Human body physiological signal detector and detection method thereof

Abstract

The invention discloses a human body physiological signal detector and a detection method thereof, and relates to the technical field of health monitoring equipment, the human body physiological signal detector comprises a detector main body, and the detector main body is provided with a display module; the detection bracelet is provided with a multifunctional sensor module, so that a plurality of physiological signals can be acquired at the same time; the data processing unit is used for receiving the physiological signals from the sensor module and performing intelligent analysis by applying an advanced signal processing technology and a machine learning model; the wireless communication module is used for transmitting an analysis result of the data processing unit to external receiving equipment; according to the technical scheme, multiple physiological signal parameters such as electrocardiogram, pulse oxyhemoglobin saturation, skin resistance response and body temperature can be obtained at the same time, comprehensive health monitoring is achieved, the collected data are intelligently analyzed through the machine learning model based on the deep learning architecture, and the monitoring accuracy and efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring equipment, and in particular to a human physiological signal detector and a detection method thereof. Background Art

[0002] With the accelerated pace of modern life and people's increasing attention to health, the demand for portable health monitoring devices is growing. Traditional health monitoring devices are often large and inconvenient to carry, and usually can only monitor a single physiological indicator and cannot fully reflect a person's overall health status. In addition, the data processing capacity of traditional devices is limited, making it difficult to achieve intelligent data analysis and real-time feedback.

[0003] In recent years, with the development of microelectronics technology, especially the application of microelectromechanical systems (MEMS), sensors have become smaller, more sensitive and less expensive. This makes it possible to develop portable devices that integrate multiple physiological signal monitoring functions. At the same time, the advancement of big data analysis and machine learning technology also provides technical support for more accurate health monitoring and prediction. However, most of the existing portable health monitoring devices on the market have failed to make full use of these advanced technologies, or have problems such as insufficient data security and poor user experience. Summary of the invention

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a human physiological signal detector and a detection method thereof to solve the problems in the background technology.

[0005] In view of this, the present invention provides a human physiological signal detector and a detection method thereof, comprising:

[0006] A detector body, wherein a display module is provided on the detector body;

[0007] A detection bracelet is provided with a multifunctional sensor module, which can simultaneously obtain multiple physiological signals, and physiological signal parameters such as electrocardiogram (ECG), pulse oxygen saturation (SpO2), skin resistance response (GSR) and body temperature;

[0008] A data processing unit, used for receiving physiological signals from the sensor module, applying advanced signal processing technology and machine learning models to perform intelligent analysis, and displaying the data after intelligent analysis on a display module;

[0009] A wireless communication module, used for transmitting the analysis result of the data processing unit to an external receiving device;

[0010] The data processing unit further includes a machine learning model based on a deep learning architecture, which can be trained to identify multiple physiological states and has self-optimization capabilities, and can continuously learn and improve detection accuracy during use;

[0011] The sensor module also includes an ambient light sensor for correcting measurement errors caused by interference from external light sources.

[0012] Optionally, the multifunctional sensor module adopts a micro-electromechanical system (MEMS) and is designed with flexible materials to achieve the effect of being both miniaturized and comfortable to wear, and is suitable for long-term wear.

[0013] Optionally, the wireless communication module supports Bluetooth Low Energy (BLE) and Wi-Fi 6 standards, and integrates network security protocols to ensure safe and efficient transmission of data, and can switch seamlessly in multiple network environments.

[0014] Optionally, the data processing unit is used to monitor changes in physiological parameters in real time, and when an abnormal situation is detected, it not only automatically triggers an alarm, but also notifies a preset contact or medical institution through a built-in emergency contact function;

[0015] In addition, the data processing unit also includes a context awareness engine that can adjust the monitoring frequency and intensity of physiological signals according to the user's activity type.

[0016] Optionally, the machine learning model is used to improve detection accuracy by continuously learning the physiological signal patterns of individual users, and can adapt to the differences between different individuals to provide personalized health monitoring services;

[0017] The machine learning model can also obtain data from user groups through the cloud platform to further improve the model's generalization ability and prediction accuracy.

