BE-FAST-based cerebral apoplexy early-stage self-detection system and method

Abstract

The invention relates to the technical field of medical instruments, in particular to a BE-FAST-based cerebral apoplexy early-stage self-detection system, which comprises interactive control equipment, an identity verification device, a detection device and an equipment end, the interactive control equipment provides an operation interface and is used for inputting a control instruction, activating the detection device after the control instruction passes verification of the identity verification device, receiving monitoring data of the detection device in real time and sending an evaluation result to the equipment end, the detection device comprises a facial expression muscle detection assembly, an eye movement detection assembly, a hearing test assembly, an arm balance degree detection assembly and a walking balance degree detection assembly, and the detection device operates and obtains monitoring data for stroke risk assessment by receiving a control command sent by the interactive equipment. By integrating high-sensitivity sensors, key parameters are accurately obtained and analyzed, the accuracy is improved, the detection process is greatly simplified, and non-professionals can perform self-detection at home or in a community environment.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to an early self-detection system and method for stroke based on BE-FAST. Background Art

[0002] Stroke is one of the diseases with high disability, mortality and recurrence rates. Among them, acute ischemic stroke is the most common type, accounting for 69.6%-72.8% of new strokes in my country. Acute ischemic stroke refers to the sudden blockage or rupture of cerebral blood vessels, which leads to the interruption of blood supply to local brain tissue. The subsequent ischemic or hypoxic damage leads to corresponding neurological deficits. The mortality rate during hospitalization is 0.5%, and the complication rate is 12.8%. The mortality rate is 1.5%-3.2% 3 months after the disease, and the mortality rate is as high as 6% in one year. The disability rate is 14.6%-23.1% 3 months after the disease, and the disability rate is 13.9-14.2% in one year. The recurrence rate of stroke is 6.5% 3 months after the disease, and the recurrence rate is 10.3% in one year.

[0003] Currently, the most effective treatment for acute ischemic stroke (AIS) is intravenous thrombolysis with recombinant tissue plasminogen activator (rt-PA) within 4.5 hours (ultra-early) after onset. However, due to the lack of experience of community residents in early identification of symptoms, the proportion of patients who actually receive intravenous thrombolysis is only about 2.4%. In this context, early detection and identification of stroke symptoms is particularly important. Although existing identification tools such as FAST and BE-FAST provide some help, the effective use of these tools usually depends on the professional judgment and interpretation of medical staff, and the accuracy of residents' self-identification needs to be improved urgently.

[0004] Therefore, developing a simple and reliable early identification and assessment system that can be widely used in hospitals and community environments for non-professionals can effectively improve the efficiency and probability of emergency treatment of stroke. Summary of the invention

[0005] The purpose of the present invention is to provide a BE-FAST-based early self-detection system and method for stroke in view of the above technical problems, to overcome the scarcity and blank of existing stroke self-detection technology, to provide a self-detection of common stroke symptoms by self-operation, and to make evaluations and suggestions by comparing the detection data with the expert database data by the professional software in the device after the inspection. The purpose of the present invention can be achieved by the following technical solutions:

[0006] The present invention provides a BE-FAST-based early self-detection system for stroke, the system comprising an interactive control device, an identity verification device, a detection device and a device terminal;

[0007] The interactive control device is respectively connected to the identity authentication device, the detection device and the device end by wire / wireless connection. The interactive control device provides an operation interface for inputting control instructions. After verification by the identity authentication device, the detection device is activated to collect data, and the monitoring data of the detection device is received in real time, and the evaluation results are sent to the device end. The detection device includes a facial expression muscle detection component, an eye movement detection component, a hearing test component, an arm balance detection component and a walking balance detection component. By receiving the control command issued by the interactive device, the monitoring data for stroke risk assessment is run and obtained, wherein the monitoring data includes facial muscle change data, eye muscle rotation signals, hearing test results, left and right arm muscle change data and walking balance data.

[0008] Furthermore, the facial expression muscle detection component adopts a shell structure that matches the shape of the skull;

[0009] A double-layer hollow medical rubber mold and several flexible sensors are provided inside the shell. The double-layer hollow medical rubber mold is provided with a gas injection port, which is configured to inject trace gas to make the several flexible sensors fit the face and capture the facial muscle change data.

[0010] Furthermore, the eye movement detection component includes a glasses-type frame, a plurality of high-definition sensors and controlled LEDs; wherein the glasses-type frame is embedded in the eye position of the outer shell structure to support and fix the left and right eyeglass barrels in the corresponding glasses-type frame; at least three controlled LEDs are arranged on the edge of the eyeglass barrels, which light up in sequence according to the settings to guide the user's eye movement; at the same time, a plurality of high-definition sensor heads are embedded in the eyeglass barrels to capture eye muscle rotation signals.

[0011] Furthermore, the hearing test component includes a pair of test headphones, which are configured to play audio instructions to guide the user to complete corresponding operations according to the audio quality to obtain corresponding hearing test results.

[0012] Furthermore, the arm balance detection component includes two sleeves for placing and detecting the left and right arms; the outer layer of the sleeve is a soft PVC polymer material, and the inner layer is a hard canvas; a plurality of thin film pressure sensing channels are clamped on the inner side of the inner layer, and a plurality of groups of pressure sensors are provided on each thin film pressure sensing channel to form a sensor matrix for real-time monitoring of the balance and movement state of the left and right arms; wherein, thin film airbags are provided on the inner side of the plurality of thin film pressure sensors, which are configured to be inflated to make the plurality of thin film pressure sensors close to the left and right arms during detection, so as to obtain the muscle change data of the left and right arms within a preset time, and output an electrical signal at the mV level.

[0013] Furthermore, the arm balance detection component also includes a grip force sensor for obtaining the user's grip force data.

