CN113314235B - Real-time data acquisition-based stroke early warning and active intervention system - Google Patents
Real-time data acquisition-based stroke early warning and active intervention system Download PDFInfo
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Abstract
The invention provides a stroke early warning and intervention system based on real-time data acquisition, which comprises a combined sensor, a timer, a remote centralized control platform and a micro edge data processing unit. The first combination sensor is used for detecting whether the at least one second combination sensor is paired in a preset range; when the first combination sensor detects that the first combination sensor is paired to the second combination sensor within a preset range, the first combination sensor acquires a plurality of physiological activity parameters detected by the second combination sensor and sends the physiological activity parameters to the micro edge data processing unit; the micro edge data processing unit carries out edge calculation processing on the plurality of physiological activity parameters based on a local early warning model and sends out early warning signals to the remote centralized control platform based on the result of the edge calculation processing; and the remote centralized control platform starts at least one timer based on the early warning signal. The invention can realize the real-time stroke early warning in a large-range target area.
Description
Technical Field
The invention belongs to the technical field of intelligent monitoring and rehabilitation, and particularly relates to a stroke early warning and active intervention system based on real-time data acquisition.
Background
Apoplexy, also known as stroke and cerebrovascular accident, refers to the disease caused by acute injury of cerebral vessels. According to the statistics of the national ministry of health, cerebrovascular diseases are the first cause of death of people. The sequelae of cerebral apoplexy is hemiplegia after apoplexy, and the sequelae of hemiplegia bring very heavy burden to patients and families and society.
Cerebral apoplexy is a common disease and a frequently encountered disease of the central nervous system, and the incidence rate of cerebral apoplexy of people in China generally tends to rise in the last two decades. Stroke is often accompanied by disorders in the patient's movement, speech and perception. Rehabilitation therapy for patients with stroke is the most effective method for reducing the disability rate of stroke and can reduce the influence of dyskinesia on the normal life of the patients. In order for rehabilitation therapists to effectively evaluate the state of the motor function of a patient and further reasonably make a rehabilitation treatment plan of the patient in clinic, the patient needs to be systematically monitored for rehabilitation treatment.
With the improvement of medical level, the death rate of cerebral apoplexy is obviously reduced, but the disability rate is still high, most patients have serious sequelae after basic rehabilitation, and hemiplegia is one of the most common manifestations, and the living level and quality of people are seriously affected. For hemiplegia caused by cerebral apoplexy, the later the rehabilitation intervention time is, the less hope is for the recovery of the function of the affected limb of the patient, so that the family members and the society of the patient need to spend great cost on treating and nursing the patient, and great economic and mental pressure is brought to the family members and the society. Therefore, the important issues of people focusing on the present are to seek a quick and effective rehabilitation method to prevent the recurrence of stroke, improve various functions and prognosis of patients and improve the quality of life of patients.
As more and more rehabilitation patients are treated and various hospitals establish rehabilitation treatment departments in succession, the consistency and traceability of the rehabilitation process are difficult to achieve due to the fact that a large number of patients are treated by multiple times of rehabilitation treatment processes in different places by means of the traditional character recording mode; in addition, the self-exercise process of the rehabilitation patient cannot be monitored in the whole process, and early warning and intervention cannot be timely performed when an accident occurs or is possible to occur, so that the problem to be solved urgently in the stroke rehabilitation process is solved.
The Chinese patent application with the application number of CN202011207792.1 provides a stroke recurrence monitoring model, which comprises a stroke data acquisition module, a stroke data processing module and a data processing module, wherein the stroke data acquisition module is used for multi-dimensionally acquiring disease data information and life data information of a plurality of patients who suffer from stroke once, and transmitting the acquired disease data information and life data case data to the stroke data processing module. A large amount of case data are collected by utilizing each hospital stroke patient database storage server and patient daily vital sign monitoring terminal equipment, and the case data are quantized into a sample data set to be used for establishing a patient recurrence monitoring model, the recurrence monitoring model can predict the probability of patient recurrence stroke according to disease data information and life data information of a patient, and the model is established for carrying out big data analysis on real patient case data, so that the prediction result is real and reliable, and the randomness of manual prediction of a doctor is avoided.
