WO2022088441A1

WO2022088441A1 - Health monitoring device

Info

Publication number: WO2022088441A1
Application number: PCT/CN2020/137973
Authority: WO
Inventors: 王景峰; 黄凯; 陈样新; 张玉玲; 郭思璐; 宋日辉
Original assignee: 生物岛实验室; 中山大学孙逸仙纪念医院
Priority date: 2020-10-26
Filing date: 2020-12-21
Publication date: 2022-05-05
Also published as: CN112263261A

Abstract

A health monitoring device (100), comprising an output assembly (110), a wearable assembly (120, 210), a processing assembly (130, 300), and a battery (140). The wearable assembly (120, 210) is formed by mixing and weaving a conductive material and an insulating material; the conductive material forms a plurality of electrodes on the wearable assembly (120, 210) and forms an electrocardiogram (ECG) signal acquisition circuit among the plurality of electrodes; the processing assembly (130, 300) comprises a scheduler (311) and a plurality of operators (321, 322, 323), and the scheduler (311) is configured to receive an ECG signal, schedule the plurality of operators (321, 322, 323) so as to determine health state information of a user, and control the output assembly (110) to output early warning information; the operators (321, 322, 323) each comprise a programmable logic gate array, a task operation unit of a binary neural network model is solidly provided within the operators (321, 322, 323), and the binary neural network model is used to determine health status information on the basis of ECG signals; and the battery (140) is used for supplying power to the wearable assembly (120, 210), the processing assembly (130, 300) and the output assembly (110). The health monitoring device (100) has low power consumption, a small volume and is convenient to wear for a long time.

Description

Health monitoring equipment

CROSS-REFERENCE TO RELATED APPLICATIONS

The present disclosure claims the priority of Chinese Patent Application No. "CN202011157399.6" filed on October 26, 2020, the entire contents of which are incorporated into this application as a whole.

technical field

The present disclosure relates to the technical field of wearable devices, and in particular, to a health monitoring device.

Background technique

Cardiovascular disease is a serious threat to human health due to its hidden and sudden characteristics, so early diagnosis and prevention are very important. At present, the 12-lead dynamic detector (Holter) commonly used in hospitals has large power consumption and volume, and is generally operated under the guidance of a doctor when wearing it, and cannot meet the needs of 3-7 days of continuous medical testing. Moreover, the current Holter's ECG monitoring and judgment needs to transmit data to the background server for calculation. This calculation lag is very obvious and cannot meet the needs of real-time calculation and all-weather real-time warning.

SUMMARY OF THE INVENTION

In order to solve the problems in the related art, embodiments of the present disclosure provide a health monitoring device.

Specifically, the health monitoring equipment includes:

output component;

The wearable component is woven by mixing conductive material and insulating material, the conductive material forms a plurality of electrodes on the wearable component and an ECG signal acquisition circuit is formed between the plurality of electrodes;

A processing component including a scheduler and a plurality of operators, wherein the scheduler is configured to receive the electrocardiographic signal, schedule the plurality of operators to determine the health status information of the user, and based on the health status The information controls the output component to output early warning information. The operator includes a programmable logic gate array. The operator is solidly provided with a task operation unit of a binary neural network model, and a hidden layer of the binary neural network model. The weights and activation values of are binary data for determining health status information based on ECG signals; and

a battery for supplying power to the wearable component, the processing component and the output component.

According to an embodiment of the present disclosure, the scheduler includes a central processing unit, the arithmetic unit further includes a general-purpose graphics processor and an integrated chip customized based on a programmable logic gate array, and the scheduler and the arithmetic unit are deployed on a printed circuit board .

According to an embodiment of the present disclosure, the processing component further includes:

The power supply management unit is configured to adjust the number of powered arithmetic units based on the current task load.

The preprocessing unit is configured to perform preprocessing on the ECG signal, the preprocessing includes at least one of noise processing, baseline drift processing, heart beat segmentation, feature pre-extraction and data resampling.