[0014] Furthermore, the walking balance detection component includes a walking pedal and a transmission unit, wherein the transmission unit is arranged at the front and rear ends of the walking pedal, and when the tester stands on the surface of the walking pedal and walks, the walking pedal rotates around the transmission unit;

[0015] The transmission unit is a roller, a shaft stick is provided through the middle of the roller, and a plurality of grooves are arranged in the middle of the shaft stick, and the grooves are used to provide a movement channel for the sensor when the walking pedal rotates;

[0016] The walking pedal is configured as a double-layer structure, wherein the walking pedal is made of a special non-slip textile material and a thin film pressure sensor is arranged between the double-layer structure; and the walking pedal is provided with at least three groups of pattern prompts corresponding to three groups of monitoring areas, to obtain the gait data of the tester in the corresponding monitoring area, and transmit the collected analog data to the interactive control device through the slip ring on the shaft stick;

[0017] A support column is provided at the bottom of the walking pedal, which is used to lift the walking pedal off the ground; and a pulley is provided at the front end of the walking pedal, which is arranged between the walking pedal and the ground. The pulley is used to reduce the friction between the walking pedal and the ground, ensuring that the friction generated between the walking pedal and the person being tested during the walking process drives the walking pedal to rotate.

[0018] Furthermore, the interactive control device is also equipped with an identity authentication device, including an RFID radio frequency card reader and a facial recognition device, for collecting, verifying and archiving the user's personal information.

[0019] Furthermore, the interactive control device is connected to the device side through the built-in 5G / WiFi, data transmission is realized by installing a SIM card and a 5G base station, and the evaluation results are distributed to the device side.

[0020] Based on the same inventive concept, the present invention also provides a method for early self-detection of stroke based on BE-FAST, using the above-mentioned early self-detection system of stroke, comprising:

[0021] Step S1, initializing the interactive control device and performing user authentication through the identity authentication device, collecting and storing the user's personal information;

[0022] Step S2, after identity authentication, select and start the specified components in the detection device including the facial expression muscle detection component, the eye movement detection component, the hearing test component, the arm balance detection component or the walking balance detection component through the interactive control device, and perform data collection;

[0023] Step S3, pre-processing the collected data and converting it into signals for analysis, and sending the evaluation results to the designated user end, including facial muscle change data collected by the facial expression muscle detection component, analyzing the activity frequency and intensity of the facial muscles, and evaluating the facial nerve function; tracking the eye movement signals collected by the sensor of the eye movement detection component, and analyzing and evaluating the coordination and speed of the eye movement through speed calculation and movement trajectory; evaluating the hearing level through the hearing reaction time collected by the hearing test component through the sound of different frequencies; evaluating the arm muscle control ability through the grip strength data and arm shaking frequency collected by the arm balance detection component; analyzing the gait stability through the gait analysis data and balance performance data collected by the walking balance detection component.

[0024] Compared with the prior art, the present invention has at least one of the following technical effects:

[0025] (1) The early self-detection system for stroke of the present application not only improves the accuracy of early identification of stroke, but also greatly simplifies the detection process, allowing non-professionals to perform self-detection at home or in a community environment. This is of great significance for alleviating the shortage of medical resources and improving the survival rate and quality of life of stroke patients.

[0026] (2) By integrating highly sensitive sensors, key parameters such as eye movement signals, facial expression changes, hearing response, arm and gait balance, etc. can be accurately acquired and analyzed, thus providing a scientific basis for the early diagnosis of stroke. In addition, through automated data processing and wireless transmission technology, the detection data can be transmitted to the user end in real time, improving the speed and efficiency of response. At the same time, through 5G communication technology, high-speed and stable data transmission is ensured, making telemedicine services more feasible and reliable, and suitable for remote areas or communities with fewer resources, greatly improving the level of local medical services. Through real-time and accurate early diagnosis, the mortality and disability rates caused by stroke have been significantly reduced, and the quality of life of patients has been improved. At the same time, early diagnosis can also help reduce the medical and social costs caused by stroke, with significant social and economic benefits.

[0027] (3) The present invention not only improves portability and ease of use through modularization and integrated installation connection, but also reduces the production and maintenance costs of the product, providing an economical and affordable early stroke screening solution. In addition, the system device is highly customizable and can be adjusted according to the specific needs of different users, enhancing the market adaptability of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the following is a brief introduction to the drawings required for describing the embodiment:

[0029] Figure 1 is a block diagram of the early self-detection system for stroke based on BE-FAST in this embodiment;

[0030] Figure 2 This is a data interaction block diagram of the single-chip microcomputer of the interactive control device in this embodiment;

[0031] Figure 3 This is an example diagram of a thin film pressure sensor matrix in this embodiment;

[0032] Figure 4 Schematic diagram of the shaft stick in the walking balance detection component in this embodiment

[0033] Figure 5 FIG. 4 is a voltage schematic diagram of the switch-stabilized power supply in this embodiment. DETAILED DESCRIPTION

[0034] The purpose of the present invention is to provide a vertical unfolding stroke early self-detection system based on BE-FAST, which includes a main frame, a head placement frame, and a facial expression muscle detection component, an eye movement detection component, a hearing test component, an arm balance detection component and a walking balance detection component for data collection. The upper end of the frame is composed of an arm balance test sleeve, and a high-sensitivity pressure sensor is installed inside the sleeve. A layer of thermoplastic film is used to capture slight frequency jitters and involuntary shaking, and can timely record the mV-level electrical energy generated by the sensor when in contact. The frequency and amplitude of this jitter are recorded and stored through the internal memory of the circuit. The generated electrical signal is proportional to the degree of limb balance movement load (that is, the degree of balance of the arm movement). At the same time, the system is also equipped with early detection references for eye movement and hearing. It can be further developed to help detect stroke and cooperate with the single-chip control board to connect the thin film sensor to collect the arm's 180-degree stability data, and the lower limb layout walking pedal to collect balance and pressure. The walking pedal is connected to the control board data collection in the interactive control device through a flexible pressure sensor. The pedal has a rolling axis, and the rolling axis segment has a concave pressure sensor to pass through and collect the walking data. This application can achieve modular production and integrated installation and connection. The bottom uses wheels with brakes for easy movement. At the same time, a radio frequency card or face recognition method is used to enter the test device to quickly carry out the purpose of collecting basic data for early stroke. After the test, the collected data can be quickly calculated, processed and packaged, and health status assessment and suggestions can be given through the WiFi transmission of the 5G network. This self-detection device is suitable for the elderly in the community to check the early state of stroke.