Chinese patent application with application number CN202010805622.7 proposes an early warning system for stroke patients, comprising: the early warning center server generates a patient information uploading command, wherein the patient information uploading command comprises a destination terminal identity identifier; means for transmitting, by the early warning center server, the generated patient information upload command to a base station, wherein the base station is in communication with the first relay node; means for, if it is determined that the remaining storage space of the buffer for the second relay node is still less than the buffer remaining space lower limit, not sending any message regarding congestion to the base station by the first relay node; means for sending, by the first relay node, a first transport channel congestion relief report to the base station if it is determined that the remaining storage space of the buffer for the second relay node is greater than the buffer remaining space lower limit, wherein the first transport channel congestion relief report includes an identity identifier of the first mobile terminal, a congestion relief indication, and the remaining storage space of the buffer for the second relay node.
However, the above prior art does not detect or warn the real-time rehabilitation activities of the stroke patient, and cannot warn and intervene in time when an accident occurs or may occur.
Disclosure of Invention
In order to solve the technical problem, the invention provides a stroke early warning system based on real-time data acquisition, which comprises a combined sensor, a timer, a remote centralized control platform and a micro edge data processing unit. The first combination sensor is used for detecting whether the at least one second combination sensor is paired in a preset range; when the first combination sensor detects that the first combination sensor is paired to the second combination sensor within a preset range, the first combination sensor acquires a plurality of physiological activity parameters detected by the second combination sensor and sends the physiological activity parameters to the micro edge data processing unit; the micro edge data processing unit carries out edge calculation processing on the plurality of physiological activity parameters based on a local early warning model and sends out early warning signals to the remote centralized control platform based on the result of the edge calculation processing; and the remote centralized control platform starts at least one timer based on the early warning signal.
Specifically, in a first aspect of the present invention, a stroke warning system based on real-time data acquisition is provided, which includes M combined sensors, N timers, and K micro edge data processing units.
As a first advantage of the present invention, the early warning system further comprises a remote centralized control platform; the remote centralized control platform is in wireless communication with the K miniature edge data processing units and the N timers, and controls the on-off states of the N timers;
as a further advantage of the present invention, a first number of the M combined sensors are distributed over a plurality of different sub-areas of the target area; a second number of second combination sensors of the M combination sensors are configured for a plurality of stroke patients;
the first combination sensor is used for detecting whether at least one second combination sensor is paired in a preset range; the second combined sensor is used for detecting a plurality of physiological activity parameters of the stroke patient;
the K micro edge data processing units are distributed at a plurality of different positions of the target area;
when the first combination sensor detects that the first combination sensor is paired to the second combination sensor within a preset range, the first combination sensor acquires the plurality of physiological activity parameters detected by the second combination sensor and sends the plurality of physiological activity parameters to the micro edge data processing unit;
as a third advantage of the present invention, the micro edge data processing unit performs edge calculation processing on the plurality of physiological activity parameters based on a local early warning model, and sends an early warning signal to the remote centralized control platform based on a result of the edge calculation processing; and the remote centralized control platform starts at least one timer based on the early warning signal.
In the above technical solution of the present invention, the micro edge data processing unit is an edge calculating unit, and can perform local edge calculation. Each of the micro edge data processing units includes a broadcasting component; the micro edge data processing unit broadcasts the result of the edge calculation processing to other micro edge data processing units through the broadcasting component.
In the above technical solution, the remote centralized control platform starts at least one of the timers based on the early warning signal, and specifically includes:
acquiring the unique identification number of the second combined sensor for acquiring a plurality of physiological activity parameters for generating the early warning signal;
based on the unique identification number, the remote centralized control platform searches a preset patient database to obtain corresponding nursing staff information;
and starting a corresponding timer based on the nursing staff information, and sending the state information of the timer to the nursing staff.
In the above technical solution, preferably, M, N, K is a positive integer, and M > N, M > K.
As a corresponding matching solution, in a second aspect of the present invention, a stroke active intervention system based on real-time data acquisition is provided, where the active intervention system includes a plurality of portable devices, and the plurality of portable devices are connected to the early warning system in the first aspect, and receive a status signal of the timer in the early warning system.