According to the embodiment of the present disclosure, the weight of the binary neural network is -1 or 1.

According to the embodiment of the present disclosure, the binary neural network model is obtained by training the following methods:

Constructing a real-number neural network model, the weights and activation values of the hidden layer of the real-number neural network model are real-number data;

The real number type neural network model is trained and compressed using the training data to obtain a binary neural network model.

According to the embodiment of the present disclosure, the use of training data to train and compress the real-number neural network model to obtain a binary neural network model includes:

Get training data;

The training operation and the compression operation are alternately performed until the convergence conditions are met, and a binary neural network model is obtained, wherein, in the training operation, the weights in the model are updated through the training data; in the compression operation, the Size of branches and/or binarized compressed models.

According to an embodiment of the present disclosure, the acquiring training data includes:

Obtain ECG data of people with different health states;

Performing preprocessing on the ECG data to obtain training samples, where the preprocessing includes at least one of noise processing, baseline drift processing, heart beat segmentation, feature pre-extraction, and data resampling;

The training samples are marked to obtain sample labels, and the sample labels represent different health states corresponding to the ECG data, and the training samples and the sample labels are determined as training data.

According to an embodiment of the present disclosure, the output component has a first output mode and a second output mode, and the processing component is configured to:

When the state of health is the first type of state, control the output device to output early warning information according to the first output mode;

When the state of health is the state of the second type, the output device is controlled to output early warning information according to the second output mode.

According to an embodiment of the present disclosure, the output device includes a three-color indicator light, and the processing component is configured to:

In the case that the health state is a normal state, control the green indicator light to light;

In the case that the health state is an abnormal state, control the yellow indicator light to light;

When the health state is an emergency state, the red indicator light is controlled to light up.

According to the technical solutions provided by the embodiments of the present disclosure, a plurality of electrodes are formed on the wearable component by using a conductive material, and an ECG signal acquisition circuit is formed between the plurality of electrodes; the scheduler is configured to receive the ECG signal, Scheduling a plurality of arithmetic units to determine the health status information of the user, and control the output components to output early warning information, the arithmetic unit includes a programmable logic gate array, and a task arithmetic unit of a binary neural network model is fixed in the arithmetic unit. The neural network model is used to determine the health status information based on the ECG signal; the battery is used to supply power to the wearable components, processing components and output components, so that the health monitoring device can be made smaller and has lower power consumption, which is convenient for Wear it for a long time.

It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

Description of drawings

Other features, objects and advantages of the present disclosure will become more apparent from the following detailed description of non-limiting embodiments, taken in conjunction with the accompanying drawings. In the attached image:

1 shows a block diagram of a health monitoring device according to an embodiment of the present disclosure;

2 shows a schematic diagram of a health monitoring device according to an embodiment of the present disclosure;

3 illustrates a block diagram of processing components according to an embodiment of the present disclosure;

FIG. 4 shows a schematic diagram of a master device according to an embodiment of the present disclosure.

Detailed ways

Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. Also, for the sake of clarity, parts unrelated to describing the exemplary embodiments are omitted from the drawings.

In the present disclosure, it should be understood that terms such as "comprising" or "having" are intended to indicate the presence of features, numbers, steps, acts, components, parts, or combinations thereof disclosed in this specification, and are not intended to exclude a or multiple other features, numbers, steps, acts, components, parts, or combinations thereof may exist or be added.

In addition, it should be noted that the embodiments of the present disclosure and the features of the embodiments may be combined with each other under the condition of no conflict. The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

At present, the average age of the aging population in China and the world is increasing, and the number of people is increasing year by year. More and more people pay attention to the detection of the physiological data of the aging population. At the same time, the generated monitoring physiological data continues to grow at an exponential growth rate.

In recent years, cardiovascular disease has become the leading cause of death in our country. Among them, myocardial infarction, arrhythmia, sudden cardiac death and other cardiovascular diseases have sudden onset, rapid changes, serious illness and high concealment, which seriously threaten human health. The common clinical ECG and 24-hour ECG are difficult to capture this type of abnormal ECG signals in patients.