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present invention refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0037] Example 1

[0038] As the global aging trend intensifies, stroke has become one of the leading causes of disability and death worldwide. Despite advances in medical technology, emergency response to ischemic stroke remains challenging in many communities, especially in early identification of stroke symptoms. Historical data show that early identification and timely intervention are key factors in reducing the severity of stroke consequences. However, ordinary residents often lack the necessary knowledge and tools to effectively identify the early signs of stroke, leading to delayed treatment.

[0039] In order to solve this problem, the present invention provides an early self-detection system for stroke, which combines modern medical knowledge and high-end technology, and aims to enable non-professionals to quickly and effectively perform stroke screening at home or in a community environment. At the same time, based on extensive clinical data and the direct experience of 200 neurology experts, this system integrates the self-perceived symptoms and medical records of more than 120 stroke patients, summarizes and screens more than 40 stroke phenomena and compresses them into six typical signs that can basically diagnose stroke. At the same time, the diagnostic experience of many experts is written into the evaluation and comparison software.

[0040] The system is designed as an easy-to-use all-in-one machine that can run 24 hours a day. It not only uses advanced hardware for symptom detection, but also is equipped with a powerful expert system database that stores a large amount of professional physician data for comparison with patient self-detection data to provide evaluation and suggestions. The system uses the FAST and BE-FAST scoring systems, which are two internationally recognized rapid stroke identification tools. FAST is used to identify the three typical symptoms of stroke: facial droop, arm weakness, and slurred speech, while BE-FAST adds balance and vision problems to cover a wider range of stroke symptoms.

[0041] By integrating these functions into the device, the present invention can not only help identify stroke symptoms early, but also quickly send data and alarms to medical centers via 5G networks, greatly shortening the time from identification to treatment and improving the effectiveness of treatment and the survival rate of patients.

[0042] The purpose of the present invention is achieved by a data acquisition technical solution using a variety of sensors with different performances: there are resistive flexible pressure sensors, capacitive flexible pressure sensors and piezoelectric flexible sensors. Resistive flexible sensors are usually composed of conductive sensing elements coupled to elastic polymers or other flexible stretchable substrate materials (such as fibers, yarns and textiles). Resistive sensors deform the internal structure of conductive materials, causing changes in contact resistance between conductive elements, resulting in changes in output voltage. Sensitive detection performance and fast response are the most widely used flexible sensors. Capacitive flexible sensors are usually composed of three layers of materials. Mechanical deformation caused by external stress usually causes spatial changes between two electrodes, resulting in changes in capacitance. Due to the limitation of geometric effects, capacitive sensors have low sensitivity. Piezoelectric flexible sensors are composed of piezoelectric materials with piezoelectric effect. Under mechanical pressure, they can generate electric charges and have the ability to convert mechanical deformation into electric potential. This type of flexible sensor has the characteristics of high sensitivity and fast response speed. However, due to the small deformation capacity of piezoelectric materials, traditional piezoelectric strain sensors lack flexibility and stretchability and are only used in applications with low strain ranges. This patent focuses on using resistive piezoelectric sensors as the main method of data acquisition.

[0043] The present invention provides an early self-detection system for stroke based on BE-FAST, the purpose of which is to overcome the scarcity and blank of existing self-detection technology for stroke, and to provide a self-detection of common stroke symptoms by self-operation, and after the inspection, the professional software in the device makes an evaluation and suggestion based on the comparison of the detection data and the expert database data. The evaluation results of the detection device are graded by 5G modern communication and quickly transmitted through the network WiFi to the mobile phone APP of the subject, and displayed on the detection screen. In case of a subject with severe symptoms, the subject is directly transmitted to the 120 emergency center and the community monitoring center through the network in the fastest and quickest way, and the subject is sent to the emergency chest pain center in the fastest and quickest way to receive proper treatment. This system can achieve the purpose of quickly launching the application of basic data collection for early stroke, and can quickly calculate and process the collected data after the detection, and give health status evaluation and suggestions through analysis and calculation. It is especially suitable for the needs of the elderly group in the community to quickly deal with the early symptoms of stroke.

[0044] like Figure 1 As shown, the early self-detection system of stroke of the present application includes an interactive control device, an identity authentication device, a detection device and a device end;

[0045] The interactive control device is respectively connected to the identity authentication device, the detection device and the device end by wire / wireless connection. The interactive control device provides an operation interface for inputting control instructions. After verification by the identity authentication device, the detection device is activated to collect data, and the monitoring data of the detection device is received in real time, and the evaluation results are sent to the device end. The detection device includes a facial expression muscle detection component, an eye movement detection component, a hearing test component, an arm balance detection component and a walking balance detection component. By receiving the control command issued by the interactive device, the monitoring data for stroke risk assessment is run and obtained, wherein the monitoring data includes facial muscle change data, eye muscle rotation signals, hearing test results, left and right arm muscle change data and walking balance data.

