Noninvasive blood glucose measurement method and device, electronic equipment and storage medium
By emitting a light source and combining it with biometric signals to correct blood glucose prediction information, the accuracy problem of non-invasive blood glucose measurement has been solved, achieving non-invasive, rapid, and accurate blood glucose measurement.
Patent Information
- Application Number
- CN202411312155.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-19
- Publication Date
- 2026-03-20
Smart Images
Figure CN121694745A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of optical blood glucose measurement technology, and in particular to a non-invasive blood glucose measurement method, device, electronic device and storage medium. Background Technology
[0002] Optical blood glucose measurement methods use light as an information carrier. Typically, a beam of light is focused onto the human body. By utilizing the fact that the intensity, phase, polarization angle, frequency, and scattering coefficient of the target tissue of the transmitted light are closely related to blood glucose concentration, changes in these information are extracted to indirectly measure blood glucose concentration. Summary of the Invention
[0003] This disclosure provides a non-invasive method, device, electronic device, computer-readable storage medium, and computer program product for measuring blood glucose, to at least solve the problem of the inability to quickly and accurately measure blood glucose data in related technologies. The technical solution of this disclosure is as follows:
[0004] According to a first aspect of the present disclosure, a non-invasive blood glucose measurement method is provided, comprising: emitting a target light source at a target wavelength and receiving a scattering signal from the target light source, wherein the target wavelength is a wavelength that can be absorbed by glucose; determining the absorbance corresponding to the scattering signal and determining blood glucose prediction information of the measurement subject based on the absorbance.
[0005] In one embodiment of this disclosure, determining the absorbance corresponding to the scattering signal includes: determining a first light intensity value of the scattering signal and a second light intensity value of the target light source; and determining the absorbance corresponding to the scattering signal based on the first light intensity value and the second light intensity value.
[0006] In one embodiment of this disclosure, determining the first light intensity value of the scattered signal includes: determining the first light intensity value of the scattered signal based on the current value output by the photodiode.
[0007] In one embodiment of this disclosure, the blood glucose prediction information includes at least one of the predicted blood glucose concentration of the measurement object and a first predicted blood glucose range. After determining the blood glucose prediction information of the measurement object based on the absorbance, the method further includes: acquiring the biometric signal of the measurement object; and correcting the blood glucose prediction information based on the biometric signal to obtain the target blood glucose prediction information of the measurement object, wherein the target blood glucose prediction information includes at least one of the target predicted blood glucose concentration and a target blood glucose range of the measurement object.
[0008] In one embodiment of this disclosure, the step of correcting the blood glucose prediction information based on the biosignal signal to obtain the target blood glucose prediction information of the measurement object includes: acquiring the blood oxygen data of the measurement object based on the biosignal signal, and determining a first metabolic parameter of the measurement object based on the blood oxygen data; determining a second predicted blood glucose range of the measurement object based on the first metabolic parameter of the measurement object; and correcting the blood glucose prediction information based on the second predicted blood glucose range of the measurement object to obtain the target blood glucose prediction information.
[0009] In one embodiment of this disclosure, determining the first metabolic parameter of the measurement object based on the blood oxygen data includes: obtaining the ratio of oxyhemoglobin to deoxyhemoglobin content of the measurement object based on the blood oxygen data; determining the amplitude / phase difference of the measurement object in medical imaging based on the oxyhemoglobin / deoxyhemoglobin ratio; and determining the first metabolic parameter of the measurement object based on the amplitude / phase difference.
[0010] In one embodiment of this disclosure, the step of correcting the blood glucose prediction information based on the biometric signal to obtain the target blood glucose prediction information of the measurement subject includes: acquiring an electrocardiogram (ECG) signal and an electrical conductance analysis (EDA) signal; determining a third predicted blood glucose range for the measurement subject based on the ECG signal and the EDA signal; and correcting the blood glucose prediction information based on the third predicted blood glucose range to obtain the target blood glucose prediction information.
[0011] In one embodiment of this disclosure, determining the third estimated blood glucose range of the measurement subject based on the ECG signal and EDA signal includes: extracting waveform features from the ECG signal and EDA signal, and determining the target neural activity of the measurement subject based on the extracted waveform features; and determining the third estimated blood glucose range of the measurement subject by querying a first reference relationship between the neural activity and blood glucose concentration based on the target neural activity.
[0012] In one embodiment of this disclosure, the step of correcting the blood glucose prediction information based on the biometric signal to obtain the target blood glucose prediction information of the measurement object includes: acquiring the heart rate and blood oxygen data of the measurement object based on the biometric signal; acquiring the temperature information of the measurement object; and correcting the blood glucose prediction information based on the heart rate, blood oxygen data and the temperature information to obtain the target blood glucose prediction information.
[0013] In one embodiment of this disclosure, the step of correcting the blood glucose prediction information based on the heart rate, blood oxygen data, and temperature information to obtain the target blood glucose prediction information includes: determining a second metabolic parameter of the measurement object based on the heart rate, blood oxygen data, and temperature information; determining a fourth predicted blood glucose range of the measurement object based on the second metabolic parameter of the measurement object; and correcting the blood glucose prediction information based on the fourth predicted blood glucose range to obtain the target blood glucose prediction information.
[0014] In one embodiment of this disclosure, obtaining the temperature information of the measurement object includes: obtaining the skin temperature of the measurement object; obtaining the heat flow or ambient temperature of the measurement object; and predicting the core body temperature of the measurement object based on the heat flow or ambient temperature of the measurement object.
[0015] In one embodiment of this disclosure, the process of obtaining the heat flow of the object being measured includes: obtaining the temperature difference between a first temperature sensor and a second temperature sensor, and obtaining the heat flow of the object being measured based on the temperature difference, wherein one of the first temperature sensor and the second temperature sensor is used to measure the skin temperature.
[0016] In one embodiment of this disclosure, when the biometric signals of the measurement object are N sets, N is a natural number greater than 1, the step of correcting the blood glucose prediction information based on the biometric signals to obtain the target blood glucose prediction information of the measurement object includes: determining the predicted blood glucose intervals of at least two sets of biometric signals among the N sets of biometric signals, and correcting the blood glucose prediction information by combining the predicted blood glucose intervals of the at least two sets of biometric signals to obtain the target blood glucose prediction information.
