Modular user health status monitoring apparatus, method, and system

Abstract

Disclosed in the present application are a modular user health status monitoring apparatus, method, and system, relating to the technical field of health status monitoring. The apparatus comprises: a plurality of functional components detachably connected end-to-end in sequence to form a wristband or finger ring band; a sensing and detection module located in any one of the functional components; and a main control module located in any one of the functional components and connected to the sensing and detection module. The modules in the present application can be conveniently disassembled and freely combined, enabling different measurement functions to be very conveniently achieved.

Description

Modular user health state monitoring device, method and system

[0001] The present application claims priority to the Chinese patent application No. 202410417852.4, filed on April 8, 2024, and entitled "Modular user health state monitoring device, method and system", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the technical field of health state monitoring, in particular to a modular user health state monitoring device, method and system. BACKGROUND

[0003] At present, various intelligent wearable devices have emerged, including human health, virtual reality and augmented reality, information and information. Intelligent wearable devices are constantly developing towards more practical, more intelligent and more convenient, bringing convenience to people's lives. However, the accuracy of the monitoring data of the wearable devices needs to be further improved, and for different application scenarios, a variety of wearable devices are usually needed to meet different application requirements. In the future, wearable devices will adapt to more application scenarios, including mobile medical care, health field, etc. User experience and endurance size will be continuously optimized.

[0004] Therefore, how to provide a user health state monitoring device with each module being detachable and arbitrarily combinable to realize different measurement functions has become a problem to be solved in the field.

[0005] SUMMARY

[0006] The purpose of the present application is to provide a modular user health state monitoring device, method and system, each module of which can be easily detached and combined arbitrarily to realize different measurement functions.

[0007] To achieve the above purpose, the present application provides the following solutions.

[0008] In a first aspect, the present application provides a modular user health state monitoring device, comprising:

[0009] A plurality of functional components, which are sequentially detachably connected at the head and tail to form a bracelet or a ring band;

[0010] A sensing and detecting module located in any of the functional components;

[0011] A master control module located in any of the functional components and connected with the sensing and detecting module.

[0012] In an embodiment, it further comprises a storage module located in any of the functional components and connected with the master control module.

[0013] In an embodiment, further comprising: a power module located in any of the functional components and connected with the sensing detection module and the master control module.

[0014] In an embodiment, further comprising: a display module located in any of the functional components and connected with the master control module.

[0015] In an embodiment, further comprising: a data communication module located in any of the functional components and connected with the master control module.

[0016] In an embodiment, the sensing detection module comprises: a MEMS gyroscope, an acceleration sensor, a heat flux sensor, a thermal resistance sensor, an electrochemical / fluorescent reaction sensor, a high-frequency electric excitation electrode, a weak electric detection electrode, an ink printing sensor, a photoelectric sensor, and a laser sensor.

[0017] In a second aspect, based on the above-mentioned device in the present application, the present application further provides a modular user health state monitoring method, which is applied to the device of the first aspect, and the monitoring method comprises: acquiring a PPG signal; calculating blood perfusion based on a decision algorithm using the PPG signal; acquiring a surface temperature of a local tissue; and calculating a deep temperature of a to-be-measured part based on the blood perfusion and the surface temperature of the local tissue.

[0018] In an embodiment, further comprising: calculating a blood glucose value of a user, specifically comprising:

[0019] collecting blood glucose monitoring data; the blood glucose monitoring data comprises: a timestamp and a corresponding blood glucose value, a multi-wavelength PPG original signal, a deep temperature Tc of a measurement site, and a Spo2 of the to-be-measured part;

[0020] preprocessing the blood glucose monitoring data; constructing a plurality of deep learning sub-models; training the plurality of deep learning sub-models based on the preprocessed blood glucose monitoring data; performing weighted fusion on the plurality of trained deep learning sub-models using a model fusion algorithm to obtain a final blood glucose prediction model; and predicting blood glucose of a user based on the blood glucose prediction model.

[0021] In an embodiment, the calculation of the deep temperature of the to-be-measured part based on the blood perfusion and the surface temperature of the local tissue specifically adopts the following formula:

[0022] wherein, T c represents the deep temperature of the to-be-measured part, T0 represents the surface temperature measured by the thermal resistance sensor, q1 is the value measured by the heat flux sensor, k is the thermal conductivity of the tissue, λ is the tissue density, ω is the blood perfusion, k1 is the thermal resistance influence calibration coefficient, and R is the influence of the contact thermal resistance.

[0023] In a third aspect, the present application provides a computer system, comprising: a memory, a processor to store a computer program on the memory and run the computer program on the processor, and the processor executes the computer program to implement the steps of the method of the second aspect.