[0046] It should be specifically stated that the main control device (interactive control device) is connected to the all-in-one computer with a display through 5G-WIFI. Through the SIM card slot designed on the detection device, all data generated by stroke self-detection can be transmitted through the 5G base station in three ways: 1: Through the USB interface of the integrated computer main display, it is transmitted to the monitoring center subsystem through the 5G private network in accordance with standard specifications, providing accurate data for reference. 2: Through the special software in the mobile phone APP, the measurement data is transmitted to the mobile phone after being processed and packaged by the computer, so that the subjects and their families can grasp the detection data in real time, which is convenient for rapid processing according to the test results. 3: Transmitted to the display (operation interface) designed and installed on the upper part of the stroke self-detection device for the subjects to view and decide on the next step. The 21.5-inch integrated computer display screen on the upper part of the detection device is connected to the module to display the data processed and packaged after real-time acquisition for the subjects to watch. All module equipment data, and the post-data is processed and packaged through the 5G private network according to the interface specification and stored in the Ai memory for system call transmission to various urgent places. During the detection process, the display component (operation interface) installed at the top will display your current operation and the part that needs to be tested next. The display receives and processes the stability data collected by various sensors on the microcontroller control board through the USB port, and displays the data on the large screen in real time.

[0047] At the same time, it should be specifically stated that Figure 2 As shown in the figure, the main control chip used in the stroke self-detection system adopts the Arduino open source software and hardware platform ATmega2560P microcontroller. The chip has 54 digital I / O ports (16 of which can be used as PWM outputs), 16 analog inputs, 4 UART interfaces, a 16MHz crystal oscillator, a USB port, a power socket, an ICSP header and a reset button. Two pins SDA and SCL are added at AREF to support the I2C interface; IOREF and a reserved pin are added, and the USB interface chip directly provided is replaced by ATmega16U2 instead of ATmega8U2. The signal transmission rate is effectively improved. The microcontroller is used to process the information obtained from the subject, which is suitable for the subject to use.

[0048] like Figure 5As shown in the figure, when designing the normal operation of the single-chip control system, a stable working voltage must be designed to convert the 220V AC voltage into multiple sets of DC working voltages required by the MCU and control system. The working efficiency of the traditional linear regulated power supply is only 30% to 50, and 50% of the electrical energy is converted into heat energy and wasted, resulting in extremely low power consumption. The average working efficiency of the switching regulated power supply can reach 70% to 90% under normal conditions. Under the condition of the same voltage drop, the switching power supply regulator has much less "heat loss" than the linear regulated device. Therefore, the switching regulated power supply can greatly reduce the volume of the heat sink and the area of ​​the PCB board. In many cases, there is no need to install a heat sink, which is convenient for the miniaturization of the product, thereby reducing the adverse effects on the MCU working environment. The LM2576 series is a typical product of the new switching voltage regulator. It has a 3A current output step-down switching integrated voltage regulator circuit, contains a fixed frequency oscillator (52kHz) and a reference voltage regulator (1.23V), and has a complete protection circuit, including current limiting and thermal shutdown circuits. The device only requires very few peripheral devices to form an efficient voltage regulator circuit. This patent uses the LM2576 series products, and the entire control circuit is stable and simple, and works reliably. The value of using a single-chip microcomputer control is not only to improve efficiency and reduce resource waste, but also to improve the economic and social benefits that resource consumption should produce.

[0049] Furthermore, the facial expression muscle detection component adopts a shell structure that matches the shape of the skull;

[0050] A double-layer hollow medical rubber mold and several flexible sensors are provided inside the shell. The double-layer hollow medical rubber mold is provided with a gas injection port, which is configured to inject trace gas to make the several flexible sensors fit the face and capture the facial muscle change data.

[0051] It should be specifically explained that, because the human face is a special organ with uneven contours, it is very difficult to make the detection mold completely consistent. Therefore, in addition to installing N flexible sensors on a layer of this component close to the face to detect the muscles, a double-layer hollow medical rubber mold that is similar to the shape of the face is also made. The nasal septum is used as the boundary, and the same trace gas is filled into each mold. The flexible sensors are brought close to the face to detect the performance of the facial muscles. That is, the facial muscles are slightly squeezed by the flexible sensors to obtain data, and whether there is any abnormality in the facial muscle state is detected and collected, and the collected data is analyzed, processed and stored.

[0052] Furthermore, the eye movement detection component includes a glasses-shaped outer frame, a plurality of high-definition sensors and a controlled LED; wherein the glasses-shaped outer frame is embedded in the eye position of the outer shell structure, and is used to support and fix the left and right eyeglass barrels in the corresponding glasses-shaped outer frame;

[0053] At least three controlled LEDs are set on the edge of the glasses barrel, which light up in sequence according to the settings to guide the user's eye movement; at the same time, several high-definition sensor heads are embedded in the glasses barrel to capture and store eye muscle rotation signals.