And the portable device is a wearable device, the wearable device comprises a navigation sensor, and the navigation sensor is used for navigating the wearable device to a position close to the first combination sensor or the second combination sensor.
Preferably, the wearable device is a smart watch, the navigation sensor includes at least one navigation module and a navigation panel, and the navigation panel includes a direction indicator.
According to the technical scheme, the brain stroke early warning in a large-range target area can be achieved through the paired combined sensors, the edge computing terminals distributed in a plurality of target sub-areas, the timer assembly and the wearable device.
Further advantages of the invention will be apparent in the detailed description section in conjunction with the drawings attached hereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is an overall architecture diagram of a stroke warning system based on real-time data acquisition according to an embodiment of the present invention
Figure 2 is a schematic diagram of a scene layout in a practical application of the system of figure 1,
figure 3 is a schematic flow diagram of the system of figure 1 for generating an early warning signal,
figure 4 is a schematic diagram of edge calculation result comparison for the system of figure 1,
fig. 5 is a schematic diagram of a stroke active intervention system implemented based on the system of fig. 1.
Detailed Description
The invention is further described with reference to the following drawings and detailed description.
Referring to fig. 1, a diagram of an overall architecture of a stroke warning system based on real-time data acquisition according to an embodiment of the present invention is shown.
As a general example, the stroke warning system based on real-time data acquisition provided in this embodiment includes M combined sensors, N timers, K micro edge data processing units, and a remote centralized control platform. Wherein the combination sensors include two, a first number of first combination sensors, and a second number of second combination sensors mateable with the first number of first combination sensors.
It is to be noted that in the present embodiment, each of the second combination sensors is paired with only one first combination sensor in any case.
And, each of said first combinational sensors is absent an identification number; in the present embodiment, the absence of the identification number means that there is no distinction between the plurality of first combination sensors.
Functionally, a first number of first combined sensors are distributed over a plurality of different sub-areas of the target area; the second combined sensors in the second number are configured on a plurality of cerebral apoplexy patients, namely the patients configure the second combined sensors with one person, and each second combined sensor configures a unique identification number corresponding to the cerebral apoplexy patient.
As an example, the target area may be a hospital rehabilitation area or a large rehabilitation therapy site;
the sub-areas may be a plurality of different location areas, such as ward activity areas, outdoor activity areas, etc., located in the hospital rehabilitation area or rehabilitation therapy site. The present invention is not particularly limited in this regard.
The first combination sensor is used for detecting whether at least one second combination sensor is paired in a preset range;
the second combined sensor is used for detecting a plurality of physiological activity parameters of the stroke patient.
In this embodiment, the micro edge data processing unit is an edge computing unit or an edge computing terminal. The edge computing unit or the edge computing terminal is a unit or a terminal or an integrated chip set capable of locally performing edge computing.
The edge computing terminal can be understood as a user terminal which completes operation by utilizing edge zone resources close to a data source, and the edge computing focuses on the analysis of real-time and short-period data, so that the real-time intelligent processing and execution of local services can be better supported; because the edge calculation is closer to the user, the data is filtered and analyzed at the edge node, and therefore the efficiency is higher.
On this basis, 4 combination sensors, three timers and two edge computing terminals are shown in fig. 1.
More generally, the remote centralized control platform wirelessly communicates with the K micro edge data processing units and the N timers, and controls on/off states of the N timers; the M, N, K is a positive integer, and M > N, M > K.
On the basis of fig. 1, see fig. 2.
In fig. 2, a first number of first combination sensors of the M combination sensors are distributed over a plurality of different sub-areas of the target area;
a second number of second combination sensors of the M combination sensors are configured for a plurality of stroke patients;
the first combination sensor is used for detecting whether at least one second combination sensor is paired in a preset range;
the second combined sensor is used for detecting a plurality of physiological activity parameters of the stroke patient;
the K micro edge data processing units are distributed at a plurality of different positions of the target area;
when the first combination sensor detects that the first combination sensor is paired to the second combination sensor within a preset range, the first combination sensor acquires the plurality of physiological activity parameters detected by the second combination sensor and sends the plurality of physiological activity parameters to the micro edge data processing unit;
the micro edge data processing unit carries out edge calculation processing on the plurality of physiological activity parameters based on a local early warning model and sends out early warning signals to the remote centralized control platform based on the result of the edge calculation processing;
and the remote centralized control platform starts at least one timer based on the early warning signal.