The inventor found through market research that most of the ambulatory ECG detectors, including Holter and implantable ECG monitors, can only realize the function of data recording, and none of them have native implantation algorithms. To achieve the local early warning function when cardiovascular events occur, terminal clinical medical experts are required to interpret the data. Some dynamic ECG detectors are equipped with real-time Bluetooth transmission system, the data can be read on the smartphone terminal, and the 4G network data can be remotely transmitted to the background to achieve the monitoring effect. However, it still requires background staff to perform 24-hour detection, which requires huge manpower and material resources, and when the network is unstable or even unavailable, the ECG signal of the monitored person may be lost, and important abnormalities may be missed. signal, causing the early warning function to fail. On the other hand, these ambulatory electrocardiographs have large power consumption and volume, and are generally operated under the guidance of a doctor when worn, and cannot meet the needs of continuous medical testing for 3-7 days.

The health monitoring device provided by the embodiments of the present disclosure can well solve the problem of network instability by locally analyzing the ECG signal through a small, portable processing component. The user detects the ECG signal by wearing a wearable device, and the signal is given real-time warning in the local machine through the algorithm implanted in the machine, which can help residents, patients, health and health management personnel to realize the early warning of sudden cardiovascular disease and solve a large number of health management problems. question. The mobile terminal realizes intelligent medical treatment. It can detect the physiological signals of patients in real time in an all-round way, and capture the paroxysmal abnormal heart rhythm that cannot be recorded by conventional short-term detection. mistake.

FIG. 1 shows a block diagram of a health monitoring device 100 according to an embodiment of the present disclosure.

As shown in FIG. 1 , the health monitoring device 100 includes:

output component 110;

The wearable component 120 is woven by mixing conductive material and insulating material, and the conductive material forms a plurality of electrodes on the wearable component and an ECG signal acquisition circuit is formed between the plurality of electrodes;

The processing component 130 includes a scheduler and a plurality of computing units, wherein the scheduler is configured to receive the ECG signal, schedule the plurality of computing units to determine the health status information of the user, and based on the health status information The state information controls the output component to output early warning information, the operator includes a programmable logic gate array, and a task operation unit of a binary neural network model is fixedly arranged in the operator, and the binary neural network model is hidden. The weights and activation values of the layers are binary data for determining health status information based on the ECG signal; and

The battery 140 is used to supply power to the wearable component, the processing component and the output component.

According to the embodiment of the present disclosure, the output assembly 110, the processing assembly 130 and the battery 140 may be packaged as the main control device through a casing, and the casing may be provided with a fixing structure, such as a spring clip, a fixing belt, etc., for fixing the main control device on the main control device. User clothing, wearable components or user body. For example, the master device may be fastened to the user's pants.

FIG. 2 shows a schematic diagram of a health monitoring device according to an embodiment of the present disclosure.

As shown in FIG. 2 , the health monitoring device may include a wearable component 210 and a main control device 220 .

According to an embodiment of the present disclosure, the wearable component 210 is woven by a mixture of conductive material and insulating material, for example, it may include a plurality of elastic corset loops and an elastic connecting belt connecting the plurality of elastic corset loops, the conductive material is in the A plurality of electrodes are formed on the wearable component and an ECG signal acquisition circuit is formed between the plurality of electrodes.

According to an embodiment of the present disclosure, the conductive material may be an elastic conductive yarn, for example, may contain a copper-nickel material or a conductive sponge material. The insulating material can be, for example, cotton, hemp, silk or various chemical fibers. The wearable component 210 is formed by mixing the conductive material and the insulating material, and an electrode with good bioconductivity is formed on the inner surface of the wearable component 210, thereby obtaining electrophysiological signals on the body surface. The textile has the functions of comfort, fixation, high resilience, ultra-light and breathability.