[0054] Specifically, the stroke self-detection device detects facial expressions and eye movement status as an important indicator. There are two types of stroke: ischemic stroke and hemorrhagic stroke. Ischemic stroke is commonly known as cerebral infarction, accounting for about 85% of stroke cases. Hemorrhagic stroke is commonly known as stroke, accounting for about 15% of stroke cases. Regardless of the type of stroke, the subject will most likely experience symptoms such as dizziness, facial paralysis, slurred speech, and abnormal eye movements. Early symptoms are likely to be ignored. The self-detection device is designed with software and hardware that processes and calculates facial expressions and eye movements through sensors. The detection mechanism is schematically shown (see figure). The eye movement signal acquisition device is used to collect electrical signals from the rotation of eye muscles. The electrode frame is designed to be a 55mm long eyeglass barrel embedded in the face frame. The eyeglass frame has embedded flexible sensors with high sensitivity and better adherence to skin biological tissue. The high flexibility of the sensor electrode allows it to fit more closely with the movement of the eye and facial muscles, thereby collecting more accurate signals. Six 3mm controlled LEDs are installed at the end of the eye barrel that light up in sequence. Voice prompts the eyeballs to rotate as the lit LEDs are used. Finally, the collected original signals of the eye movement are transmitted to the eye movement signal detection and processing module, and the eye movement signals of the left and right eyeballs are obtained after analog conversion and denoising. After being transmitted to the host computer for analysis and processing by the single-chip microcomputer, the results of the analysis and calculation of the left and right eye movement signal detection modules are displayed respectively, and the result data is presented on the computer interface in the form of curves and quantification, showing the difference between left and right eye movement in data. Finally, the eye movement signal collected after the test is connected to the expert diagnosis module for processing, analysis, storage, packaging and sending. It can achieve the best and most effective rescue and treatment opportunities for early detection of stroke symptoms and avoid the subject losing the golden time when discovering early symptoms. This eye movement detection component mainly collects the strength and rotation effect of the eyeball's up, down, left and right movements through a self-made eye movement state synchronization tracker, and provides warm guidance on eye movement by synchronously issuing voice prompts. The two sensors accurately transmit the eye movement state to the I / O port of the single-chip host for processing.

[0055] Furthermore, the hearing test component includes a pair of test headphones, which are configured to play audio instructions to guide the user to complete corresponding operations according to the audio quality to obtain corresponding hearing test results.

[0056] Furthermore, the arm balance detection component includes two sleeves for placing and detecting the left and right arms; the outer layer of the sleeve is a soft PVC polymer material, which is easy to scrub and disinfect after treatment; the inner layer is a hard canvas, and a number of thin film pressure sensing channels are clamped on the inner side of the inner layer (such as setting four thin film pressure sensing channels), and each thin film pressure sensing channel is provided with a number of groups of pressure sensors to form a sensor matrix for real-time monitoring of the balance and movement status of the left and right arms; functional data of the arm balance of the subject is collected, and the pressure sensor matrix transmits the balance data of the upper limb function of the subject to the single-chip microcomputer in real time; wherein, thin film airbags are provided on the inner side of the several thin film pressure sensors, which are configured to make the several thin film pressure sensors close to the left and right arms through inflation during detection, obtain the muscle change data of the left and right arms within a preset time, and output mV level electrical signals.

[0057] Specifically, this thin film pressure sensor uses a pressure sensor of model 42CAEZ, which can detect the frequency of the arm shaking up and down of the person being tested. Six sets of pressure sensors are installed on each side of the balance sleeve. The thin film airbag installed on the inside of the pressure sensor is inflated at the initial startup, so that the pressure sensor is close to the limb end of the arm. At the same time, the display runs synchronously so that the person being tested can clearly know the position of the arm in the sleeve, and the arm position is fine-tuned according to the image. When it is detected that the arm cannot be controlled autonomously and shakes or sways, the pressure sensor will accurately record the time and number of shakes or sways. According to experience, the arm detection time is set at 120 seconds.

[0058] Furthermore, the arm balance detection component also includes a grip sensor for obtaining the user's grip data. The grip sensor in front of the detection device tightly grasped by the subject transmits the data of the grip of the left and right hands to the microcontroller, and the data is transmitted to the monitoring center through the A / D conversion of Atmega2560. After processing, the test data is displayed in real time on the display screen.

[0059] This grip force sensor uses a pressure sensor model LC201-300. The load capacity is 300lb, the working voltage is 15VDC, the minimum working temperature can reach -54℃, and the maximum working temperature is 121℃. The sensor outputs mV / V 2. The performance is stable and the sensitivity is high. The data of the sensor held by the subject is transmitted to the ATmega2560 microcontroller for analysis and processing. The ATmega2560 microcontroller will conduct a comprehensive analysis and processing of the corresponding data indicators transmitted by the sensor. After the various self-test conditions of the subject are obtained based on the data indicators, the data will be uploaded to the network layer through the WLAN connection method through the wireless transmission module based on the TCP / IP protocol of the 5G website. The subject and related personnel only need to install the APP to connect to the cloud network layer to obtain the relevant information of the test, and can directly download and view it.

[0060] Furthermore, the walking balance detection component includes a walking pedal and a transmission unit, which is used to perform real-time detection of the walking balance of the subject;

[0061] The transmission unit is arranged at the front and rear ends of the walking pedal. When the tester stands on the surface of the walking pedal and walks, the walking pedal rotates around the transmission unit.

[0062] The transmission unit is a roller, a shaft stick is provided through the middle of the roller, and a plurality of grooves are arranged in the middle of the shaft stick, and the grooves are used to provide a movement channel for the sensor when the walking pedal rotates;

[0063] The walking pedal is configured as a double-layer structure, wherein the walking pedal is made of a special non-slip textile material and a thin film pressure sensor is arranged between the double-layer structure; and the walking pedal is provided with at least three groups of pattern prompts corresponding to three groups of monitoring areas, to obtain the gait data of the tester in the corresponding monitoring area, and transmit the collected analog data to the interactive control device through the slip ring on the shaft stick;

[0064] A support column is provided at the bottom of the walking pedal, which is used to lift the walking pedal off the ground; and a pulley is provided at the front end of the walking pedal, which is arranged between the walking pedal and the ground. The pulley is used to reduce the friction between the walking pedal and the ground, ensuring that the friction generated between the walking pedal and the person being tested during the walking process drives the walking pedal to rotate.