[0024] According to the specific embodiments provided by the present application, the following technical effects are disclosed:

[0025] The present application focuses on a modular user health state monitoring device, method and system. The device comprises a plurality of functional components, a sensing detection module and a master control module. The plurality of functional components are sequentially and detachably connected to form a bracelet or a ring band. The sensing detection module and the master control module are located in any one of the functional components. Each module can be easily detached and combined to realize different measurement functions.

[0026] In addition, a power module is also included in any one of the functional components. The existing wearable devices have limited battery capacity, and generally need to be removed for measurement. The modular ring-shaped monitoring device designed in the present application can directly replace the power module without the need to remove the entire device for charging, thereby improving the continuity of monitoring. Moreover, the connection between the modules is elastic, i.e., each module has a certain stretching performance, which can extend up and down, left and right in three dimensions to adapt to different sizes, making it more convenient to wear and achieving comfortable, high customization and high accuracy monitoring.

[0027] Drawings

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor based on these drawings.

[0029] FIG. 1 is a structural schematic diagram of a modular user health state monitoring device according to one or more embodiments of the present application;

[0030] FIG. 2 is a front view of a modular user health state monitoring device according to one or more embodiments of the present application. DETAILED DESCRIPTION

[0031] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below, obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0032] The purpose of the present application is to provide a modular user health status monitoring device, method and system, each module can be easily disassembled, and different measurement functions can be easily realized.

[0033] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0034] Embodiment one

[0035] Fig. 1 is a schematic diagram of the structure of the modular user health status monitoring device provided by the present application according to one or more embodiments. The monitoring device in the present application comprises: a plurality of functional components, a sensing detection module, a main control module, a storage module, a power module, a display module and a data communication module. The sensing detection module, the storage module, the power module, the display module and the data communication module are all connected with the main control module.

[0036] Among them, the plurality of functional components are connected in series at the head and tail to form a bracelet or a ring band, as shown in Fig. 1, the functional components shown in the present embodiment are four, namely, the first functional component 1, the second functional component 2, the third functional component 3 and the fourth functional component 4, the number of functional components is not limited in the present application, and in actual application process, those skilled in the art can adjust the number according to the demand.

[0037] Specifically, each functional component is connected by elasticity, so that the bracelet or ring has certain stretchability, which is convenient to wear, for example, the internal elastic component can be a spring, which realizes the wearing of different sizes of fingers / wrists through the spring.

[0038] As shown in Fig. 2, the modules are connected and fixed by detachable pins, the pins provide a certain length of deformation space which can be stretched, so that the ring structure can extend in the direction of the longer circumference. Among them, the second functional component and the fourth functional component are each provided with a spring inside the two sides, the deformation length of the spring is slightly smaller than the deformation length of the pin which can be stretched, and provides a force opposite to the deformation direction of the ring structure, so that different sizes of fingers / wrists can be well contacted and worn, and will not fall off. The deformation space of each functional component is provided with a conductive contact / track, which can provide electrical connection of the ring module, including power connection and signal connection between the sensing module and the mainboard.

[0039] In addition, the functional components can be fixed through a lockable structure. The lockable structure provides a deformation space with a certain length that can be stretched, so that the ring-shaped structure can extend in multiple directions in which the circumference is lengthened. The overlapping region of the functional components is provided with a spring, which provides a force to prevent the circumference of the ring-shaped structure from being lengthened, so that fingers / wrists of different sizes can maintain good contact with the wearable device and are not easily detached. The deformation space of each functional component is provided with a flexible signal connection device, such as an electronic wire, which can provide electrical connection of the ring-shaped module, including power connection and signal connection between the sensing module and the mainboard.

[0040] The power module is arranged in the first functional component 1 and the third functional component 3, that is, the modular user health state monitoring device in the embodiment includes two power sources and is detachable.

[0041] The sensing and detecting module and the main control module are arranged in the second functional component 2.

[0042] The storage module, the display module, and the data communication module are arranged in the fourth functional component.

[0043] The arrangement of the modules is only an exemplary embodiment in the embodiment, and the positions of the modules can be adjusted according to actual application.

[0044] Specifically, the sensing and detecting module includes a MEMS gyroscope, an acceleration sensor, a heat flux sensor, a thermal resistance sensor, an electrochemical / fluorescent reaction sensor, a high-frequency electric excitation electrode, a weak electric detection electrode, an ink printing sensor, a photoelectric sensor, and a laser sensor.