According to an embodiment of the present disclosure, the plurality of elastic corsets may include, for example, three elastic corsets located on the user's neck, chest and abdomen when the health monitoring device is worn by the user, or more or less may be provided. elastic waistband. Among them, an elastic corset band located near the user's chest is usually necessary because many standardized indicators require electrodes at this location to be collected. However, if these standardized indicators are not required, elastic corsets may not be provided near the user's chest.

According to the technical solutions provided by the embodiments of the present disclosure, since only the elastic body girdle is used, it has better air permeability compared with the overall clothing, and in the case of long-term use, for example, more than 24 hours, or in hot weather In summer, the wearable components of the embodiments of the present disclosure bring a more comfortable experience to use.

In addition, there is almost no mutual interference between the multiple elastic corset belts, which can better adapt to the local situation of the body, such as different bust and waist circumferences, so that the elastic corset belt and the body can be fixed more stably and elastically. The electrodes on the body girdle are not easy to fall off between the body and the body.

Devices such as Holter require the help of a doctor to smooth the patient's skin before the electrodes can be attached to the skin, but they can only last for about 24 hours and cannot be worn for a long time. In the scenario of long-term wearing of the health monitoring device according to the embodiment of the present disclosure, the user can even take off the wearable component halfway, and when wearing it again, no complicated preparation is required. It can automatically fit with the user's body, reducing the difficulty of operation.

According to an embodiment of the present disclosure, the elastic connecting straps can be positioned on the front and back sides of the user's spine, and the elastic corset straps on the neck can be pulled from the front and rear sides to fix it. The elastic connecting straps are not limited to the form shown in the figure, for example, the elastic connecting straps located on the ventral side and the back side can only be provided between the elastic girdle straps located near the neck and near the chest, while the elastic connecting straps near the chest and near the abdomen The elastic connecting belts between the body girdle belts can be arranged on the left and right sides of the human body.

According to the embodiment of the present disclosure, the length of the elastic corset can be adjusted, for example, a length adjustment structure can be provided on the back side, so as to further improve the adaptability of the wearable component and reduce the probability of the electrode falling off.

According to an embodiment of the present disclosure, the elastic corset band on the user's chest has an asymmetric structure in the left-right direction. Since the position of the heart is on the left side, in order to arrange more acquisition electrodes around the position of the heart, the elastic corset band near the chest in the embodiment of the present disclosure has an asymmetric structure in the left-right direction, as shown in FIG. 2 , The area on the left is larger than the area on the right to accommodate more electrodes. While the right side does not require more acquisition electrodes, it can have a smaller area to maintain comfort.

According to the embodiments of the present disclosure, electrodes with good bioconductivity are formed on the inner surface of the wearable component 210 through the mixed weaving of conductive materials, so as to obtain electrophysiological signals on the body surface. When the health monitoring device is worn by the user, these electrodes may be located in at least two of the following positions of the user: the fourth intercostal space on the right sternal border, the fourth intercostal space on the left sternal border, the intersection of the left midclavicular line and the fifth intercostal space, The midpoint of the line connecting the fourth intercostal space on the left sternal border and the intersection, the left anterior axillary line and the intersection at the same level, the left midaxillary line at the same level as the intersection, and the left posterior axillary line at the same level as the intersection , at the same level of the spine as the intersection point, the left and right anterior sides of the abdomen, and the anterior sides of the left and right clavicle. Through the above settings, the 12-lead ECG data of the human body can be collected conveniently, wherein the electrodes of the limbs are replaced with electrodes near the clavicle and abdomen.