[0065] It should be specifically stated that the pressure collection of the lower limb balance function and the lower limb strength is carried out through a special ultra-short walking machine for balance stability. The walking bottom plate is made of non-slip special textile materials. The walking gait data is collected through the pressure sensor installed between the fabrics. The walking pedal is about 10mm off the ground through the support column. The two ends are made of special rollers. There are six 18mm wide grooves in the middle of the shaft stick, which are convenient for the pressure sensor to pass stably and safely and collect normal pressure data. There is a 3mm fine hole from the beginning to the end in the center of the roller, which passes through the connecting line to the right side of the shaft stick. The 12 groups of highly sensitive far-infrared photoelectric sensor groups installed on the top of the right side transmit the data collected from the walking balance and strength to the receiving sensor installed on the right side of 5mm through photoelectric transmission, and the walking data is recorded, analyzed and stored. At the same time, the bottom of the gait balance walking machine uses wheels with brakes to achieve stable and reliable operation and stop.

[0066] Specifically, the detection component is made of PDMR material, and is divided into two layers on the inner and outer sides, with a thin film pressure sensor installed between them. The main body is made of PET (polyethylene terephthalate) material. The thin film pressure sensor is small in size, small in thickness, light in weight, good in plasticity and stability. The outer layer of the walking detection is painted with a foot print frame picture. The whole walking component is similar to a treadmill. The walking machine (or treadmill) of this application is 1 meter long. The embedded sensor is the best choice for walking balance. Anti-fall bars are set on both sides of the walking machine. The two ends of the walking component are equipped with 100mm diameter round rods with bearings, and the structures of the two ends are basically consistent. A ground-contact pulley is set at the bottom of the front end of the running frame to ensure that the walking components reduce the friction with the ground. In order to ensure the accuracy of data collection during walking, the walking detection device collects the pressure sensor through another slip ring belt set on the inner layer of the PDMR material of the drum to transmit the balance data collected by the walking pressure sensor to the subject.

[0067] Combined with Figure 3 and Figure 4The specific test for collecting walking balance is described in detail for this component. By using multiple slow walking tests to detect the pressure information of the left and right feet of the person being tested, the walking balance data is collected and processed. In the normal walking state, the B and C flexible sensors receive the walking data of the average person. During the walking test lasting several minutes, the walking action is controlled by the neurons in the motor area of ​​the brain, and the person is fully able to accept the control signal and walk stably in the B and C areas. When the early subjects are walking, the brain control will be out of control for a moment or temporarily, and then there will be an involuntary imbalance in walking. According to the area where the brain is temporarily out of control, there will be an unbalanced gait. In some cases, the walking imbalance is more on the left, and the data collected by the A sensor is more than the normal state. In some cases, the walking imbalance is more on the right, and the data collected by the D group sensor is obviously more than the normal state. The resistance R of the flexible film pressure sensor decreases with the increase of the force F. The advantages are high accuracy of data collection and strong anti-interference ability. The analog signal obtained by the walking pressure sensor is transmitted to the ATmega2560P microcontroller through the slip ring installed on the shaft of the walking machine. After the built-in A / D measurement reads the data of the film pressure sensor, the collected analog quantity is converted into a digital quantity, the gait of the subject's walking is determined, and after processing and packaging, it is sent to the mobile phone APP of the relevant personnel through the 5G base station. At the same time, the microcontroller pre-processes the sampled signal, encapsulates it and sends it to the host computer synchronously through the serial port. In order to ensure the integrity of the extracted walking pressure data collection, and prevent the incomplete pressure data from causing the measurement error due to the edge of the contact sensor position. After computer processing, the display of the right detection device realizes quantitative data display. The collected values ​​of the sensor change due to different pressures. The single-chip microcomputer collects the data changes of the four nodes A, B, C, and D of the flexible film pressure sensor to obtain accurate data on the pressure changes of the sensor during the subject's walking, and quantifies the pressure change data. The pressure values ​​of walking at different positions can be measured to obtain the curve of the pressure sensor pressure and walking data. The patent of the present invention sets the upper and lower limits of the aspect ratio greater than 20% of the normal human foot. When the pressure is completely stepped on the designated B and C areas, the pressure sensor collects data in real time. Generally, the center point of the sole pressure is located near the inner side of the middle part of the sole. The pressure change data of the sole during walking is collected by the film pressure sensor, and the contact part between the walking machine shaft and the film pressure sensor is provided with a groove. The collected data is collected by the thin film sensor and then transmitted to the ATmega2560P microcontroller, which encapsulates the measured data and transmits it to the host computer. The host computer designs the recognition test software algorithm, collects the change data of the subject's walking, and realizes the identification of walking balance.The collected data is connected to the photoelectric sensor through the copper slip ring in the concave part of the walking machine shaft stick through the data line through the round hole in the center of the walking machine shaft stick. As a photoelectric sensor installed on the walking balance receiving device, it must be small, sensitive and stable. The ultra-thin photoelectric sensor EX-10 is an original small photoelectric sensor designed and produced by Panasonic Corporation of Japan. It has a built-in NPN open collector transistor with a maximum inflow current of 50mA. The amplifier is a small sensor with a volume of only 3X5Cm. It is easy to install and has a high-speed response of 0.5ms, which is suitable for detecting small parts and high-speed moving objects. The clear and visible two-color indicator light has a waterproof working mode switch, which can detect its workability before starting without being affected by the background. It is sensitive, stable and reliable, and the red LED light that is easy to install makes it easy to align the detection head. This patent adopts a thin film sensor matrix gait balance recognition algorithm, applies slip ring technology to collect data, and then transmits it to a high-sensitivity photoelectric sensor. The data is received by the I / O port of the microcontroller and analyzed and processed for evaluation. Multiple test results confirm that this method has an average recognition rate of 95.4% for walking balance status. It has certain medical evaluation value in the recognition of gait balance in the early self-detection of stroke.