[0045] Embodiment two

[0046] For the modular user health state monitoring device provided in embodiment one, another embodiment of the present application provides a modular user health state monitoring method, which includes the following steps.

[0047] Step 1: Obtain a PPG signal.

[0048] Specifically, the PPG signal is obtained through a photoelectric sensor, the sampling frequency is not less than 100 HZ, and the analysis time window is 10 s.

[0049] Step 2: Calculate blood perfusion based on a decision algorithm using the PPG signal.

[0050] Specifically, the blood oxygen saturation Spo2 of the measured part is calculated according to the diffuse reflection principle from the multi-wavelength PPG signal.

[0051] Step 3: Obtain the surface temperature of the local tissue.

[0052] Based on step 2, the surface temperature of the local tissue is measured by a thermal resistance sensor.

[0053] Step 4: Calculate the deep temperature of the measured part based on the blood perfusion and the surface temperature of the local tissue.

[0054] Specifically, the following formula is used:

[0055] Among them, T c represents the deep temperature of the measured part, T0 represents the surface temperature measured by the thermal resistance sensor, q1 is the value measured by the heat flux sensor, k is the tissue thermal conductivity, λ is the tissue density, ω is the blood perfusion, k1 is the thermal resistance influence calibration coefficient, and R is the influence of contact thermal resistance.

[0056] The process of deep temperature transfer to the outside mainly involves three parts of heat transfer analysis: heat transfer analysis within the organism, thermal conductivity at the interface between the organism and the sensor, and acquisition of heat flow into the sensor.

[0057] For heat transfer analysis in organisms, the most widely used biological heat transfer equation is the Penne equation (see Formula 2), which takes into account heat conduction, metabolic heat, and simplifies the energy carried in the blood into an additional heat source term. The Pennes equation is simplified to:

[0058] Among them, ρ t is tissue density, C b is the tissue specific heat capacity, T is the tissue temperature, t is the time, k is the tissue thermal conductivity, ρ b is the blood density, C t is the specific heat capacity of blood, ω b is the blood perfusion rate, T a For blood temperature, this equation accounts for heat conduction and heat storage in living tissue, and simulates biological tissue using solid media with density, heat capacity, and thermal conductivity. By continuously improving and refining this model, we can better understand the temperature distribution patterns in living organisms and provide more effective tools and methods for biomedical research and applications.

[0059] Assume that the range of the tissue is semi-infinite and the temperature at the far boundary is the core temperature T c And it is stable. At this time, the boundary conditions of the deep tissue are given as follows:

[0060] At the biosensor interface (x = 0), the heat flux must be matched by the thermal contact resistance due to factors such as dry skin and deep wrinkles that may cause air gaps. This, combined with the surface temperature (T0) measured by the sensor, gives the second boundary condition:

[0061] where R is the contact thermal resistance at the interface, assuming that the heat flow into the sensor is not lost in the process of contact, so the heat flow at the interface q0 can be obtained by the sensor and the temperature inside the sensor T0.

[0062] From the above analysis, the simplified Pennes equation and the boundary conditions can be solved as follows:

[0063] where ω b ρ b C b The perfusion rate, density and specific heat capacity of the blood in the tissue, respectively, k is the thermal conductivity of the tissue, and the expression of α is The subscripts skin and c represent the skin and the core, respectively.

[0064] The solution can obtain the tissue temperature distribution under the stable condition as follows:

[0065] The corresponding heat flow at the skin is:

[0066] This value can be obtained by the sensor, and the core temperature T c The relationship between the contact thermal resistance R and the sensor temperature T0 is:

[0067] T c is the deep temperature to be measured, T0 is the surface temperature measured by the thermistor sensor, q1 is the value measured by the heat flux sensor, k is the thermal conductivity of the tissue, λ is the constant related to the density and specific heat capacity of the tissue, ω b is the blood perfusion, k1 is the calibration coefficient affected by the thermal resistance, and R is the influence of the contact thermal resistance. It can be seen that the second term of the formula is related to the blood perfusion, which can reflect the body temperature regulation function inside the human tissue, and the third term is related to the contact thermal resistance, which can express the influence of the contact interface on heat transfer.

[0068] Step 5: calculating the blood glucose value of the user, specifically including:

[0069] S5.1: collecting blood glucose monitoring data as a training set; the blood glucose monitoring data includes: time stamp and corresponding blood glucose value, multi-wavelength PPG original signal, measured part deep temperature Tc, and Spo2 of the part to be measured.

[0070] S5.2: preprocessing the blood glucose monitoring data.

[0071] First, handle missing values, outliers and noise; second, extract useful features from raw data, including time, diet, exercise, medication use, etc. in addition to the training set data mentioned above; finally, create new features such as blood glucose trends, change rates, etc.