[0018] The present invention also protects a detection method of a human physiological signal detector, comprising the following steps:

[0019] a) Acquisition: Acquiring physiological signals from the user through a multifunctional sensor module;

[0020] b) Intelligent analysis: The data processing unit uses machine learning models to perform intelligent analysis on the collected signals to identify potential health problems;

[0021] c) Secure transmission: The analysis results are encrypted and transmitted to the designated receiving device through the wireless communication module;

[0022] d) Feedback: Provide personalized health advice to users based on the analysis results and initiate emergency response mechanisms when necessary;

[0023] The method also includes a visual display of data analysis results, so that users can intuitively understand the changing trends of their health status.

[0024] Optionally, the intelligent analysis includes using the machine learning model to perform deep learning processing on the data, and combining it with a cloud-based big data analysis platform to improve the accuracy and reliability of the detection;

[0025] At the same time, the method also includes learning about user behavior patterns in order to better explain the reasons for changes in physiological signals.

[0026] Optionally, the machine learning model is periodically updated to adapt to new physiological signal patterns, and the latest model update can be automatically downloaded and installed via OTA (Over-The-Air) technology;

[0027] The update process also includes data backup and recovery functions to ensure that user data is not lost.

[0028] Optionally, the wireless communication module is capable of receiving instructions from an external device to adjust the working mode or parameter settings of the detector, and can automatically adjust the sampling frequency of the sensor according to the user's activity status, thereby extending the battery life of the device;

[0029] At the same time, the method also includes a self-diagnosis function for the device status, which can detect hardware failures in time and remind users to perform maintenance.

[0030] Optionally, a user interface is included that allows the user to view historical data trends, set health goals, and integrate with health management tools or services via a smartphone application;

[0031] The user interface supports multiple languages ​​and accessibility options to accommodate users from different regions and with special needs.

[0032] It can be seen from the above technical solutions that the embodiments of the present invention have the following advantages:

[0033] 1. The present invention integrates multiple sensor modules on the detection bracelet to simultaneously obtain multiple physiological signal parameters such as electrocardiogram, pulse oximetry, skin resistance response and body temperature, thereby achieving comprehensive health monitoring. The collected data is intelligently analyzed using a machine learning model based on a deep learning architecture, which can identify multiple physiological states and has self-optimization capabilities, thereby improving the accuracy and efficiency of monitoring. At the same time, the machine learning model continuously learns the physiological signal patterns of individual users, can provide personalized health monitoring services, and obtains data from user groups through a cloud-based big data analysis platform, further improving the generalization ability and prediction accuracy of the model.

[0034] 2. The present invention ensures the safe and efficient transmission of data through a wireless communication module that supports low-power Bluetooth and Wi-Fi 6 standards, and can switch seamlessly in a variety of network environments, thereby enhancing the practicality of the device. The data processing unit can monitor changes in physiological parameters in real time, and automatically trigger an alarm when an abnormal situation is detected, and notify the preset contact or medical institution through the built-in emergency contact function, thereby improving the timeliness of emergency treatment.

[0035] 3. The present invention can adjust the monitoring frequency and intensity of physiological signals according to the user's activity type through the context perception engine, thereby improving the pertinence and effectiveness of monitoring.

[0036] These features and advantages of the present invention will be disclosed in detail in the following specific embodiments and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, they can also obtain the drawings based on the structures shown in these drawings without paying any creative work.

[0038] Figure 1 It is a schematic diagram of the structure of the present invention;

[0039] Figure 2 This is a system structure block diagram of the present invention.