[0017] According to a second aspect of the present disclosure, a non-invasive blood glucose measurement device is provided, comprising: a transceiver module for transmitting a target light source at a target wavelength and receiving a scattering signal from the target light source, wherein the target wavelength is a wavelength that can be absorbed by glucose; and a determination module for determining the absorbance corresponding to the scattering signal and determining blood glucose prediction information of the measurement subject based on the absorbance.
[0018] In one embodiment of this disclosure, the determining module is further configured to: determine a first light intensity value of the scattered signal and a second light intensity value of the target light source; and determine the absorbance corresponding to the scattered signal based on the first light intensity value and the second light intensity value.
[0019] In one embodiment of this disclosure, the determining module is further configured to: determine a first light intensity value of the scattered signal based on the current value output by the photodiode.
[0020] In one embodiment of this disclosure, the determining module is further configured to: acquire the biometric signal of the measurement object; and correct the blood glucose prediction information based on the biometric signal to obtain the target blood glucose prediction information of the measurement object, wherein the target blood glucose prediction information includes at least one of the target predicted blood glucose concentration and the target predicted blood glucose range of the measurement object.
[0021] In one embodiment of this disclosure, the determining module is further configured to: acquire blood oxygen data of the measurement object based on the biosignal signal, and determine a first metabolic parameter of the measurement object based on the blood oxygen data; determine a second estimated blood glucose range of the measurement object based on the first metabolic parameter of the measurement object; and correct the blood glucose prediction information based on the second estimated blood glucose range of the measurement object to obtain the target blood glucose prediction information.
[0022] In one embodiment of this disclosure, the determining module is further configured to: obtain the ratio of oxyhemoglobin to deoxyhemoglobin content of the measured object based on the blood oxygen data; determine the amplitude / phase difference of the measured object in medical imaging based on the ratio of oxyhemoglobin to deoxyhemoglobin; and determine the first metabolic parameter of the measured object based on the amplitude / phase difference.
[0023] In one embodiment of this disclosure, the determining module is further configured to: acquire an electrocardiogram (ECG) signal and an electrical conductance analysis (EDA) signal; determine a third estimated blood glucose range for the measurement subject based on the ECG signal and the EDA signal; and correct the blood glucose prediction information based on the third estimated blood glucose range to obtain the target blood glucose prediction information.
[0024] In one embodiment of this disclosure, the determining module is further configured to: extract waveform features from the ECG signal and EDA signal, and determine the target neural activity of the measurement object based on the extracted waveform features; and, based on the target neural activity, query a first reference relationship between neural activity and blood glucose concentration to determine a third estimated blood glucose range for the measurement object.
[0025] In one embodiment of this disclosure, the determining module is further configured to: acquire the heart rate and blood oxygen data of the measurement object based on the biometric signal; acquire the temperature information of the measurement object; and correct the blood glucose prediction information according to the heart rate, blood oxygen data and the temperature information to obtain the target blood glucose prediction information.
[0026] In one embodiment of this disclosure, the determining module is further configured to: determine a second metabolic parameter of the measurement object based on the heart rate and blood oxygen data and the temperature information; determine a fourth estimated blood glucose range of the measurement object based on the second metabolic parameter of the measurement object; and correct the blood glucose prediction information based on the fourth estimated blood glucose range to obtain the target blood glucose prediction information.
[0027] In one embodiment of this disclosure, the determining module is further configured to: acquire the skin temperature of the measurement object; acquire the heat flow or ambient temperature of the measurement object, and predict the core body temperature of the measurement object based on the heat flow or ambient temperature of the measurement object.
[0028] In one embodiment of this disclosure, the determining module is further configured to: acquire the temperature difference between the first temperature sensor and the second temperature sensor, and acquire the heat flow of the object being measured based on the temperature difference, wherein one of the first temperature sensor and the second temperature sensor is used to measure the skin temperature.
[0029] In one embodiment of this disclosure, the determining module is further configured to: determine the estimated blood glucose range of at least two sets of biosignal signals among N sets of biosignal signals, and combine the estimated blood glucose range of the at least two sets of biosignal signals to correct the blood glucose prediction information to obtain the target blood glucose prediction information.
[0030] According to a third aspect of the present disclosure, an electronic device is provided, including a processor; a memory for storing processor-executable instructions; wherein the processor is configured to implement the steps of the method described in the first aspect of the present disclosure.
[0031] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the steps of the method described in the first aspect of the present disclosure.
[0032] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, characterized in that, when executed by a processor of an electronic device, the computer program implements the steps of the method as described in the first aspect of the present disclosure.
[0033] The technical solution provided by the embodiments of this disclosure offers at least the following beneficial effects: It sends a target light source of a target wavelength to the measurement object and receives the scattered signal scattered by the measurement object. Based on the absorbance of the scattered signal, the blood glucose level of the measurement object is estimated to obtain the estimated blood glucose concentration and the first estimated blood glucose range within which the estimated blood glucose concentration falls. The estimated blood glucose concentration and the first estimated blood glucose range are then fed back to the measurement object as blood glucose prediction information. This disclosure uses a light source with a stronger correlation to human blood glucose levels for blood glucose prediction, providing a direct and accurate basis for predicting changes in human blood glucose levels, thereby improving the accuracy of blood glucose prediction. Furthermore, the convenience of blood glucose measurement is improved by using wearable devices to measure blood glucose.
[0034] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0035] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.
[0036] Figure 1 This is a flowchart illustrating a non-invasive blood glucose measurement method according to an exemplary embodiment;
[0037] Figure 2 This is an example diagram of a wearable device according to an exemplary embodiment;
[0038] Figure 3 This is a flowchart illustrating a non-invasive blood glucose measurement method according to another exemplary embodiment;
[0039] Figure 4 This is a flowchart illustrating the process of correcting blood glucose prediction information in a non-invasive blood glucose measurement method according to an exemplary embodiment;
[0040] Figure 5 This is a flowchart illustrating the process of correcting blood glucose prediction information in a non-invasive blood glucose measurement method according to another exemplary embodiment;
[0041] Figure 6 This is a flowchart illustrating the process of correcting blood glucose prediction information in a non-invasive blood glucose measurement method according to another exemplary embodiment;
[0042] Figure 7 This is a block diagram illustrating a non-invasive blood glucose measurement device according to an exemplary embodiment;
[0043] Figure 8 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation
[0044] To enable those skilled in the art to better understand the technical solutions of this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings.