[0068] Furthermore, the interactive control device is also equipped with an identity authentication device, including an RFID radio frequency card reader and a facial recognition device, which is used to collect, verify and archive the user's personal information. Using an information card (RFID radio frequency card reader) and face recognition to enter the test device increases the privacy of the subject and the reliability of personal information acquisition, and is less likely to make mistakes. It is conducive to accurately matching the self-test data of the subject. Among them, the RFID radio frequency card reader can read the original medical data of the subject, and can store the self-test information of the subject for matching. The facial recognition device can ensure that the subject must first enter the facial information of the subject into the memory of the self-test device before using the facial recognition software. Confirmation is made by comparing facial features during use.

[0069] Furthermore, the interactive control device is connected to the device end through the built-in 5G / WiFi, and data transmission is realized by installing a SIM card and a 5G base station, and the evaluation results are distributed to the device end. Therefore, the advanced and fast 5G network system is used as a bridge to connect with the host computer of the monitoring center. The ESP8266EXWI-FI module is in a high level and is in normal working state. The CH_PD pin of the module must be connected to VCC. The ESP8266EX WI-FI module is connected to the serial port of the microcontroller RXD and TXD to issue and receive instructions. The ESP8266EX WI-FIEXwifi module produced by Espressif Information Technology (Shanghai) Co., Ltd. is used. The ESP8266EX WI-FI module adopts ESP8266 with a high-performance wireless SoC (Sys-temonChip), the core complies with the IEEE 802.11b / g / n protocol, has a built-in TCP / IP protocol stack, and has a built-in high-speed cache memory, which is conducive to improving system performance and reducing memory requirements. ESP8266 is designed for mobile devices and has low-power technology. The power consumption in standby state is less than 1.0mW. It is equipped with 6-8MB software storage space and supports AT (attention) instructions. Users can change the master and slave modes, serial port baud rate, device name, pairing password and other parameters as needed, which is easy to use. Control unit. It mainly realizes the detection and control of keyboard input, display, reminder, and prompt processes, and wireless real-time data transmission and viewing through ESP8266EX WI-FI. The computer can receive physiological data in sequence through the USB interface and transfer it to TXT text files for further data analysis and processing. When these data are abnormal, the host computer of the monitoring center will receive the abnormal information and issue a warning. The device is developed by combining the Arduino platform and the ESP8266EX WI-FI module, giving full play to the characteristics of the platform's software and hardware open source, and has a series of advantages such as fast development, simplicity, stability and reliability. There are usually two usage schemes for ESP8266: one is to directly use the internal processor, conduct secondary development of the chip, and control devices such as relays and sensors through the I / O port. This scheme saves costs, but the development is difficult; the other is to connect the ESP8266 module to an external microcontroller, which is only used to complete network-related tasks, use AT command operations, communicate with the external microcontroller through the serial port, and communicate with the host computer of the physician workstation through an advanced and fast remote network system as a bridge of contact. The ESP8266EXWI-FI module is in a high level and is in normal working state. The CH_PD pin of the module must be connected to VCC. The ESP8266EX WI-FI module is connected to the serial port of the microcontroller RXD and TXD to send and receive instructions. Use the ESP8266EXWI-FIEXwifi module.The ESP8266EX WI-FI module adopts ESP8266, which has a high-performance wireless SoC (Sys-temon Chip). The core complies with the IEEE 802.11b / g / n protocol, has a built-in TCP / IP protocol stack, and has a built-in high-speed cache memory, which is conducive to improving system performance and reducing memory requirements. ESP8266 is designed for mobile devices and has low-power technology. The power consumption in standby state is less than 1.0mW. It is equipped with 6-8MB software storage space and supports AT (attention) instructions. Users can change the master and slave modes and serial port baud rate, device name, pairing password and other parameters as needed, which is easy to use. Control unit. It mainly realizes the detection and control of keyboard input, display, reminder, and prompt processes, and wireless real-time data transmission and viewing through ESP8266EX WI-FI. The computer can receive physiological data in sequence through the USB interface and transfer it to TXT text files for further data analysis and processing. When these data are abnormal, the host machine will issue a warning. At this time, the doctor on duty will analyze and compare the results based on the changing condition and provide timely assessment suggestions to the person being examined remotely.

[0070] Example 2

[0071] Based on the same inventive concept, the present invention also provides a method for early self-detection of stroke based on BE-FAST, using the above-mentioned early self-detection system of stroke, comprising:

[0072] Step S1, initializing the interactive control device and performing user authentication through the identity authentication device, collecting and storing the user's personal information;

[0073] Step S2, after identity authentication, select and start the specified components in the detection device including the facial expression muscle detection component, the eye movement detection component, the hearing test component, the arm balance detection component or the walking balance detection component through the interactive control device, and perform data collection;

[0074] Step S3, pre-processing the collected data and converting it into signals for analysis, and sending the evaluation results to the designated user end, including facial muscle change data collected by the facial expression muscle detection component, analyzing the activity frequency and intensity of the facial muscles, and evaluating the facial nerve function; tracking the eye movement signals collected by the sensor of the eye movement detection component, and analyzing and evaluating the coordination and speed of the eye movement through speed calculation and movement trajectory; evaluating the hearing level through the hearing reaction time collected by the hearing test component through the sound of different frequencies; evaluating the arm muscle control ability through the grip strength data and arm shaking frequency collected by the arm balance detection component; analyzing the gait stability through the gait analysis data and balance performance data collected by the walking balance detection component.