[0040] Explanation of the accompanying drawings: 1. Detector body; 2. Detection bracelet; 21. Multi-function sensor module; 3. Display module; 4. Data processing unit; 5. Wireless communication module; 6. User interface; 7. External receiving device; 8. Cloud platform. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back, etc.), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0043] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features limited to "first" and "second" may explicitly or implicitly include at least one of the features. In addition, if "and / or" or "and / or" appears in the full text, its meaning includes three parallel solutions. Taking "A and / or B" as an example, it includes solution A, solution B, or solutions that satisfy both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0044] A human physiological signal detector and a detection method thereof according to an embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

[0045] Example 1

[0046] For easier understanding, see Figure 1 to Figure 2 , an embodiment of a human physiological signal detector and a detection method thereof provided by the present invention comprises:

[0047] A detector body 1, wherein a display module 3 is provided on the detector body 1;

[0048] The detection bracelet 2 is provided with a multifunctional sensor module 21, which can simultaneously obtain multiple physiological signals, and physiological signal parameters such as electrocardiogram (ECG), pulse oxygen saturation (SpO2), skin resistance response (GSR) and body temperature;

[0049] A data processing unit 4 is used to receive the physiological signals from the sensor module, apply advanced signal processing technology and machine learning models to perform intelligent analysis, and display the data after intelligent analysis on the display module 3;

[0050] A wireless communication module 5, used for transmitting the analysis result of the data processing unit 4 to an external receiving device 7;

[0051] The data processing unit 4 further includes a machine learning model based on a deep learning architecture, which can be trained to identify a variety of physiological states and has self-optimization capabilities, and can continuously learn and improve detection accuracy during use;

[0052] The sensor module also includes an ambient light sensor for correcting measurement errors caused by interference from external light sources.

[0053] It should be noted that the display module 3 is used to display physiological signal data and analysis results. The display module 3 should be clear and easy to read, and it is best to be touch-operated, so that users can browse information directly on the detector. The multi-function sensor module 21 on the bracelet can simultaneously collect physiological signals such as electrocardiogram (ECG), pulse oxygen saturation (SpO2), skin resistance response (GSR) and body temperature, and is also equipped with an ambient light sensor to reduce the impact of external light sources on the measurement results. The data processing unit 4 is responsible for receiving data from the sensor module and analyzing it using a pre-trained deep learning model. The deep learning model can recognize a variety of physiological states and has the ability to self-optimize. It continues to learn during use to improve detection accuracy. The analysis results will be displayed on the display module 3 of the detector body 1. The wireless communication module 5 supports low-power Bluetooth (BLE) and Wi-Fi 6 standards to ensure a safe and efficient connection with external devices.

[0054] In some embodiments, the multifunctional sensor module 21 is a micro-electromechanical system (MEMS) and is designed with flexible materials to achieve miniaturization and comfortable wearing, suitable for long-term wear. The wireless communication module 5 supports low-power Bluetooth (BLE) and Wi-Fi 6 standards, and integrates network security protocols to ensure secure and efficient data transmission, and can switch seamlessly in a variety of network environments.

[0055] The detection bracelet 2 is manufactured by combining micro-electromechanical system technology and flexible materials, which ensures the miniaturization of the device and the comfort of wearing, and is suitable for long-term wearing without causing discomfort. In order to ensure the security of data, the wireless communication module 5 integrates the network security protocol. In addition, the module can also achieve seamless switching in different network environments.

[0056] In some embodiments, the data processing unit 4 is used to monitor changes in physiological parameters in real time, and when an abnormal situation is detected, it not only automatically triggers an alarm, but also notifies a preset contact or medical institution through a built-in emergency contact function;

[0057] Furthermore, the data processing unit 4 also includes a context awareness engine, which can adjust the monitoring frequency and intensity of the physiological signals according to the activity type of the user.

[0058] When the data processing unit 4 detects an abnormality in physiological parameters, it can automatically trigger an alarm and notify the preset contact or medical institution through the emergency contact function. In addition, the context awareness engine can adjust the monitoring frequency according to the user's activity type to ensure that physiological signal changes can be captured more accurately when needed.