The inventor found that the accuracy of the traditional algorithm for ECG signal processing is not very high, and it must rely on a large number of manual feature extraction, which is highly subjective, and the obtained features have no hierarchy at all. Experts come to perform manual identification, which requires relatively high professional knowledge. The health status recognition model based on deep learning has more network layers for feature classification, which can improve the accuracy. However, with the increasing scale of chip design, its functions are becoming more and more complex, and there are big differences in functions, architecture, and design ideas. The existing single commercial semiconductor platform is often difficult to balance computing power and power consumption. On the one hand, with the evolution of functions and capabilities, the related algorithms become more and more complex, so the amount of computation required to complete a specific task is also increasing, and the power consumption during runtime also increases. On the other hand, it is difficult to deploy complex intelligent algorithms due to the portability requirements for wearable devices. In environments with limited load and power consumption, it is also difficult to place bulky, high-quality, high-power high-performance graphics processing unit (GPU) boards to handle complex applications. Therefore, the processing capability of the wearable device processor is often greatly restricted due to the limitation of load, power consumption, and volume.

The embodiments of the present disclosure provide a customized processing component, which can effectively alleviate the above problems.

3 shows a block diagram of a processing component 300 in accordance with an embodiment of the present disclosure.

As shown in FIG. 3 , the processing component 300 includes a scheduler 311 and a plurality of computing

units

321 , 322 and 323 , wherein the scheduler 311 is configured to receive an electrocardiogram signal, and schedule a plurality of computing units to determine the health state of the user information, and control the output component 110 to output early warning information based on the health status information, the arithmetic unit includes a programmable logic gate array (FPGA), and the arithmetic unit is fixedly provided with the task operation of the binary neural network model unit, the weights and activation values of the hidden layer of the binary neural network model are binary data, which are used to determine the health state information based on the ECG signal.

By limiting the weights and activation values of the hidden layer to binary data, and solidifying the neural network model in an operator such as an FPGA, and scheduling by the scheduler, it can greatly reduce the amount of calculation, and reduce the complexity and power of the device. It can meet the needs of portable devices.

According to an embodiment of the present disclosure, the processing assembly 300 may be implemented as a printed circuit board (PCB). The printed circuit board may include, for example, a fixed portion 310 and a reconfigurable portion 320 . The fixed part 310 includes a scheduler 311 for allocating computing tasks to operators according to a scheduling policy; the reconfigurable part 320 includes multiple operators, such as

operators

321, 322, and 323, among the multiple operators Part or all of the task computing unit is fixedly provided with a binary neural network model. The above layout can reduce the area of the original hardware circuit and reduce the volume of the processing components. The reconfigurable portion 320 may, for example, contain hundreds of parallel cores, and the spatial redundancy of computing resources allows safety and non-safety critical applications to coexist, thereby providing an appropriate partitioning mechanism that can be used to achieve high reliability, secure authentication and re-certified multi-core computing processing systems.

According to an embodiment of the present disclosure, the scheduler may be implemented by, for example, a central processing unit (CPU). The scheduler is only used for task scheduling and does not participate in any operation. All computing tasks are executed by the operation unit, which can reduce system power consumption.

According to an embodiment of the present disclosure, in addition to the scheduler 311, the fixed part 310 may further include functional modules such as a runtime environment 312, an operating system 313, and an input and output management 314, so as to ensure that the processing components realize necessary functions. For example, the RTE (Runtime Environment) is to aggregate different devices, build an installable client driver loader, and act as a proxy between the user program and the actual implementation, so that different vendors can be called smoothly. OpenCL implementation without any conflict. These modules may be implemented in a software manner, may be implemented in a programmable hardware manner, or may be implemented in a combination of software and hardware, which is not limited in the present disclosure.

According to an embodiment of the present disclosure, the operator may be an accelerator supporting PCIe. The arithmetic unit may include one or more of the following: General-purpose computing on graphics processing units (GPGPU, General-purpose computing on graphics processing units), Field Programmable Gate Array (FPGA, Field Programmable Gate Array), Field Programmable Gate Array integrated chip.

Among them, FPGA appears as a semi-custom circuit in the field of special application circuits, which not only solves the shortcomings of full-custom circuits, but also overcomes the shortcomings of the limited number of gate circuits of the original programmable logic device. One of its notable features is low power consumption. GPGPU enables stream processing components to process non-graphics data due to the powerful parallel processing capabilities and programmable pipelines of modern graphics processing components. Especially in the face of single instruction stream multiple data stream (SIMD), and the amount of data processing is far greater than the needs of data scheduling and transmission, GPGPU greatly surpasses the traditional central processing unit in performance.