[0075] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. According to the content of this specification, many modifications and personalized choices of implementation methods can be made, including but not limited to different demodulation methods, different error correction and detection coding methods, and different sub-segment arrangements. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An early self-detection system for stroke based on BE-FAST, characterized in that: The system includes an interactive control device, an identity verification device, a detection device and a device terminal; The interactive control device is connected to the identity authentication device, the detection device and the device end by wire or wireless connection respectively. The interactive control device provides an operation interface for inputting control instructions. After being verified by the identity authentication device, the detection device is activated to collect data, and the monitoring data of the detection device is received in real time, and the evaluation results are sent to the device end, wherein: The detection device includes a facial expression muscle detection component, an eye movement detection component, a hearing test component, an arm balance detection component and a walking balance detection component. By receiving the control command issued by the interactive device, the detection device runs and obtains the monitoring data for stroke risk assessment, wherein the monitoring data includes facial muscle change data, eye muscle rotation signals, hearing test results, left and right arm muscle change data and walking balance data.

2. The stroke early self-detection system according to claim 1, characterized in that: The facial expression muscle detection component adopts a shell structure that matches the shape of the skull; A double-layer hollow medical rubber mold and a plurality of flexible sensors are arranged inside the shell. The double-layer hollow medical rubber mold is provided with a gas injection port, which is configured to inject a trace amount of gas to make the plurality of flexible sensors fit the face and capture the facial muscle change data.

3. The stroke early self-detection system according to claim 2, characterized in that: The eye movement detection component includes a glasses-shaped frame, a plurality of high-definition sensors and a controlled LED; wherein, The glasses-shaped outer frame is embedded in the eye position of the outer shell structure, and is used to support and fix the left and right glasses barrels in the corresponding glasses-shaped outer frame; At least three of the controlled LEDs are arranged at the edge of the eyeglass barrel, and light up in sequence according to the settings to guide the user's eye movement; at the same time, a plurality of the high-definition sensor heads are embedded in the eyeglass barrel to capture the eye muscle rotation signals.

4. The early self-detection system for stroke according to claim 3, characterized in that: The hearing test component includes a pair of test headphones, which are configured to play audio instructions to guide the user to complete corresponding operations according to the audio quality to obtain the corresponding hearing test results.

5. The stroke early self-detection system according to claim 1, characterized in that: The arm balance detection component includes two sleeves for placing and detecting the left and right arms; the outer layer of the sleeve is a soft PVC polymer material, and the inner layer is a hard canvas; a plurality of thin film pressure sensing channels are clamped on the inner side of the inner layer, and a plurality of groups of pressure sensors are provided on each of the thin film pressure sensing channels to form a sensor matrix for real-time monitoring of the balance and movement state of the left and right arms; wherein thin film airbags are provided on the inner side of the plurality of thin film pressure sensors, and are configured to inflate the plurality of thin film pressure sensors to be close to the left and right arms during detection, so as to obtain the muscle change data of the left and right arms within a preset time, and output an electrical signal at the mV level.

6. The stroke early self-detection system according to claim 5, characterized in that: The arm balance detection component also includes a grip force sensor for acquiring the grip force data of the user.

7. The stroke early self-detection system according to claim 6, characterized in that: The walking balance detection assembly includes a walking pedal and a transmission unit, wherein the transmission unit is arranged at the front and rear ends of the walking pedal, and when the tester stands on the surface of the walking pedal and walks, the walking pedal rotates around the transmission unit; The transmission unit is a roller, a shaft rod is provided through the middle of the roller, and a plurality of grooves are arranged in an array in the middle of the shaft rod, and the grooves are used to provide a movement channel for the sensor when the walking pedal rotates; The walking pedal is configured as a double-layer structure, wherein the walking pedal is made of a special non-slip textile material and a thin film pressure sensor is arranged between the double-layer structure; and the walking pedal is provided with at least three groups of pattern prompts corresponding to three groups of monitoring areas, so as to obtain the gait data of the tester in the corresponding monitoring areas, and transmit the collected analog data to the interactive control device through the slip ring on the shaft stick; A support column is provided at the bottom of the walking pedal, and the support column is used to lift the walking pedal off the ground; and a pulley is provided at the front end of the walking pedal, and the pulley is arranged between the walking pedal and the ground. The pulley is used to reduce the friction between the walking pedal and the ground, ensuring that the friction generated between the walking pedal and the person being tested during the movement drives the walking pedal to rotate.

8. The stroke early self-detection system according to claim 1, characterized in that: The interactive control device is also equipped with the identity verification device, including an RFID radio frequency card reader and a facial recognition device, which are used to collect, verify and archive the user's personal information.

9. The stroke early self-detection system according to claim 1, characterized in that: The interactive control device is connected to the device end via built-in 5G / WiFi, data transmission is achieved by installing a SIM card and a 5G base station, and the evaluation result is distributed to the device end.

10. A method for early self-detection of stroke based on BE-FAST, using the early self-detection system for stroke as claimed in claims 1 to 9, characterized in that: include, Step S1, initializing the interactive control device and performing user authentication through the identity authentication device, collecting and storing the user's personal information; Step S2, after passing the identity authentication, selecting and starting the specified components in the detection device including the facial expression muscle detection component, the eye movement detection component, the hearing test component, the arm balance detection component or the walking balance detection component through the interactive control device, and performing data collection; Step S3, pre-processing the collected data and converting it into signals for analysis, and sending the evaluation results to the designated user end, including: analyzing the activity frequency and intensity of the facial muscles and evaluating the facial nerve function through the facial muscle change data collected by the facial expression muscle detection component; tracking the eye movement signals collected by the sensor of the eye movement detection component, and analyzing and evaluating the coordination and speed of the eye movement through speed calculation and movement trajectory; evaluating the hearing level through the hearing reaction time collected by the hearing test component through the sound of different frequencies; evaluating the muscle control ability of the arm through the grip strength data and arm shaking frequency collected by the arm balance detection component; analyzing gait stability through the gait analysis data and balance performance data collected by the walking balance detection component.

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