[0045] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.
[0046] The acquisition, storage, use, and processing of data in this disclosed technical solution all comply with the relevant laws and regulations.
[0047] The following description, with reference to the accompanying drawings, describes a non-invasive blood glucose measurement method and apparatus according to embodiments of the present disclosure.
[0048] Figure 1 This is a flowchart illustrating a non-invasive blood glucose measurement method according to an exemplary embodiment, such as... Figure 1 As shown, the non-invasive blood glucose measurement method of this disclosure includes the following steps:
[0049] S101 emits a target light source at the target wavelength and receives the scattered signal from the target light source. The target wavelength is a wavelength that can be absorbed by glucose.
[0050] It should be noted that the non-invasive blood glucose measurement method of this disclosure is executed by an electronic device, which is a wearable device. Wearable devices may include wrist-worn devices (such as smartwatches and smart bracelets), head-worn devices, foot-worn devices, etc. The non-invasive blood glucose measurement method of this disclosure can be executed by the non-invasive blood glucose measurement device of this disclosure, and the non-invasive blood glucose measurement device of this disclosure can be configured in any electronic device to execute the non-invasive blood glucose measurement method of this disclosure.
[0051] In some implementations, wearable devices emit a target light source of a target wavelength towards the object being measured and receive the scattered signal from the target light source. The target wavelength is one that can be absorbed by glucose. For example, the target light source of the target wavelength can be emitted by the optical measurement module within the wearable device. This target light source can be a visible light source, a near-infrared source, or a short-wave infrared source. In other words, it can emit a visible light source, a near-infrared source, or a short-wave infrared source of the target wavelength.
[0052] For example, the typical wavelengths of visible light sources are located in the green (500-565nm) and red (625-750nm) bands; the wavelengths of near-infrared sources are in the 750-1000nm band, with a typical value of 940nm; the wavelengths of short-wave infrared sources are in the 1000-2500nm band, with typical values of one or more of 1050nm, 1300nm, and 1450nm, and other wavelengths in this band are also possible.
[0053] It is understandable that when a wearable device emits a target light source towards the object being measured, the object being measured, i.e., the human body, can absorb and scatter the target light source, thereby obtaining the scattered signal of the target light source, which the wearable device can then receive.
[0054] like Figure 2 As shown, Figure 2 This is an example of a wearable device. Figure 2 The wearable device in the design is a smartwatch. It sends a target light source to the wrist, which absorbs and scatters the light. The smartwatch can then receive the scattered signals from the wrist.
[0055] S102, determine the absorbance corresponding to the scattered signal, and determine the blood glucose prediction information of the measured object based on the absorbance.
[0056] It is understandable that when a target light source shines on human tissue, components in the blood, such as glucose, absorb the light of the target wavelength, causing a change in the intensity of the scattered light. In other words, the blood glucose level of the subject can be estimated based on the absorbance of the scattered signal, thus obtaining the subject's blood glucose prediction information.
[0057] It is understood that absorbance refers to the logarithm (base 10) of the ratio of the intensity of incident light before the light source passes through the solution or substance to the intensity of transmitted light after the light source passes through the solution or substance. Absorbance is related to both the intensity of incident light and the intensity of transmitted light. In other words, in this disclosure, the absorbance corresponding to the scattered signal can be determined by calculating the logarithm of the ratio of the intensity of the target light source to the intensity of the scattered signal.
[0058] Furthermore, based on the pre-established mapping relationship between absorbance and blood glucose concentration, the blood glucose prediction information for the measured object can be determined. This prediction information can include the predicted blood glucose concentration and a first predicted blood glucose range. The first predicted blood glucose range includes the predicted blood glucose concentration. In other words, the predicted blood glucose concentration is determined by querying the aforementioned mapping relationship based on absorbance, and an offset is set for the blood glucose concentration. The first predicted blood glucose range is obtained by adding or subtracting the offset from the predicted blood glucose concentration.
[0059] Optionally, the offset of blood glucose concentration can be set according to the accuracy of the mapping relationship. For example, if the accuracy of the mapping relationship is high, the offset of blood glucose concentration can be relatively small; if the accuracy of the mapping relationship is low, the offset of blood glucose concentration can be relatively large. For example, if the estimated blood glucose concentration is 5 mmol / L, the first estimated blood glucose range is 4.8 mmol / L-5.2 mmol / L.
[0060] The non-invasive blood glucose measurement method provided in this disclosure involves sending a target light source of a target wavelength to the measurement subject and receiving the scattered signal from the subject. The absorbance of the scattered signal is used to estimate the blood glucose level of the measurement subject, resulting in an estimated blood glucose concentration and a first estimated blood glucose range. This estimated blood glucose concentration and the first estimated blood glucose range are then fed back to the measurement subject as blood glucose prediction information. This disclosure uses a light source with a stronger correlation to human blood glucose levels for blood glucose prediction, providing a direct and accurate basis for predicting changes in human blood glucose levels, thereby improving the accuracy of blood glucose prediction. The use of wearable devices to measure blood glucose improves the convenience of blood glucose measurement.
[0061] Figure 3 This is a flowchart illustrating a non-invasive blood glucose measurement method according to an exemplary embodiment, such as... Figure 3 As shown, the non-invasive blood glucose measurement method of this disclosure includes the following steps:
[0062] S301 emits a target light source at the target wavelength and receives the scattered signal from the target light source. The target wavelength is a wavelength that can be absorbed by glucose.
[0063] For details regarding step S301, please refer to the above embodiments, which will not be repeated here.
[0064] S302, determine the first light intensity value of the scattered signal and the second light intensity value of the target light source.
[0065] In some implementations, the first light intensity value of the scattered signal and the second light intensity value of the target light source can be determined based on the photodiode within the wearable device itself. It is understood that when a photodiode is illuminated, its internal semiconductor material absorbs photons and releases electrons, forming a photocurrent. The magnitude of this photocurrent is directly proportional to the intensity of the incident light (i.e., the light's strength or brightness). In other words, the stronger the light, the greater the current generated by the photodiode; the weaker the light, the smaller the current.
