.
And matching the heart rate of each monitoring time point in the heart rate variation graph corresponding to the current monitoring period of the target old people with a set abnormal heart rate threshold value, if the heart rate of a certain monitoring time point in the heart rate variation graph corresponding to the current monitoring period of the target old people is successfully matched with the abnormal heart rate threshold value, marking the monitoring time point as an abnormal heart rate time point, and integrating adjacent abnormal heart rate time points based on the heart rate variation graph corresponding to the current monitoring period of the target old people to form heart rate abnormal periods, so that each heart rate abnormal period corresponding to the current monitoring period of the target old people is obtained. The method comprises the steps of obtaining the time length of each heart rate abnormal time period in the current monitoring time period corresponding to the target old people, obtaining the previous time period of each heart rate abnormal time period, enabling the time length of the previous time period to be equal to the time length of the corresponding heart rate abnormal time period, further forming the whole time period by each heart rate abnormal time period and the corresponding previous time period, and recording the whole time period as a heart rate marking time period, so that each heart rate marking time period of the target old people corresponding to the current monitoring time period is obtained.
Extracting the step number of each heart rate marking period in the current monitoring period corresponding to the target old people from the activity data of the current monitoring period corresponding to the target old peopleDistance to walk->Foot speed->I is denoted as the number of each heart rate marking period,。
calculating to obtain the heart rate of the target old peopleThe walking state influence indexes of the abnormal time periods are matched with the heart rate influence values corresponding to the set walking state influence indexes, and the heart rate influence values corresponding to the heart rate abnormal time periods of the target old people are obtained。
According to the formulaCalculating walking state influence index ++for target old people corresponding to each heart rate abnormality period>，/>Respectively expressed as a set reference step number, a reference walking distance, a reference walking speed, +.>The number of steps, the walking distance, and the walking speed are set as the corresponding influence factors, respectively.
Extracting heart rate of the target old people corresponding to each time point in each heart rate marking period from the heart rate change curve graph of the target old people corresponding to the current monitoring period, and recording asF is the number of each time point, f=1, 2, …, g.
According to the formulaCalculating heart rate health evaluation index of the target old corresponding to the current monitoring period>，/>Denoted as the set reference heart rate, e is denoted as a natural constant.
It is to be noted that, the heart rate of the target old people corresponding to the current monitoring period is monitored, meanwhile, the walking data corresponding to the abnormal heart rate period are intercepted, and the specific analysis is carried out, so that the error early warning caused by abnormal heart rate of the target old people in the walking process is avoided, and the accuracy and the scientificity of remote monitoring of the wearable equipment are further improved.
The blood pressure analysis unit is used for analyzing the blood pressure health evaluation index of the target old people corresponding to the current monitoring period, and the specific analysis mode is as follows:
extracting blood pressure of the target old people corresponding to each monitoring time point in the current monitoring period from the body health data of the target old people corresponding to the current monitoring period, and constructing a blood pressure change curve graph of the target old people corresponding to the current monitoring period.
Matching the blood pressure of each monitoring time point in the blood pressure change curve graph of the target old people corresponding to the current monitoring period with a set abnormal blood pressure threshold value, if the blood pressure of a certain monitoring time point in the target old people corresponding to the current monitoring period is successfully matched with the abnormal blood pressure threshold value, marking the monitoring time point as an abnormal blood pressure time point, and simultaneously integrating adjacent abnormal blood pressure time points based on the blood pressure change curve graph of the target old people corresponding to the current monitoring period to form blood pressure abnormal periods, so that each blood pressure abnormal period of the target old people corresponding to the current monitoring period is obtained. The method comprises the steps of obtaining the time length of each abnormal blood pressure time period in the current monitoring time period corresponding to the target old people, obtaining the previous time period of each abnormal blood pressure time period, enabling the time length of the previous time period to be equal to the time length of the corresponding abnormal blood pressure time period, further forming the whole time period by each abnormal blood pressure time period and the corresponding previous time period, and recording the whole time period as a blood pressure marking time period, so that each marked blood pressure time period of the target old people corresponding to the current monitoring time period is obtained.
Extracting the step number of each blood pressure marking period in the current monitoring period corresponding to the target old people from the activity data of the current monitoring period corresponding to the target old peopleDistance to walk->Foot speed->J is denoted as the number of each blood pressure mark period,。
extracting the atmospheric temperature and the environmental noise of each marking time point in each blood pressure marking time period in the current monitoring time period corresponding to the target old people from the activity data of the current monitoring time period corresponding to the target old people, and respectively carrying out average calculation on the atmospheric temperature and the environmental noise to obtain the average atmospheric temperature of each blood pressure marking time period in the current monitoring time period corresponding to the target old peopleMean ambient noise->。
According to the formulaCalculating the activity data influence coefficient of the target old people corresponding to each blood pressure abnormal period +.>，/>Respectively expressed as a set reference atmospheric temperature, reference ambient noise, ">Respectively expressed as a set weight factor corresponding to the atmospheric temperature and the ambient noise.