[0072] S5.3: Construct multiple deep learning sub-models.

[0073] S5.4: Train the multiple deep learning sub-models based on the pre-processed blood glucose monitoring data.

[0074] The deep learning sub-models can be selected from LSTM, GAN, Bi-GRU and attention mechanism, etc. The training data set is used to train the model, optimize the model parameters, cross-validation and adjust the hyperparameters to avoid overfitting.

[0075] S5.5: Use model fusion algorithm to weight and fuse multiple trained deep learning sub-models to get the final blood glucose prediction model.

[0076] Specifically, use model fusion techniques such as weighted average, voting, etc. to fuse the prediction results of different models to improve overall performance.

[0077] S5.6: Predict the user's blood glucose based on the blood glucose prediction model.

[0078] Use the trained blood glucose prediction model to predict new blood glucose monitoring data. Output the predicted blood glucose value and generate the corresponding prompt information according to the threshold, such as hypoglycemia, normal, hyperglycemia, etc.

[0079] Embodiment three

[0080] A computer device comprising: a memory, a processor to store a computer program on the memory and executable on the processor, the processor executing the computer program to implement the steps of the modular user health state monitoring method in embodiment two.

[0081] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. The non-volatile memory can include read-only memory (Read-Only Memory, ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric memory (Ferroelectric Random Access Memory, FRAM), phase change memory (Phase Change Memory, PCM), graphene memory, etc. The volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc. The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0082] Any combination of the technical features of the above embodiments can be made, and in order to make the description simple, not all possible combinations of the technical features in the above embodiments are described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0083] The principles and implementation modes of the present application are described by applying specific examples herein, and the above description of the embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A modular user health status monitoring device, characterized in that: include: A plurality of functional components, wherein the plurality of functional components are detachably connected end to end to form a wristband or a finger ring band; A sensing detection module, located in any of the functional components; The main control module is located in any of the functional components and is connected to the sensor detection module.

2. The modular user health status monitoring device according to claim 1, characterized in that: Also includes: The storage module is located in any of the functional components and is connected to the main control module.

3. The modular user health status monitoring device according to claim 1, characterized in that: Also includes: The power supply module is located in any of the functional components and is connected to the sensing detection module and the main control module.

4. The modular user health status monitoring device according to claim 1, characterized in that: Also includes: The display module is located in any of the functional components and is connected to the main control module.

5. The modular user health status monitoring device according to claim 1, characterized in that: Also includes: The data communication module is located in any of the functional components and is connected to the main control module.

6. The modular user health status monitoring device according to claim 1, characterized in that: The sensing detection module includes: a MEMS gyroscope, an acceleration sensor, a heat flux sensor, a thermal resistance sensor, an electrochemical / fluorescence reaction sensor, a high-frequency electrical excitation electrode, a weak current detection electrode, an ink printing sensor, a photoelectric sensor, and a laser sensor.

7. A modular user health status monitoring method, characterized in that: The monitoring method is applied to the device according to any one of claims 1 to 6, and the monitoring method includes: Get PPG signal; calculating blood perfusion based on a decision algorithm using the PPG signal; Obtain the surface temperature of local tissue; The deep temperature of the measured part is calculated based on the blood perfusion and the surface temperature of the local tissue.

8. The modular user health status monitoring method according to claim 7, characterized in that: Also includes: Calculate the user's blood sugar level, including: Collect blood glucose monitoring data; the blood glucose monitoring data includes: timestamp and corresponding blood glucose value As well as multi-wavelength PPG raw signals, deep temperature Tc of the measurement site, and Spo2 of the measured site; Preprocessing the blood glucose monitoring data; Build multiple deep learning sub-models; Training the multiple deep learning sub-models based on the preprocessed blood glucose monitoring data; The model fusion algorithm is used to perform weighted fusion on multiple trained deep learning sub-models to obtain the final blood glucose prediction model; The user's blood sugar is predicted based on the blood sugar prediction model.

9. The modular user health status monitoring method according to claim 7, characterized in that: The deep temperature of the measured part is calculated based on the blood perfusion and the surface temperature of the local tissue using the following formula: Among them, T c represents the deep temperature of the measured part, T0 represents the surface temperature measured by the thermal resistance sensor, q1 is the value measured by the heat flux sensor, k is the tissue thermal conductivity, λ is the tissue density, ω is the blood perfusion, k1 is the thermal resistance influence calibration coefficient, and R is the influence of contact thermal resistance.

10. A computer system comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method according to any one of claims 7 to 9.

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