[0059] The machine learning model is used to improve detection accuracy by continuously learning the physiological signal patterns of individual users, and can adapt to the differences between different individuals to provide personalized health monitoring services;

[0060] The machine learning model can also obtain data of user groups through the cloud platform 8 to further improve the generalization ability and prediction accuracy of the model.

[0061] It should be noted that the machine learning model improves the accuracy of detection by learning the individual physiological signal patterns of users, and can adapt to the differences between different individuals to provide personalized health monitoring services. In addition, the model can also obtain data from user groups through the cloud platform 8 to enhance its generalization ability and prediction accuracy.

[0062] Example 2

[0063] The present invention also protects a detection method of a human physiological signal detector, comprising the following steps:

[0064] a) Acquisition: Acquiring physiological signals from the user through the multifunctional sensor module 21;

[0065] b) Intelligent analysis: the data processing unit 4 uses a machine learning model to perform intelligent analysis on the collected signals to identify potential health problems;

[0066] c) Secure transmission: The analysis results are encrypted and transmitted to the designated receiving device through the wireless communication module 5;

[0067] d) Feedback: Provide personalized health advice to users based on the analysis results and initiate emergency response mechanisms when necessary;

[0068] The method also includes a visual display of data analysis results, so that users can intuitively understand the changing trends of their health status.

[0069] It should be noted that the physiological signals are first collected by the multifunctional sensor module 21, and then the data processing unit 4 performs intelligent analysis using the machine learning model, and the analysis results are encrypted and transmitted to the designated receiving device through the wireless communication module 5. Finally, personalized health advice is provided based on the analysis results, and an emergency response mechanism is initiated when necessary. The method also includes a visual display of data to help users intuitively understand the changing trend of health status.

[0070] In order to maintain the advancement and accuracy of the machine learning model, the present invention also provides a method for regularly updating the model. The updating process can be automatically completed through OTA technology, and user data will be backed up during the updating process to prevent data loss.

[0071] The wireless communication module 5 can not only receive instructions sent by external devices to adjust the working mode or parameter settings, but also automatically adjust the sampling frequency of the sensor according to the user's activity status, which helps to extend the battery life of the device. At the same time, the device has a self-diagnosis function, which can detect hardware failures in time and issue reminders to the user.

[0072] Users can view historical data trends, set personal health goals, and integrate with health management tools or services through the smartphone app. The app supports multi-language and barrier-free access options to meet users in different regions and with special needs.

[0073] In some embodiments, the intelligent analysis includes using the machine learning model to perform deep learning processing on the data, and combining it with a cloud-based big data analysis platform to improve the accuracy and reliability of detection;

[0074] At the same time, the method also includes learning user behavior patterns to better explain the reasons for changes in physiological signals. It also includes regularly updating the machine learning model to adapt to new physiological signal patterns, and the latest model updates can be automatically downloaded and installed through OTA (Over-The-Air) technology;

[0075] The update process also includes data backup and recovery functions to ensure that user data is not lost.

[0076] The wireless communication module 5 is capable of receiving instructions from an external device to adjust the working mode or parameter settings of the detector, and can automatically adjust the sampling frequency of the sensor according to the user's activity status, thereby extending the battery life of the device;

[0077] At the same time, the method also includes a self-diagnosis function for the device status, which can detect hardware failures in time and remind users to perform maintenance.

[0078] Also included is a user interface 6 that allows a user to view historical data trends, set health goals, and integrate with health management tools or services via a smartphone application;

[0079] User Interface 6 supports multiple languages ​​and accessibility options to accommodate users in different regions and with special needs.