[0066] Optionally, the photodiode can receive the scattered signal, convert it into an electrical signal, and measure the current value of the electrical signal to obtain the first light intensity value of the scattered signal. In other words, the first light intensity value of the scattered signal can be determined based on the current value output by the photodiode.
[0067] Optionally, a second light intensity value of the target light source can be determined by monitoring the optical emitting device in the wearable device. The second light intensity value can be obtained by measuring the current output by the optical emitting device.
[0068] S303, determine the absorbance corresponding to the scattered signal based on the first light intensity value and the second light intensity value.
[0069] In some implementations, the ratio of the first light intensity value to the second light intensity value can be calculated, and then the absorbance corresponding to the scattered signal can be determined based on this ratio. For example, if the first light intensity value is I1 and the second light intensity value is I0, then the absorbance is lg(I0 / I1).
[0070] S304 determines the blood glucose prediction information of the measured subject based on absorbance.
[0071] For details regarding step S304, please refer to the above embodiments, which will not be repeated here.
[0072] In some implementations, to improve the accuracy of blood glucose prediction information, biometric signals of the measurement subject can be collected, and the blood glucose prediction information can be corrected based on these signals to obtain more accurate blood glucose prediction information. The blood glucose prediction information includes at least one of the predicted blood glucose concentration and a first predicted blood glucose range. Optionally, biometric signals include, but are not limited to, information such as blood oxygen saturation, heart rate, skin temperature, body temperature, electrocardiogram (ECG) signals, and electrodermal activity (EDA) signals.
[0073] In other words, by acquiring the biometric signals of the measurement subject and correcting the blood glucose prediction information based on the biometric signals, the target blood glucose prediction information of the measurement subject is obtained. The target blood glucose prediction information includes at least one of the target predicted blood glucose concentration and the target predicted blood glucose range of the measurement subject.
[0074] Optionally, the blood glucose information of the measured object can be estimated based on the mapping relationship between biometric signals and blood glucose concentration to obtain a second estimated blood glucose range, so as to correct the blood glucose prediction information based on the second estimated blood glucose range.
[0075] Optionally, if the error between the first estimated blood glucose range and the second estimated blood glucose range is less than or equal to the error threshold, the first estimated blood glucose range and the second estimated blood glucose range are averaged to obtain the target estimated blood glucose range, and the estimated blood glucose concentration is used as the target estimated blood glucose concentration.
[0076] Optionally, if the error between the first and second estimated blood glucose intervals is greater than the error threshold, a prompt message can be sent to the subject, prompting the subject to retest blood glucose.
[0077] The non-invasive blood glucose measurement method provided in this disclosure sends a target light source of a target wavelength to the measurement subject and receives the scattered signal scattered by the measurement subject. By determining the second light intensity value of the target light source and the first light intensity value of the scattered signal, the absorbance corresponding to the scattered signal is calculated, and the blood glucose of the measurement subject is estimated based on the absorbance to obtain the estimated blood glucose concentration of the measurement subject and the first estimated blood glucose range in which the estimated blood glucose concentration falls. The estimated blood glucose concentration and the first estimated blood glucose range are used as blood glucose prediction information. The blood glucose prediction information can be corrected based on the biometric signals of the measurement subject to determine the target blood glucose prediction information, which can then be fed back to the measurement subject. This disclosure uses a light source with a stronger correlation to human blood glucose for blood glucose prediction, providing a direct and accurate basis for predicting changes in human blood glucose levels, thereby improving the accuracy of blood glucose prediction. By using a wearable device to measure blood glucose, the convenience of blood glucose measurement is improved.
[0078] Based on the above embodiments, the present disclosure embodiments can explain and illustrate the process of correcting blood glucose prediction information, such as... Figure 4 As shown, the process of correcting blood glucose prediction information in this embodiment includes the following steps:
[0079] S401: Based on biosignal signals, acquire blood oxygen data of the measurement subject, and determine the first metabolic parameter of the measurement subject based on the blood oxygen data.
[0080] In this embodiment of the disclosure, the biosignature signal refers to the blood oxygen data of the measured object, which may be blood oxygen saturation. Optionally, photoplethysmography (PPG) can be used to analyze the scattering signal to obtain the blood oxygen data of the measured object.
[0081] Understandably, the amount of blood in the blood vessels changes periodically during a heartbeat, and this change affects the amount of light absorbed by tissues. Since oxyhemoglobin and deoxyhemoglobin have different absorption rates for different wavelengths of light, PPG can utilize this property to estimate blood oxygen saturation. The absorption rates of these two hemoglobins can be measured separately using two different wavelengths of light (e.g., 660nm red light and 940nm infrared light). Because oxyhemoglobin has a lower absorption rate for infrared light and a higher absorption rate for red light, while deoxyhemoglobin has a higher absorption rate for infrared light and a lower absorption rate for red light, blood oxygen saturation can be estimated by measuring the ratio of absorption of these two types of light.
[0082] In some implementations, blood oxygenation data refers to the percentage of oxyhemoglobin in total hemoglobin. Based on this data, the oxyhemoglobin / deoxyhemoglobin ratio of the measured object can be obtained. Optionally, the percentage of oxyhemoglobin can be directly expressed as blood oxygen saturation. In this case, the percentage of deoxyhemoglobin is 100% minus the percentage of oxyhemoglobin. Based on the percentages of oxyhemoglobin and deoxyhemoglobin, the oxyhemoglobin / deoxyhemoglobin ratio can be calculated.
[0083] Understandably, amplitude / phase differences are usually related to changes in light intensity. Since oxyhemoglobin and deoxyhemoglobin have different absorption coefficients for different wavelengths of light, the intensity of scattered light will change accordingly when their proportions in human tissues change. This change in light intensity can be considered as an amplitude / phase difference.
[0084] In other words, the amplitude / phase difference of the measured object in medical imaging can be determined based on the oxyhemoglobin / deoxyhemoglobin ratio, and the first metabolic parameter of the measured object can be determined based on the amplitude / phase difference. Optionally, the metabolic characteristics of the measured object can be calculated based on the amplitude / phase difference, and these metabolic characteristics can be used as the first metabolic parameter.