Matching the activity data influence coefficient of the target old people corresponding to each abnormal blood pressure period with the set blood pressure influence value corresponding to each activity data influence coefficient to obtain the blood pressure influence value of the target old people corresponding to each abnormal blood pressure period。
Extracting heart rate of the target old people corresponding to each time point in each blood pressure marking period from the blood pressure change curve graph of the target old people corresponding to the current monitoring period, and recording asR is denoted as the number of each time point, r=1, 2, …, p.
According to the formulaCalculating blood pressure health evaluation index of the target old corresponding to the current monitoring period>，/>Indicated as set reference blood pressure.
It is to be noted that, the blood pressure and the environmental data of the target old people corresponding to the current monitoring period are monitored, and the blood pressure state of the target old people corresponding to the current monitoring period is analyzed, so that the analysis result is more targeted, the influence of the motion state and the environmental data on the blood pressure of the target old people is comprehensively considered, the targeting of the blood pressure health analysis of the target old people is realized, and the analysis result is more scientific, representative and comprehensive.
The health state analysis module is used for analyzing the health state of the target old people corresponding to the current monitoring period, and the specific analysis process is as follows:
comparing the heart rate health evaluation index of the target old people corresponding to the current monitoring period with a set heart rate health evaluation index threshold, if the heart rate health evaluation index of the target old people corresponding to the current monitoring period is smaller than the heart rate health evaluation index threshold, judging that the heart rate health state of the target old people corresponding to the current monitoring period is abnormal, otherwise, judging that the heart rate health state of the target old people corresponding to the current monitoring period is normal.
Comparing the blood pressure health evaluation index of the target old person corresponding to the current monitoring period with a set blood pressure health evaluation index threshold, if the blood pressure health evaluation index of the target old person corresponding to the current monitoring period is smaller than the blood pressure health evaluation index threshold, judging that the blood pressure health state of the target old person corresponding to the current monitoring period is abnormal, otherwise, judging that the blood pressure health state of the target old person corresponding to the current monitoring period is normal.
If the health state of the target old people corresponding to the current monitoring period is abnormal, executing an abnormal parameter analysis module, otherwise, sending the health state of the target old people corresponding to the current monitoring period to a remote display terminal.
By analyzing the corresponding health state of the target old people and performing corresponding display processing and early warning processing, the response efficiency of the dangerous state of the target old people is improved to the greatest extent, the solitary safety of solitary old people is greatly improved, and the solitary risk of solitary old people is reduced to a certain extent.
The abnormal parameter analysis module is used for analyzing the abnormal type and the abnormal grade of the target old corresponding to the current monitoring period and sending the abnormal type and the abnormal grade to the remote early warning terminal, and the specific analysis mode is as follows:
if the heart rate health state of the target old people corresponding to the current monitoring period is an abnormal state or the blood pressure health state is an abnormal state, judging that the abnormal type of the target old people corresponding to the current monitoring period is heart rate or blood pressure, and if the heart rate health state of the target old people corresponding to the current monitoring period is an abnormal state and the blood pressure health state is an abnormal state, judging that the abnormal type of the target old people corresponding to the current monitoring period is heart rate and blood pressure.
If the abnormal type of the target old people corresponding to the current monitoring period is heart rate or blood pressure, the corresponding health state evaluation index is differed from the corresponding health state evaluation index threshold value to obtain health state evaluation index differences corresponding to the heart rate or the blood pressure, and the health state evaluation index differences corresponding to the set abnormal grades are matched with the health state evaluation index differences corresponding to the different abnormal grades to obtain the abnormal grade of the abnormal type of the target old people corresponding to the current monitoring period.
And the remote early warning terminal is used for sending the remote early warning information to the appointed equipment of the corresponding guardian of the target old people and carrying out corresponding early warning operation.
And the remote display terminal is used for sending the remote display information to the appointed equipment of the corresponding guardian of the target old man and simultaneously carrying out corresponding display.