[0080] Working principle: The multifunctional sensor module 21 continuously collects various physiological signal data from the wearer. After receiving the sensor data, the data processing unit 4 uses the trained machine learning model for analysis, identifies possible health problems, and continuously optimizes the model performance according to the user's individual physiological signal pattern. The analysis results are encrypted by the wireless communication module 5 and securely transmitted to the user's mobile device or designated terminal. The analysis results are displayed to the user through the display module 3, and an alarm is automatically triggered when an abnormality is found. At the same time, users can get personalized health advice through smartphone applications and activate emergency response mechanisms when necessary. The situational awareness engine adjusts the monitoring strategy according to the user's activity type to ensure the effectiveness and accuracy of data collection; and the wireless communication module 5 automatically adjusts the sensor sampling frequency according to the user's activity status to extend the device's battery life. The device has a self-diagnosis function that can detect hardware failures and warn the user. The machine learning model is regularly updated through the cloud big data analysis platform to adapt to new physiological signal patterns, and the latest model updates are automatically downloaded and installed through OTA technology.

[0081] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A human physiological signal detector, characterized in that: include: A detector body, wherein a display module is provided on the detector body; A detection bracelet is provided with a multifunctional sensor module, which can simultaneously acquire multiple physiological signals, and physiological signal parameters such as electrocardiogram, pulse oxygen saturation, skin resistance response and body temperature; A data processing unit, used for receiving physiological signals from the sensor module, applying advanced signal processing technology and machine learning models to perform intelligent analysis, and displaying the data after intelligent analysis on a display module; A wireless communication module, used for transmitting the analysis result of the data processing unit to an external receiving device; The data processing unit further includes a machine learning model based on a deep learning architecture, which can be trained to identify multiple physiological states and has self-optimization capabilities, and can continuously learn and improve detection accuracy during use; The sensor module also includes an ambient light sensor for correcting measurement errors caused by interference from external light sources.

2. A human physiological signal detector according to claim 1, characterized in that: The multifunctional sensor module adopts a micro-electromechanical system and is designed with flexible materials to achieve the effect of being both miniaturized and comfortable to wear, and is suitable for long-term wear.

3. A human physiological signal detector according to claim 1, characterized in that: The wireless communication module supports low-power Bluetooth and Wi-Fi 6 standards, and integrates network security protocols to ensure safe and efficient data transmission, and can switch seamlessly in multiple network environments.

4. A human physiological signal detector according to claim 1, characterized in that: The data processing unit is used to monitor changes in physiological parameters in real time, and when an abnormal situation is detected, it not only automatically triggers an alarm, but also notifies a preset contact or medical institution through a built-in emergency contact function.

5. The human physiological signal detector according to claim 1, characterized in that: The machine learning model is used to improve detection accuracy by continuously learning the physiological signal patterns of individual users. The machine learning model can also obtain data of user groups through a cloud platform.

6. A detection method of a human physiological signal detector according to any one of claims 1 to 5, characterized in that: The following steps are involved: a) Acquisition: Acquiring physiological signals from the user through a multifunctional sensor module; b) Intelligent analysis: The data processing unit uses machine learning models to perform intelligent analysis on the collected signals to identify potential health problems; c) Secure transmission: The analysis results are encrypted and transmitted to the designated receiving device through the wireless communication module; d) Feedback: Provide personalized health advice to users based on the analysis results and initiate emergency response mechanisms when necessary; The method also includes a visual display of data analysis results, so that users can intuitively understand the changing trends of their health status.

7. The detection method of a human physiological signal detector according to claim 6, characterized in that: The intelligent analysis includes using the machine learning model to perform deep learning processing on the data, and combining it with a cloud-based big data analysis platform. The method also includes learning user behavior patterns.

8. The detection method of a human physiological signal detector according to claim 6, characterized in that: It also includes regular updates of the machine learning model, and the latest model updates can be automatically downloaded and installed via OTA technology.

9. The detection method of a human physiological signal detector according to claim 6, characterized in that: The wireless communication module is capable of receiving instructions from an external device to adjust the working mode or parameter settings of the detector.

10. The detection method of a human physiological signal detector according to claim 6, characterized in that: It also includes a user interface that allows users to view historical data trends, set health goals, and integrate with health management tools or services through a smartphone application; The user interface supports multiple languages ​​and accessibility options to accommodate users from different regions and with special needs.

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