[0085] S402, based on the first metabolic parameter of the subject, determine the second estimated blood glucose range for the subject.
[0086] In some implementations, the correspondence between metabolic parameters and blood glucose ranges can be calibrated. By determining the first metabolic parameter of the measurement object and querying the correspondence based on the first metabolic parameter, the blood glucose range corresponding to the first metabolic parameter can be determined as the second estimated blood glucose range.
[0087] Optionally, a model can be trained based on sample blood oxygen and blood glucose data, and a second predicted blood glucose range can be determined based on the trained model. This model can be a large model or a neural network model. That is, blood oxygen data can be directly input into the trained model, which can then output the second predicted blood glucose range.
[0088] S403, based on the second estimated blood glucose range of the measurement object, corrects the blood glucose prediction information to obtain the target blood glucose prediction information.
[0089] In some implementations, the blood glucose prediction information includes at least one of the predicted blood glucose concentration of the measured object and a first predicted blood glucose range. The target blood glucose concentration can be obtained by determining whether the predicted blood glucose concentration is within a second predicted blood glucose range. If it is, the predicted blood glucose concentration is used as the target blood glucose concentration. If it is not, the predicted blood glucose concentration is corrected based on the difference between the second and first predicted blood glucose ranges to obtain the target blood glucose concentration.
[0090] Optionally, it can be determined whether the difference between the second estimated blood glucose range and the first estimated blood glucose range is greater than an error threshold. If it is less than the error threshold, the first estimated blood glucose range and the second estimated blood glucose range can be averaged to obtain the target blood glucose range. If it is greater than or equal to the error threshold, a prompt message is sent to the measurement subject, prompting the subject to re-test blood glucose.
[0091] Furthermore, at least one of the target blood glucose concentration and the target blood glucose range is used as the target blood glucose prediction information.
[0092] The non-invasive blood glucose measurement method provided in this disclosure corrects blood glucose prediction information based on biometric signals to obtain target blood glucose prediction information, thereby improving the accuracy of the target blood glucose prediction information. Using human biometric signals to correct blood glucose prediction information provides a direct and accurate basis for predicting changes in human blood glucose levels, thus improving the accuracy of blood glucose prediction.
[0093] Based on the above embodiments, the present disclosure embodiments can explain and illustrate the process of correcting blood glucose prediction information, such as... Figure 5 As shown, the process of correcting blood glucose prediction information in this embodiment includes the following steps:
[0094] S501 acquires electrocardiogram (ECG) signals and electrical skin response (EDA) signals.
[0095] In some implementations, the wearable device's own electrode sensors acquire the electrical signal of the object being measured, and then perform processing operations such as amplification, filtering, and analog-to-digital conversion on the electrical signal to obtain the processed electrical signal. The processed electrical signal is then analyzed to obtain the electrocardiogram (ECG) signal and the electrodermal activity (EDA) signal.
[0096] by Figure 2 Taking the smartwatch shown as an example, the object being measured can obtain electrical signals by touching the electrode sensors configured on the smartwatch, and the electrical signals can be analyzed to obtain ECG signals and EDA signals.
[0097] For example, suppose Figure 2 The bottom button of a smartwatch contains an electrode sensor, and there are two bottom electrodes on the back of the watch. By touching the bottom button with a finger, the bottom electrodes contact the skin, allowing ECG and EDA signals to be acquired. In other words, by touching the bottom button, or any other side button with an electrode sensor, and using the bottom electrodes in contact with the skin, an ECG signal can be obtained. The EDA signal can be directly obtained from the two bottom electrodes in contact with the skin.
[0098] S502 determines the third estimated blood glucose range for the subject based on ECG and EDA signals.
[0099] In some implementations, the neural activity of the subject can be determined based on the waveform characteristics of the ECG and EDA signals, and a third estimated blood glucose range can be determined based on the neural activity. Optionally, waveform features can be extracted from the ECG and EDA signals, and the target neural activity of the subject can be determined based on the extracted waveform features. Further, based on the target neural activity, a first reference relationship between neural activity and blood glucose concentration is queried to determine the third estimated blood glucose range of the subject.
[0100] S503 corrects the blood glucose prediction information based on the third predicted blood glucose range to obtain the target blood glucose prediction information.
[0101] In some implementations, the blood glucose prediction information includes at least one of the predicted blood glucose concentration of the measured object and a first predicted blood glucose interval. The target blood glucose concentration can be obtained by determining whether the predicted blood glucose concentration is within a third predicted blood glucose interval. If it is, the predicted blood glucose concentration is used as the target blood glucose concentration. If it is not, the predicted blood glucose concentration is corrected based on the difference between the third predicted blood glucose interval and the first predicted blood glucose interval.
[0102] Optionally, it can be determined whether the difference between the third estimated blood glucose range and the first estimated blood glucose range is greater than an error threshold. If it is less than the error threshold, the first estimated blood glucose range and the third estimated blood glucose range can be averaged to obtain the target blood glucose range. If it is greater than or equal to the error threshold, a prompt message is sent to the measurement subject, prompting the subject to re-test blood glucose.
[0103] Furthermore, at least one of the target blood glucose concentration and the target blood glucose range is used as the target blood glucose prediction information.
[0104] The non-invasive blood glucose measurement method provided in this disclosure corrects blood glucose prediction information based on biometric signals to obtain target blood glucose prediction information, thereby improving the accuracy of the target blood glucose prediction information. Using human biometric signals to correct blood glucose prediction information provides a direct and accurate basis for predicting changes in human blood glucose levels, thus improving the accuracy of blood glucose prediction.
[0105] Based on the above embodiments, the present disclosure embodiments can explain and illustrate the process of correcting blood glucose prediction information, such as... Figure 6 As shown, the process of correcting blood glucose prediction information in this embodiment includes the following steps:
[0106] S601 acquires heart rate and blood oxygen data of the measured object based on biometric signals.
[0107] In this embodiment of the disclosure, the biometric signal refers to the heart rate and blood oxygenation data of the measured object, wherein the blood oxygenation data may be blood oxygen saturation. Optionally, PPG can be used to analyze the scattered signal to obtain the heart rate and blood oxygenation data of the measured object.