The database is used for storing each actual time period corresponding to the sleep state in the history period corresponding to the target old people, wherein the actual time period comprises but is not limited to: beijing time period, london time period.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (1)
1. A remote monitoring system for a wearable device, comprising:
the equipment wearing state monitoring module is used for monitoring and analyzing the wearing state of the wearable equipment corresponding to the target old people: detecting the limb distance between the wearable device and the target old people through a human body infrared sensor in the wearable device to obtain the limb distance of the wearable device corresponding to the target old people, and marking the limb distance as a marking distance;
comparing the marking distance of the wearable device corresponding to the target old man with the set reference marking distance, if the marking distance of the wearable device corresponding to the target old man is larger than or equal to the set reference marking distance, judging that the wearing state of the wearable device corresponding to the target old man is an unworn state, otherwise, judging that the wearing state of the wearable device corresponding to the target old man is a worn state; if the wearing state of the wearable equipment corresponding to the target old people is the unworn state, starting the remote early warning terminal, otherwise, executing the use state monitoring and analyzing module;
the use state monitoring and analyzing module is used for monitoring and analyzing the use state of the target old people corresponding to the current monitoring period: monitoring the action amplitude of each detection point in the current monitoring period corresponding to the target old people through the action sensor to obtain action amplitude detection values of each detection point in the current monitoring period corresponding to the target old people, and constructing an action amplitude waveform diagram corresponding to the current monitoring period corresponding to the target old people;
matching the action amplitude waveform diagram of the target old people corresponding to the current monitoring period with each action amplitude waveform diagram corresponding to each set use state to obtain the use state of the target old people corresponding to the current monitoring period;
if the use state of the target old person corresponding to the current monitoring period is a sleep state, starting the intelligent camera to monitor the sleep state of the target old person corresponding to the current monitoring period, and sending the sleep state to the remote display terminal, otherwise, starting the body data monitoring module;
the body data monitoring module is used for monitoring the body health data of the target old people corresponding to the current monitoring period in real time and sending the body health data to the remote display terminal;
the activity data monitoring module is used for monitoring the activity data of the target old people corresponding to the current monitoring period in real time and sending the activity data to the remote display terminal;
the body state analysis module is used for analyzing the body state of the target old people corresponding to the current monitoring period in real time, wherein the body state analysis module comprises a heart rate analysis unit and a blood pressure analysis unit;
the heart rate analysis unit is used for analyzing heart rate health assessment indexes of the target old people corresponding to the current monitoring period, and the specific analysis process is as follows:
extracting heart rate of the target old people corresponding to each monitoring time point in the current monitoring period from the body health data of the target old people corresponding to the current monitoring period, and constructing a heart rate change curve graph of the target old people corresponding to the current monitoring period;
extracting each heart rate abnormal period from a heart rate variation graph of the target old people corresponding to the current monitoring period, acquiring the time length of each heart rate abnormal period in the target old people corresponding to the current monitoring period, acquiring the previous period of each heart rate abnormal period, enabling the time length of the previous period to be equal to the time length of the corresponding heart rate abnormal period, further forming an integral period by each heart rate abnormal period and the corresponding previous period, and marking the integral period as a heart rate marking period, thereby obtaining each heart rate marking period of the target old people corresponding to the current monitoring period;
extracting the step number of each heart rate marking period in the current monitoring period corresponding to the target old people from the activity data of the current monitoring period corresponding to the target old peopleDistance to walk->Foot speed->I is denoted as the number of each heart rate marking period,；
obtaining the heart rate influence value of the target old corresponding to each heart rate abnormality period by calculating the walking state influence index of the target old corresponding to each heart rate abnormality period and matching the walking state influence index with the heart rate influence value corresponding to each set walking state influence index；
According to the formulaCalculating walking state influence index ++for target old people corresponding to each heart rate abnormality period>，/>Respectively expressed as a set reference step number, a reference walking distance, a reference walking speed, +.>Respectively expressed as the set step number, walking distance and corresponding influence factors of walking speed;
extracting heart rate of the target old people corresponding to each time point in each heart rate marking period from the heart rate change curve graph of the target old people corresponding to the current monitoring period, and recording asF is expressed as the number of each time point, f=1, 2, …, g;
according to the formulaCalculating heart rate health evaluation index of the target old corresponding to the current monitoring period>，/>Expressed as a set reference heart rate, e expressed as a natural constant;
the blood pressure analysis unit is used for analyzing the blood pressure health evaluation index of the target old people corresponding to the current monitoring period, and the specific analysis mode is as follows:
extracting blood pressure of the target old people corresponding to each monitoring time point in the current monitoring period from the body health data of the target old people corresponding to the current monitoring period, and constructing a blood pressure change curve graph of the target old people corresponding to the current monitoring period;