More specifically, in the embodiment of fig. 1-2, the micro edge data processing unit includes a broadcast component;
the micro edge data processing unit broadcasts the result of the edge calculation processing of the micro edge data processing unit to other micro edge data processing units through the broadcasting component.
Specifically, referring to fig. 3, the performing, by the micro edge data processing unit, edge calculation processing on the plurality of physiological activity parameters based on the local early warning model specifically includes:
the second combined sensor detects that the plurality of physiological activity parameters of the first stroke patient are sent to the first miniature marginal data processing unit;
the first micro edge data processing unit carries out edge calculation processing on the plurality of physiological activity parameters based on the local early warning model to obtain a first edge calculation processing result;
comparing the first edge calculation processing result with a second edge calculation processing result to obtain the early warning signal;
wherein the second edge calculation processing result is an existing edge calculation processing result obtained by the first edge calculation terminal through the second edge calculation processing terminal received by the broadcasting component for the plurality of physiological activity parameters of the first stroke patient.
Fig. 4 shows that the system further comprises a comparator for comparing the edge calculation results of the first edge calculation terminal and the second edge calculation terminal.
Moreover, in fig. 4, the data trend analysis is performed by performing at least two edge calculation processes on a plurality of physiological activity parameters (such as blood pressure, heart rate, activity speed, gait, and the like) of a patient (such as a patient 123456) passing through one identification number, and the conclusion is more objective and accurate compared with the single analysis in the prior art.
Moreover, on the basis of the existing edge calculation result, the result of the next edge calculation is processed, and the result of the previous (previous) edge calculation is referred, so that the whole judgment process is closed-loop feedback of the whole process, and the integrity is further embodied.
As shown in fig. 4, the micro edge data processing unit embeds at least one data trend analysis model as the local pre-warning model.
In fig. 4, the first edge computing terminal and the second edge computing terminal are both configured with three data trend analysis models, including a data trend analysis model a, a data trend analysis model B, and a data trend analysis model C.
It should be noted that the data trend analysis model may adopt various physiological signal time trend models, early warning models, etc. known in the art, such as a self-learning model, a neural network model, etc., and the present invention is not limited in this respect.
The local early warning model used by the first edge calculation processing result obtained by the first edge calculation terminal is the same as the local early warning model used by the second edge calculation processing result obtained by the second edge calculation terminal.
In fig. 4, two edge calculation processes are performed on a plurality of physiological activity parameters (such as blood pressure, heart rate, activity speed, gait, and the like) of a patient (such as a patient 123456) with the same identification number, and a first edge calculation processing terminal and a second edge calculation processing terminal both use a data trend analysis model B.
Obviously, the same model should be used for comparability due to the overall trend analysis, but there are multiple models to choose from for different physiological parameters with different identification numbers, which is one of the improvements of the present invention.
On the basis of fig. 1-4, see fig. 5.
Fig. 5 shows a stroke active intervention system based on real-time data acquisition, wherein the active intervention system comprises a plurality of portable devices, the portable devices are connected with the early warning system in fig. 1-4 and receive the state signal of the timer in the early warning system.
The portable device is a wearable device, the wearable device comprises a navigation sensor, and the navigation sensor is used for navigating the wearable device to a position close to the first combination sensor or the second combination sensor.
Preferably, the wearable device is a smart watch, the navigation sensor includes at least one navigation module and a navigation panel, and the navigation panel includes a direction indicator.
Preferably, the navigation sensor can be paired with the first or second combination sensor, and the wearable device is navigated to a position close to the first combination sensor or the second combination sensor by magnetic induction.
In the above embodiment, the starting, by the remote centralized control platform, at least one of the timers based on the early warning signal specifically includes:
acquiring the unique identification number of the second combined sensor for acquiring a plurality of physiological activity parameters for generating the early warning signal;
based on the unique identification number, the remote centralized control platform searches a preset patient database to obtain corresponding nursing staff information;
and starting a corresponding timer based on the caregiver information, and sending the state information of the timer to the wearable equipment of the caregiver.