[0108] S602, acquire the temperature information of the object being measured.
[0109] Optionally, the temperature information includes, but is not limited to, skin temperature and core body temperature. Optionally, the temperature information of the object being measured can be determined based on multiple temperature sensors on the wearable device itself.
[0110] Optionally, the skin temperature of the object being measured can be obtained based on a temperature sensor in a wearable device. This temperature sensor is in direct contact with the skin of the object being measured. Figure 2 The smartwatch shown has a temperature sensor that measures skin temperature located near the bottom of the watch case. It directly measures skin temperature by contacting the skin through a heat-conducting material.
[0111] Optionally, the heat flux or ambient temperature of the object being measured can be obtained based on another temperature sensor in the wearable device, and the core body temperature of the object can be predicted based on the heat flux or ambient temperature. In this case, the other temperature sensor does not come into contact with the skin of the object being measured.
[0112] Optionally, if another temperature sensor detects the ambient temperature, a model can be pre-trained based on the ambient temperature, skin temperature, and heart rate. This model is used to predict the core body temperature of the subject being measured. In other words, when the other sensor detects the ambient temperature, it inputs the ambient temperature into the pre-trained model, which then outputs the predicted core body temperature of the subject.
[0113] In some implementations, if another temperature sensor detects the heat flow of the object being measured, the core body temperature can be predicted based on a pre-trained heat flow model. Alternatively, the heat flow can be determined based on the difference between the temperature sensors.
[0114] In other words, the heat flow of the object being measured is obtained by acquiring the temperature difference between the first temperature sensor and the second temperature sensor, and based on the temperature difference, one of the first temperature sensor and the second temperature sensor is used to measure skin temperature.
[0115] S603 corrects the blood glucose prediction information based on heart rate, blood oxygen data and temperature information to obtain the target blood glucose prediction information.
[0116] In some implementations, a fourth estimated blood glucose range can be determined based on heart rate, blood oxygen data, and temperature information. The blood glucose prediction information can then be corrected based on this fourth estimated blood glucose range to obtain the target blood glucose prediction information.
[0117] Optionally, a second metabolic parameter can be determined based on heart rate and blood oxygenation data, as well as temperature information. Heart rate, blood oxygenation data, and temperature information are all directly proportional to metabolic rate; for example, a higher heart rate corresponds to a higher metabolic rate. The metabolic rate of the subject can be calculated based on heart rate, blood oxygenation data, and temperature information, and used as the second metabolic parameter.
[0118] Furthermore, a fourth estimated blood glucose range can be determined based on the second metabolic parameter of the measurement subject. In other words, the correspondence between calibrated metabolic parameters and blood glucose ranges can be obtained. By determining the second metabolic parameter of the measurement subject and querying this correspondence based on the second metabolic parameter, the blood glucose range corresponding to the second metabolic parameter can be determined as the fourth estimated blood glucose range.
[0119] Furthermore, the blood glucose prediction information can be corrected based on the fourth predicted blood glucose range to obtain the target blood glucose prediction information. The blood glucose prediction information includes at least one of the predicted blood glucose concentration of the measured object and the first predicted blood glucose range. The target blood glucose concentration can be obtained by determining whether the predicted blood glucose concentration falls within the fourth predicted blood glucose range. If it does, the predicted blood glucose concentration is used as the target blood glucose concentration; otherwise, the predicted blood glucose concentration is corrected based on the difference between the fourth and first predicted blood glucose ranges.
[0120] Optionally, it can be determined whether the difference between the fourth estimated blood glucose interval and the first estimated blood glucose interval is greater than an error threshold. If it is less than the error threshold, the first estimated blood glucose interval and the fourth estimated blood glucose interval can be averaged to obtain the target blood glucose interval. If it is greater than or equal to the error threshold, a prompt message is sent to the measurement subject, prompting the subject to re-test blood glucose.
[0121] Furthermore, at least one of the target blood glucose concentration and the target blood glucose range is used as the target blood glucose prediction information.
[0122] In some implementations, the biometric signals of the measured object include, but are not limited to: blood oxygen, heart rate, skin temperature, body temperature, ECG signal and EDA signal, and the blood glucose prediction information can be corrected based on any one of these biometric signals.
[0123] Optionally, when the biometric signals of the measured object are N groups, where N is a natural number greater than 1, the blood glucose prediction information is corrected by determining the predicted blood glucose range of at least two groups of biometric signals among the N groups of biometric signals and combining the predicted blood glucose range of at least two groups of biometric signals, so as to obtain the target blood glucose prediction information.
[0124] For example, if N is 3, then the N sets of biometric signals can be blood oxygen data, temperature information, ECG signal, and EDA signal, respectively. This allows for the determination of a second estimated blood glucose range corresponding to the blood oxygen data, a third estimated blood glucose range corresponding to the ECG and EDA signals, and a fourth estimated blood glucose range corresponding to the temperature information. Furthermore, the blood glucose prediction information can be corrected based on the second and third estimated blood glucose ranges, or based on the second and fourth estimated blood glucose ranges, or based on the third and fourth estimated blood glucose ranges, or based on the second, third, and fourth estimated blood glucose ranges.
[0125] The non-invasive blood glucose measurement method provided in this disclosure corrects blood glucose prediction information based on biometric signals to obtain target blood glucose prediction information, thereby improving the accuracy of the target blood glucose prediction information. Using human biometric signals to correct blood glucose prediction information provides a direct and accurate basis for predicting changes in human blood glucose levels, thus improving the accuracy of blood glucose prediction.
[0126] Figure 7 This is a block diagram illustrating a non-invasive blood glucose measurement device according to an exemplary embodiment. (Refer to...) Figure 7 The non-invasive blood glucose measuring device 700 of this disclosure includes a transceiver module 701 and a determination module 702:
[0127] The transceiver module 701 is used to transmit a target light source at a target wavelength and receive the scattering signal of the target light source, wherein the target wavelength is a wavelength that can be absorbed by glucose.
[0128] The determination module 702 is used to determine the absorbance corresponding to the scattering signal and determine the blood glucose prediction information of the measurement object based on the absorbance.
[0129] In one embodiment of this disclosure, the determining module 702 is further configured to: determine a first light intensity value of the scattered signal and a second light intensity value of the target light source; and determine the absorbance corresponding to the scattered signal based on the first light intensity value and the second light intensity value.