extracting each abnormal blood pressure period from a blood pressure change graph of the target old people corresponding to the current monitoring period, acquiring the time length of each abnormal blood pressure period in the target old people corresponding to the current monitoring period, acquiring the previous time length of each abnormal blood pressure period, enabling the time length of the previous time length to be equal to the time length of the corresponding abnormal blood pressure period, further forming an integral time length by each abnormal blood pressure period and the corresponding previous time length, and marking the integral time length as a blood pressure marking time length, thereby obtaining each marked blood pressure period of the target old people corresponding to the current monitoring period;
extracting the step number of each blood pressure marking period in the current monitoring period corresponding to the target old people from the activity data of the current monitoring period corresponding to the target old peopleDistance to walk->Foot speed->J is denoted as the number of each blood pressure mark period,；
extracting the atmospheric temperature and the environmental noise of each marking time point in each blood pressure marking time period in the current monitoring time period corresponding to the target old people from the activity data of the current monitoring time period corresponding to the target old people, and respectively carrying out average calculation on the atmospheric temperature and the environmental noise to obtain the average atmospheric temperature of each blood pressure marking time period in the current monitoring time period corresponding to the target old peopleMean ambient noise->；
According to the formulaCalculating the activity data influence coefficient of the target old people corresponding to each blood pressure abnormal period +.>，/>Respectively expressed as a set reference atmospheric temperature, reference ambient noise, ">Respectively representing the set atmospheric temperature and the weight factors corresponding to the environmental noise;
matching the activity data influence coefficient of the target old people corresponding to each abnormal blood pressure period with the set blood pressure influence value corresponding to each activity data influence coefficient to obtain the blood pressure influence value of the target old people corresponding to each abnormal blood pressure period；
Extracting heart rate of the target old people corresponding to each time point in each blood pressure marking period from the blood pressure change curve graph of the target old people corresponding to the current monitoring period, and recording asR is expressed as the number of each time point, r=1, 2, …, p;
calculating to obtain blood pressure health evaluation index of the target old corresponding to the current monitoring period, and recording as；
The health state analysis module is used for analyzing the health state of the target old people corresponding to the current monitoring period: comparing the heart rate health evaluation index of the target old people corresponding to the current monitoring period with a set heart rate health evaluation index threshold, if the heart rate health evaluation index of the target old people corresponding to the current monitoring period is smaller than the heart rate health evaluation index threshold, judging that the heart rate health state of the target old people corresponding to the current monitoring period is abnormal, otherwise, judging that the heart rate health state of the target old people corresponding to the current monitoring period is normal;
comparing the blood pressure health evaluation index of the target old person corresponding to the current monitoring period with a set blood pressure health evaluation index threshold, if the blood pressure health evaluation index of the target old person corresponding to the current monitoring period is smaller than the blood pressure health evaluation index threshold, judging that the blood pressure health state of the target old person corresponding to the current monitoring period is abnormal, otherwise, judging that the blood pressure health state of the target old person corresponding to the current monitoring period is normal;
if the health state of the target old people corresponding to the current monitoring period is abnormal, executing an abnormal parameter analysis module, otherwise, sending the health state of the target old people corresponding to the current monitoring period to a remote display terminal;
the abnormal parameter analysis module is used for analyzing the abnormal type and the abnormal grade of the target old people corresponding to the current monitoring period: if the heart rate health state of the target old people corresponding to the current monitoring period is an abnormal state or the blood pressure health state is an abnormal state, judging that the abnormal type of the target old people corresponding to the current monitoring period is heart rate or blood pressure, and if the heart rate health state of the target old people corresponding to the current monitoring period is an abnormal state and the blood pressure health state is an abnormal state, judging that the abnormal type of the target old people corresponding to the current monitoring period is heart rate and blood pressure;
if the abnormal type of the target old people corresponding to the current monitoring period is heart rate or blood pressure, making a difference between the corresponding health state evaluation index and the corresponding health state evaluation index threshold to obtain a health state evaluation index difference corresponding to the heart rate or the blood pressure, and matching the health state evaluation index difference with the health state evaluation index differences corresponding to the set abnormal grades to obtain the abnormal grade of the abnormal type of the target old people corresponding to the current monitoring period; and sending the result to a remote early warning terminal;
the remote early warning terminal is used for sending the remote early warning information to the appointed equipment of the corresponding guardian of the target old people and carrying out corresponding early warning operation at the same time;
and the remote display terminal is used for sending the remote display information to the appointed equipment of the corresponding guardian of the target old man and simultaneously carrying out corresponding display.
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