According to the embodiment of the present disclosure, on the basis of the above-mentioned FPGA, the feasibility of making the FPGA into a chip is verified by simulation technology, so that the FPGA can be made into a smaller integrated chip based on the field programmable logic gate array, which can further reduce the processing time. size, weight and power consumption of the device.

According to an embodiment of the present disclosure, the FPGA in the operator is customized based on the trained neural network model. The process of customizing the FPGA includes: constructing a neural network-based health state recognition model, using ECG data as training data to train and compress the health state recognition model, and customizing a programmable logic gate array based on the compressed health state model. Among them, the step of compression can include pruning or parameter binarization, pruning reduces the association between neurons, and parameter binarization makes neurons easily implemented as hardware circuits, both of which greatly reduce the neural network. It also reduces the complexity of the deployed FPGA, which can improve computing efficiency and reduce power consumption.

According to the embodiment of the present disclosure, the weight value of the binary neural network is -1 or 1. Therefore, the multiplication calculation of the weight value between neurons can be simplified as a bit operation, and the multiplication by -1 can be realized by complementing the code. operation, which greatly improves the efficiency of model calculation.

According to the embodiment of the present disclosure, the binary neural network model can be obtained by training the following methods:

Construct a real-number neural network model, the weights and activation values of the hidden layer of the real-number neural network model are real-number data;

The real-number neural network model is trained and compressed using the training data to obtain a binary neural network model.

According to the embodiments of the present disclosure, a real-number type neural network can be trained on a platform with higher computing power first, and the real-number type neural network can include, for example, an input layer, four hidden layers and an output layer. By compressing the neural network A small binary neural network model is obtained, and then an FPGA is designed based on the compressed binary neural network model.

Get training data;

According to an embodiment of the present disclosure, acquiring training data includes:

Obtain ECG data of people with different health states;

According to the embodiments of the present disclosure, the ECG signal (including ECG signals of patients with abnormal ECG and normal people) can be denoised first. The filter eliminates myoelectric interference noise, and the IIR zero-phase shift digital filter corrects the baseline drift, etc. The embodiment of the present invention does not limit the noise removal method.

According to the embodiment of the present disclosure, after the denoising process, the denoised ECG signal can be divided into heartbeats to obtain multiple heartbeat signals of each ECG signal, and the sampling data obtained by performing data resampling on the heartbeat signals can be used as training samples , the ECG signal to which the heartbeat signal belongs is a patient with abnormal ECG or a normal person as the sample label of the training sample, and the training sample and the sample label constitute the training data.

According to the embodiment of the present disclosure, feature extraction rules, such as P-wave length, inter-PQ length, and QRS-wave length, may also be preset. This data can be used as part of the input to improve predictions.

According to an embodiment of the present disclosure, after acquiring training data, the real-number neural network model can be trained by using the training data to update the real-number weights of the real-number neural network model, and then the real-number neural network model is performed pruning, and binarizing the real weights to obtain a binary neural network model, and training the binary neural network model through the training data until convergence. In the above, the binary neural network model is obtained through one compression and trained until convergence. Alternatively, compression can be performed in multiple rounds. Each compression can prune and/or binarize some nodes, and continue training after compression. Then enter the next round of compression until both the model size and prediction accuracy meet the predetermined convergence conditions.

According to the embodiment of the present disclosure, since the FPGA is customized based on a specific neural network, it has smaller volume, lower power consumption and higher efficiency than general processing components. The comprehensive index of the processing component according to the embodiment of the present disclosure can reach the following levels:

Table 1

According to the embodiment of the present disclosure, for example, two TPS54386 dual 3A asynchronous converters may be used for the power supply interface of the processing component to ensure stable and sufficient dual power supply.