[0130] In one embodiment of this disclosure, the determining module 702 is further configured to: determine a first light intensity value of the scattered signal based on the current value output by the photodiode.
[0131] In one embodiment of this disclosure, the determining module 702 is further configured to: acquire the biometric signal of the measurement object; and correct the blood glucose prediction information based on the biometric signal to obtain the target blood glucose prediction information of the measurement object, wherein the target blood glucose prediction information includes at least one of the target predicted blood glucose concentration and the target predicted blood glucose range of the measurement object.
[0132] In one embodiment of this disclosure, the determining module 702 is further configured to: acquire blood oxygen data of the measurement object based on the biometric signal, and determine a first metabolic parameter of the measurement object based on the blood oxygen data; determine a second estimated blood glucose range of the measurement object based on the first metabolic parameter of the measurement object; and correct the blood glucose prediction information based on the second estimated blood glucose range of the measurement object to obtain the target blood glucose prediction information.
[0133] In one embodiment of this disclosure, the determining module 702 is further configured to: obtain the ratio of oxyhemoglobin to deoxyhemoglobin content of the measured object based on the blood oxygen data; determine the amplitude / phase difference of the measured object in medical imaging based on the ratio of oxyhemoglobin to deoxyhemoglobin; and determine the first metabolic parameter of the measured object based on the amplitude / phase difference.
[0134] In one embodiment of this disclosure, the determining module 702 is further configured to: acquire an electrocardiogram (ECG) signal and an electrical skin response (EDA) signal; determine a third estimated blood glucose range for the measurement object based on the ECG signal and the EDA signal; and correct the blood glucose prediction information based on the third estimated blood glucose range to obtain the target blood glucose prediction information.
[0135] In one embodiment of this disclosure, the determining module 702 is further configured to: extract waveform features from the ECG signal and EDA signal, and determine the target neural activity of the measurement object based on the extracted waveform features; and, based on the target neural activity, query a first reference relationship between neural activity and blood glucose concentration to determine a third estimated blood glucose range for the measurement object.
[0136] In one embodiment of this disclosure, the determining module 702 is further configured to: acquire the heart rate and blood oxygen data of the measurement object based on the biometric signal; acquire the temperature information of the measurement object; and correct the blood glucose prediction information according to the heart rate, blood oxygen data and the temperature information to obtain the target blood glucose prediction information.
[0137] In one embodiment of this disclosure, the determining module 702 is further configured to: determine a second metabolic parameter of the measurement object based on the heart rate and blood oxygen data and the temperature information; determine a fourth estimated blood glucose range of the measurement object based on the second metabolic parameter of the measurement object; and correct the blood glucose prediction information based on the fourth estimated blood glucose range to obtain the target blood glucose prediction information.
[0138] In one embodiment of this disclosure, the determining module 702 is further configured to: acquire the skin temperature of the measurement object; acquire the heat flow or ambient temperature of the measurement object, and predict the core body temperature of the measurement object based on the heat flow or ambient temperature of the measurement object.
[0139] In one embodiment of this disclosure, the determining module 702 is further configured to: acquire the temperature difference between the first temperature sensor and the second temperature sensor, and acquire the heat flow of the object being measured based on the temperature difference, wherein one of the first temperature sensor and the second temperature sensor is used to measure the skin temperature.
[0140] In one embodiment of this disclosure, the determining module 702 is further configured to: determine the estimated blood glucose range of at least two sets of biometric signals among N sets of biometric signals, and combine the estimated blood glucose range of the at least two sets of biometric signals to correct the blood glucose prediction information to obtain the target blood glucose prediction information.
[0141] The non-invasive blood glucose measurement device provided in this disclosure sends a target wavelength light source to the measurement subject and receives the scattered signal from the measurement subject. By analyzing the absorbance of the scattered signal, the blood glucose level of the measurement subject is estimated to obtain the estimated blood glucose concentration and a first estimated blood glucose range. The estimated blood glucose concentration and the first estimated blood glucose range are then fed back to the measurement subject as blood glucose prediction information. This disclosure uses a light source with a stronger correlation to human blood glucose levels for blood glucose prediction, providing a direct and accurate basis for predicting changes in human blood glucose levels, thereby improving the accuracy of blood glucose prediction. The use of a wearable device to measure blood glucose improves the convenience of blood glucose measurement.
[0142] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.
[0143] Figure 8 This is a block diagram illustrating an electronic device according to an exemplary embodiment.
[0144] like Figure 8 As shown, the above-mentioned electronic device 800 includes:
[0145] The memory 801 and the processor 802 are connected by a bus 803, which connects the different components (including the memory 801 and the processor 802). The memory 801 stores a computer program, and when the processor 802 executes the program, it implements the non-invasive blood glucose measurement method described in this embodiment of the present disclosure.
[0146] Bus 803 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.
[0147] Electronic device 800 typically includes a variety of electronic device readable media. These media can be any available media that can be accessed by electronic device 800, including volatile and non-volatile media, removable and non-removable media.
[0148] Memory 801 may also include computer system readable media in the form of volatile memory, such as random access memory (RAM) 804 and / or cache memory 805. Electronic device 800 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 806 may be used to read and write non-removable, non-volatile magnetic media (… Figure 8 Not shown; usually referred to as a "hard drive"). Although Figure 8 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 803 via one or more data media interfaces. Memory 801 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.
[0149] A program / utility 808 having a set (at least one) of program modules 807 may be stored, for example, in memory 801. Such program modules 807 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 807 typically perform the functions and / or methods described in the embodiments of this disclosure.
[0150] Electronic device 800 can also communicate with one or more external devices 809 (e.g., keyboard, pointing device, display 891, etc.), and with one or more devices that enable a user to interact with the electronic device 800, and / or with any device that enables the electronic device 800 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed through input / output (I / O) interface 892. Furthermore, electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 893. Figure 8 As shown, network adapter 893 communicates with other modules of electronic device 800 via bus 803. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0151] The processor 802 executes various functional applications and data processing by running programs stored in the memory 801.