In particular, the status information of the timer includes a countdown indicating a time limit to which the caregiver should respond.
According to the technical scheme, the cerebral apoplexy warning system can achieve real-time cerebral apoplexy warning in a large-range target area through the paired combined sensors, the edge computing terminals distributed in a plurality of target sub-areas, the timer assembly and the wearable equipment.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A stroke early warning system based on real-time data acquisition comprises M combined sensors, N timers, K micro edge data processing units and a far-end centralized control platform,
the method is characterized in that:
said M, N, K is a positive integer, and M > N, M > K;
a first number of the M combined sensors are distributed in a plurality of different sub-areas of the target area;
a second number of second combination sensors of the M combination sensors are configured for a plurality of stroke patients;
the K micro edge data processing units are distributed at a plurality of different positions of the target area;
the second combined sensor is used for detecting a plurality of physiological activity parameters of the stroke patient;
the first combination sensor is used for detecting whether at least one second combination sensor is paired in a preset range;
each of the second combination sensors is paired with only one first combination sensor in any case;
when the first combination sensor detects that the first combination sensor is paired to the second combination sensor within a preset range, the first combination sensor acquires the plurality of physiological activity parameters detected by the second combination sensor and sends the plurality of physiological activity parameters to the micro edge data processing unit;
the system further includes a comparator and a broadcast component;
each micro edge data processing unit broadcasts the result of the edge calculation processing to other micro edge data processing units through the broadcasting component;
the second combined sensor sends the detected multiple physiological activity parameters of the first stroke patient to a first miniature marginal data processing unit;
the first micro edge data processing unit carries out edge calculation processing on the plurality of physiological activity parameters based on a local early warning model to obtain a first edge calculation processing result;
the comparator compares the first edge calculation processing result with the second edge calculation processing result to obtain an early warning signal and sends the early warning signal to the remote centralized control platform;
the second edge calculation processing result is an existing edge calculation processing result which is obtained by the first edge calculation terminal aiming at the plurality of physiological activity parameters of the first stroke patient through the second edge calculation processing terminal received by the broadcasting component;
each of the first combination sensors is absent an identification number; each second combined sensor is configured with a unique identification number corresponding to the stroke patient; the remote centralized control platform is in wireless communication with the K miniature edge data processing units and the N timers to acquire the unique identification number of the second combined sensor for acquiring a plurality of physiological activity parameters generating the early warning signal;
based on the unique identification number, the remote centralized control platform searches a preset patient database to obtain corresponding nursing staff information;
starting a corresponding timer based on the nursing staff information, and sending the state information of the timer to nursing staff;
the status information of the timer includes a countdown indicating a time limit to which the caregiver should respond.
2. The stroke warning system based on real-time data acquisition as claimed in claim 1, wherein:
the plurality of physiological activity parameters include blood pressure, heart rate, activity speed, gait.
3. The stroke warning system based on real-time data acquisition as claimed in claim 1, wherein:
at least one data trend analysis model is built in the micro edge data processing unit and serves as the local early warning model.
4. The stroke warning system based on real-time data acquisition as claimed in claim 3, wherein:
the data trend analysis model is a physiological signal time trend model or an early warning model.
5. The stroke warning system based on real-time data acquisition as claimed in claim 3 or 4, wherein:
and performing at least two edge calculation processes on a plurality of physiological activity parameters of the patient with the same identification number so as to perform data trend analysis.
6. An active intervention system for brain stroke based on real-time data acquisition, the active intervention system comprising a plurality of portable devices, the plurality of portable devices being connected to the early warning system of any one of claims 1 to 5 and receiving a status signal of a timer in the early warning system.
7. The active stroke intervention system based on real-time data acquisition of claim 6, wherein:
the portable device is a wearable device, the wearable device comprises a navigation sensor, and the navigation sensor is used for navigating the wearable device to a position close to the first combination sensor or the second combination sensor.
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| CN107863152A (en) * | 2017-11-08 | 2018-03-30 | 杨昆蓉 | Cerebral apoplexy early warning system and method |
| CN109730687A (en) * | 2019-01-14 | 2019-05-10 | 清华大学 | Wearable gait testing and analysis system for patients with cerebral palsy |
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