According to an embodiment of the present disclosure, the processing component may further include a power supply management unit configured to adjust the number of powered arithmetic units based on a current task load. For example, when the current task load is not less than the first threshold, all operators may be enabled; when the current task load is less than the first threshold, the number of enabled budgeters may be determined according to the current task load. The number of enabled operators may be determined according to the ratio of the current task load to the first threshold, or an interval may be defined, and the number of enabled operators may be determined according to a predetermined corresponding relationship. Through the power supply management unit, the processor of the embodiment of the present disclosure can effectively reduce its power consumption.

According to an embodiment of the present disclosure, the processing component may further include a preprocessing unit configured to perform preprocessing on the ECG signal, the preprocessing including noise processing, baseline drift processing, heart beat segmentation, feature pre-extraction, and data At least one of resampling.

According to the embodiment of the present disclosure, it is difficult to obtain a good recognition effect by directly inputting the collected raw data into the health state recognition model. The ECG signal can be preprocessed, for example, through noise processing and baseline drift processing, the information in the ECG signal can be expressed more effectively; through heartbeat segmentation, the input data is made in heartbeat units, which simplifies the processing difficulty of the model and is easy to train. ; Through feature pre-extraction, the existing experience can be used to improve the accuracy of model prediction; through data resampling, the size of input data can be reduced, the amount of calculation can be reduced, and the processing efficiency can be improved.

The power supply management unit or the preprocessing unit described in the embodiments of the present disclosure may be implemented in a software manner, or may be implemented in a programmable hardware manner. For example, the power management unit or the preprocessing unit may be set in the scheduler, and the names of these units or modules do not constitute a limitation on the unit or the module itself.

According to an embodiment of the present disclosure, the output component may have at least a first output mode and a second output mode, and the processing component is configured to:

Controlling the output device to output early warning information according to the first output mode when the health state is the first type of state;

FIG. 4 shows a schematic diagram of a master device 400 according to an embodiment of the present disclosure.

As shown in FIG. 4 , the main control device 400 may include, for example, a screen 410 , indicator lights 421 , 422 , and 423 ,

buttons

431 , 432 , and the like. Of course, the types and numbers of the above input/output units are only exemplary, more or less input/output units may be provided, or different types of input/output units may be provided, or one of the above input/output units may be omitted. one or more.

According to an embodiment of the present disclosure, the output device may have different output modes for prompting different types of health states. For example, in the first output mode, the indicator light 421 or the screen 410 can be turned on, and prompt information can also be displayed on the screen 410; in the case of). In other embodiments of the present disclosure, the first output mode may be that the indicator light 423 or the screen 410 is blinking, and the second output mode may be that the indicator light 423 or the screen 410 is always on. In still other embodiments of the present disclosure, the first output mode may be a local prompt, and the second output mode may be sending early warning information to a predetermined device to remind a predetermined contact. Of course, in other embodiments, regardless of the type of health state, early warning information can be sent to the predetermined device to remind the predetermined contact.

According to an embodiment of the present disclosure, the output device includes three-color indicator lights. For example, indicator lights 421, 422, and 423 may be green indicator lights, yellow indicator lights, and red indicator lights, respectively, or may be integrated in the same indicator light. Three different colored lamp beads. The processing component is configured to:

Some related algorithms process the ECG signal to obtain the patient's disease name, such as arrhythmia, atrial fibrillation, heart failure, sudden cardiac death, etc. If these contents are directly used for the prompts of health monitoring equipment, patients who lack the relevant knowledge cannot respond well. According to the technical solutions provided by the embodiments of the present disclosure, users can understand the monitoring results without having to master medical knowledge, and thus take appropriate countermeasures. For example, if you find that the red indicator light is on, you can call an ambulance immediately. If you find that the yellow indicator light is on, you can go to the hospital by yourself. If the green indicator light is on, you don't need to take any measures.