[0152] It should be noted that the implementation process and technical principles of the electronic device in this embodiment are explained in the foregoing description of the non-invasive blood glucose measurement method of this disclosure, and will not be repeated here.
[0153] To implement the above embodiments, this disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the steps of the non-invasive blood glucose measurement method provided in this disclosure.
[0154] Alternatively, the computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.
[0155] To achieve the above embodiments, this disclosure also provides a computer program product, including a computer program, characterized in that, when the computer program is executed by the processor of an electronic device, it implements the non-invasive blood glucose measurement method as described above.
[0156] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.
[0157] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.
Claims
1. A non-invasive method for measuring blood glucose, characterized in that, The method includes: The system emits a target light source at a target wavelength and receives the scattered signal from the target light source, wherein the target wavelength is a wavelength that can be absorbed by glucose. The absorbance corresponding to the scattered signal is determined, and the blood glucose prediction information of the measured object is determined based on the absorbance.
2. The method according to claim 1, characterized in that, Determining the absorbance corresponding to the scattered signal includes: Determine a first light intensity value of the scattered signal and a second light intensity value of the target light source; The absorbance corresponding to the scattering signal is determined based on the first light intensity value and the second light intensity value.
3. The method according to claim 2, characterized in that, Determining the first light intensity value of the scattered signal includes: The first light intensity value of the scattered signal is determined based on the current value output by the photodiode.
4. The method according to any one of claims 1-3, characterized in that, The blood glucose prediction information includes at least one of the predicted blood glucose concentration of the measurement subject and a first predicted blood glucose range. After determining the blood glucose prediction information of the measurement subject based on the absorbance, the method further includes: Acquire the biometric signals of the measured object; Based on the biometric signal, the blood glucose prediction information is corrected to obtain the target blood glucose prediction information of the measurement object. The target blood glucose prediction information includes at least one of the target predicted blood glucose concentration and the target predicted blood glucose range of the measurement object.
5. The method according to claim 4, characterized in that, The step of correcting the blood glucose prediction information based on the biometric signal to obtain the target blood glucose prediction information for the measured object includes: Based on the biosignal signals, blood oxygen data of the measurement subject is obtained, and based on the blood oxygen data, a first metabolic parameter of the measurement subject is determined; Based on the first metabolic parameters of the measured object, a second estimated blood glucose range is determined for the measured object; Based on the second estimated blood glucose range of the measured object, the blood glucose prediction information is corrected to obtain the target blood glucose prediction information.
6. The method according to claim 5, characterized in that, The determination of the first metabolic parameter of the measured object based on the blood oxygen data includes: Based on the blood oxygen data, the ratio of oxyhemoglobin to deoxyhemoglobin in the measured object is obtained; Based on the oxyhemoglobin / deoxyhemoglobin ratio, the amplitude / phase difference of the measured object in medical imaging is determined, and based on the amplitude / phase difference, the first metabolic parameter of the measured object is determined.
7. The method according to claim 4, characterized in that, The step of correcting the blood glucose prediction information based on the biometric signal to obtain the target blood glucose prediction information for the measured object includes: Acquire electrocardiogram (ECG) signals and electrical skin response (EDA) signals; Based on the ECG and EDA signals, the third estimated blood glucose range for the measured subject is determined; Based on the third estimated blood glucose range, the blood glucose prediction information is corrected to obtain the target blood glucose prediction information.
8. The method according to claim 7, characterized in that, The step of determining the third estimated blood glucose range for the measured subject based on the ECG signal and EDA signal includes: Waveform features are extracted from the ECG and EDA signals, and the target neural activity of the measured object is determined based on the extracted waveform features. Based on the target neural activity, the first reference relationship between neural activity and blood glucose concentration is queried to determine the third estimated blood glucose range for the measured object.
9. The method according to claim 4, characterized in that, The step of correcting the blood glucose prediction information based on the biometric signal to obtain the target blood glucose prediction information for the measured object includes: Based on the biosignature signals, the heart rate and blood oxygen data of the measurement subject are obtained; Obtain the temperature information of the object being measured; Based on the heart rate, blood oxygen data, and temperature information, the blood glucose prediction information is corrected to obtain the target blood glucose prediction information.
10. The method according to claim 9, characterized in that, The step of correcting the blood glucose prediction information based on the heart rate, blood oxygen data, and temperature information to obtain the target blood glucose prediction information includes: Based on the heart rate and blood oxygen data, as well as the temperature information, the second metabolic parameter of the measured object is determined; Based on the second metabolic parameters of the measured object, a fourth estimated blood glucose range for the measured object is determined; Based on the fourth predicted blood glucose range, the predicted blood glucose information is corrected to obtain the target predicted blood glucose information.
11. The method according to claim 9, characterized in that, The step of obtaining the temperature information of the object being measured includes: Obtain the skin temperature of the object being measured; The heat flow or ambient temperature of the object being measured is obtained, and the core body temperature of the object being measured is predicted based on the heat flow or ambient temperature of the object being measured.
12. The method according to claim 11, characterized in that, The process of obtaining the heat flow of the measured object includes: The temperature difference between the first temperature sensor and the second temperature sensor is obtained, and the heat flow of the measured object is obtained based on the temperature difference, wherein one of the first temperature sensor and the second temperature sensor is used to measure the skin temperature.
13. The method according to claim 4, characterized in that, When the biometric signals of the measurement object are N sets, where N is a natural number greater than 1, the step of correcting the blood glucose prediction information based on the biometric signals to obtain the target blood glucose prediction information of the measurement object includes: Determine the predicted blood glucose range of at least two sets of biosignature signals from N sets of biosignature signals, and combine the predicted blood glucose ranges of the at least two sets of biosignature signals to correct the blood glucose prediction information to obtain the target blood glucose prediction information.
14. A non-invasive blood glucose measuring device, characterized in that, The device includes: A transceiver module is used to transmit a target light source at a target wavelength and receive the scattered signal from the target light source, wherein the target wavelength is a wavelength that can be absorbed by glucose. The determination module is used to determine the absorbance corresponding to the scattering signal and to determine the blood glucose prediction information of the measurement object based on the absorbance.
15. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured as follows: The steps for implementing the method according to any one of claims 1-13.
16. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When executed by a processor, the program instructions implement the steps of the method described in any one of claims 1-13.