According to the technical solutions provided by the embodiments of the present disclosure, the health monitoring device can give an early warning to the user's health status in time; local analysis of the ECG signal through a small, portable processing component can well solve the problem of network instability. question. The user detects the ECG signal by wearing a wearable device, and the signal is given real-time warning in the local machine through the algorithm implanted in the machine. question. The mobile terminal realizes intelligent medical treatment. It can detect the physiological signals of patients in real time in an all-round way, and capture the paroxysmal abnormal heart rhythm that cannot be recorded by conventional short-term detection. mistakes.

The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. It should be understood by those skilled in the art that the scope of the invention involved in the present disclosure is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover the above technical features without departing from the inventive concept. Other technical solutions formed by any combination of its equivalent features. For example, a technical solution formed by replacing the above features with the technical features disclosed in the present disclosure (but not limited to) with similar functions.

Claims

A health monitoring device comprising:

output component;

The wearable component is woven by mixing conductive material and insulating material, the conductive material forms a plurality of electrodes on the wearable component and an ECG signal acquisition circuit is formed between the plurality of electrodes;

A processing component including a scheduler and a plurality of operators, wherein the scheduler is configured to receive the electrocardiographic signal, schedule the plurality of operators to determine health status information of the user, and based on the health status The information controls the output component to output early warning information. The operator includes a programmable logic gate array. The operator is solidly provided with a task operation unit of a binary neural network model, and a hidden layer of the binary neural network model. The weights and activation values of are binary data for determining health status information based on ECG signals; and

a battery for supplying power to the wearable component, the processing component and the output component.
The device according to claim 1, wherein the scheduler comprises a central processing unit, the arithmetic unit further comprises a general-purpose graphics processor and an integrated chip customized based on a programmable logic gate array, the scheduler and the arithmetic The device is deployed on the printed circuit board.
The apparatus of claim 1, wherein the processing component further comprises:

The power supply management unit is configured to adjust the number of powered arithmetic units based on the current task load.
The apparatus of claim 1, wherein the processing component further comprises:

The preprocessing unit is configured to perform preprocessing on the ECG signal, the preprocessing includes at least one of noise processing, baseline drift processing, heart beat segmentation, feature pre-extraction and data resampling.
The device according to claim 1, wherein the weight of the binary neural network is -1 or 1.
The device according to claim 1, wherein the binary neural network model is obtained by training the following methods:

Constructing a real-number neural network model, the weights and activation values of the hidden layer of the real-number type neural network model are real-number data;

The real number type neural network model is trained and compressed using the training data to obtain a binary neural network model.
equipment according to claim 6, is characterized in that, described using training data training and compressing described real number type neural network model, obtains binary neural network model, including:

Get training data;

The training operation and the compression operation are alternately performed until the convergence conditions are met, and a binary neural network model is obtained, wherein in the training operation, the weights in the model are updated by the training data; in the compression operation, the weights in the model are updated by cutting The size of the branch and/or binarized compressed model.
The device according to claim 7, wherein the acquiring training data comprises:

Obtain ECG data of people with different health states;

Performing preprocessing on the ECG data to obtain training samples, where the preprocessing includes at least one of noise processing, baseline drift processing, heart beat segmentation, feature pre-extraction, and data resampling;

The training samples are marked to obtain sample labels, and the sample labels represent different health states corresponding to the ECG data, and the training samples and the sample labels are determined as training data.
The apparatus of any one of claims 1-8, wherein the output component has a first output mode and a second output mode, and the processing component is configured to:

In the case that the health state is the first type of state, controlling the output device to output early warning information according to the first output mode;

When the state of health is the state of the second type, the output device is controlled to output early warning information according to the second output mode.
10. The apparatus of claim 9, wherein the output device includes a three-color indicator light, and the processing component is configured to:

In the case that the health state is a normal state, control the green indicator light to light;

In the case that the health state is an abnormal state, control the yellow indicator light to light;

When the health state is an emergency state, the red indicator light is controlled to light up.