Flexible magnetic earring type intelligent vital sign monitoring system

Abstract

The invention relates to the technical field of medical monitoring equipment, in particular to a flexible magnetic earring type intelligent vital sign monitoring system which comprises a sensor module, an ear attaching module, a wireless communication module, a data processing module, an alarm module and a display module. By means of the ear attaching module used for being worn, a sensor used for monitoring the heart rate, the respiratory rate and the oxyhemoglobin saturation can be fixed to the ear of a user through a magnetic earring, the ear cannot be hurt when the user wears the device through a silica gel material and a flexible sensor, and when monitoring is conducted, through a data processing module, the user can monitor the heart rate, the respiratory rate and the oxyhemoglobin saturation. According to the utility model, signals monitored by the sensor can be fed back and processed, and the signals can be transmitted remotely through the wireless communication module, so that early warning prompt or information transmission can be carried out on a guardian or a nurse remotely and timely when the system is used at home or in an intensive care unit, and therefore, a user can be taken care more accurately and reasonably.

Description

Technical Field

[0001] The present invention relates to the technical field of medical monitoring devices, and particularly to a flexible magnetic earring type intelligent vital sign monitoring system. Background Art

[0002] The flexible magnetic earring type intelligent vital sign monitoring system is an innovative medical device designed to monitor the vital signs of users in real time, such as heart rate, respiratory rate, blood oxygen saturation, etc. This system usually consists of a flexible sensor, a magnetic earring, a wireless communication module, and a data processing platform.

[0003] After retrieval, based on the authorized publication number CN118986302A, it discloses an ear health monitoring method based on headphones. The method includes the following steps: collecting original photoelectric signal data through a photoplethysmography sensor integrated in the headphones, where the photoplethysmography sensor includes multiple micro LEDs with different wavelengths and highly sensitive micro photodiodes; enhancing the original photoelectric signal data to obtain high-quality photoelectric signal data; performing physiological feature analysis on the high-quality photoelectric signal data to obtain physiological feature data including heart rate, heart rate variability, blood oxygen saturation, body temperature, and blood pressure estimation; transmitting the physiological feature data to a signal processing module through a flexible circuit board, and performing preliminary filtering and feature extraction to obtain physiological parameter data. The present invention integrates multiple sensors and intelligent processing modules, and users only need to wear headphones to complete the collection and analysis of health data, realizing simple operation.

[0004] The above patent can prove the feasibility of ear biometric monitoring;

[0005] Also based on the authorized publication number CN110207729A, it discloses a flexible electronic skin that applies radio frequency technology to electronic skin technology, and the data collected on the electronic skin can be wirelessly transmitted, making the use of the electronic skin more convenient; in the present invention, an RFID antenna is used as a sensor for sensing pressure, or identifying gas, or sensing temperature. When pressure acts on the electronic skin, or the gas or temperature around the electronic skin changes, the impedance of the RFID antenna changes, thereby affecting the strength of the signals received and sent by the processor. The receiving data processor determines the pressure, gas, or temperature state of the electronic skin according to the strength of the received signals, without the need to integrate sensors; in the present invention, the RFID antenna emits the signals of the processor in a wireless manner, and the RFID reading antenna receives the emitted signals and transmits the signals to the receiving data processor on the remote status acquisition module, thereby realizing wireless transmission of data.

[0006] The above patent can show the innovation of the ear-attached monitoring device.

[0007] Currently, during the monitoring of auricular signs, it not only needs to have real-time monitoring of basic items such as heart rate and respiration, but also needs to handle possible interference situations when the user wears it. For example, common infant crying increases the overall movement of the infant, accelerates blood flow rate and respiration frequency, which can easily cause misjudgment of the monitoring device. Moreover, when continuously acquiring information, it is also necessary to increase the correct assessment of the danger level to ensure that the auricular vital sign monitoring device can have a sufficient safety usage level. Summary of the Invention

[0008] In view of the deficiencies in the prior art, the present invention provides a flexible magnetic earring type intelligent vital sign monitoring system. The intelligent vital sign monitoring system includes a sensor module, an auricle module, a wireless communication module, a data processing module, an alarm module, and a display module.

[0009] The sensor module is a flexible sensor group, and the flexible sensor group includes a photoplethysmography sensor, a capacitive respiration sensor, and a pulse oximetry sensor.

[0010] The auricle module is a magnetic earring.

[0011] The wireless communication module uses Bluetooth or WIFI for transmission.

[0012] The data processing module includes a real-time monitoring unit, a data analysis unit, and a data storage unit.

[0013] The alarm module includes abnormal monitoring and remote notification.

[0014] The display module displays the data of the user's heart rate, respiration frequency, and blood oxygen saturation through a corresponding display interface.

[0015] Furthermore, the real-time monitoring unit: real-time displays the vital sign data of the user through a dedicated software or application.

[0016] The data analysis unit: analyzes the monitoring data using artificial intelligence and machine learning algorithms, identifies abnormal situations, and issues alarms.

[0017] The data storage unit: stores the monitoring data in the cloud or a local server for easy viewing and analysis by doctors and guardians at any time.

[0018] Furthermore, for the abnormal monitoring: when the monitored vital sign data exceeds the normal range, the system will automatically issue an alarm to alert medical staff and guardians.

[0019] Remote notification: notifies through text messages, phone calls, or applications to ensure timely response.

[0020] Further, a filtering unit is also included in the data analysis unit, and the filtering unit is divided into primary filtering and secondary filtering.

[0021] Further, the filtering steps based on the user's vital sign monitoring signals are as follows:

[0022] A1. Obtain the monitoring signals of the user at any time period, monitor them through the sensor module, and transmit them to the data processing module for analysis and processing;

[0023] A2. Filter the noises caused by the user's crying, turning over, and the noises emitted by surrounding people or the environment through the primary filtering in the filtering unit to ensure the stability of the transmission signals of each high-precision sensor in the sensor module;

[0024] A3. Set the passband so that the signal frequencies of the user's normal monitoring and the frequencies of accidents can pass through normally;

[0025] A4. Set the judgment for monitoring interference signals, that is, when the time duration of the interference signal exceeds 60 seconds during the filtering of the signal by the filtering unit, the alarm module is directly activated.

[0026] Further, according to the signals passing through the passband in A3, a risk rating is performed. When the critical value of the dangerous situation is reached, the alarm module can be activated to give a warning.

[0027] Further, the signal data passing through the passband in A3 is statistically analyzed. Taking 0:00 to 24:00 every day as a time cycle, a time interval t is set for the time cycle, and the user's heart rate, blood oxygen saturation, and breathing rate are observed at intervals of 3 minutes in each time interval t.

[0028] Further, through the setting of the time interval, the statistical settlement node is determined. Every 3 days is a settlement node. If there are more accident signals in the time interval t, the interval frequency in this interval is increased to once every 2 minutes. Conversely, if there are more normal vital sign signals, the interval frequency in this interval is reduced to once every 5 minutes.

[0029] Further, based on the accident occurrence signals in A3 as abnormal signal points, each sensor in the sensor module is used as a test tool, and each sensor is set with a standard value range.

[0030] Further, according to the setting of the standard value range, within the standard value range, the alarm module does not give a warning, and only information is transmitted to the user through the display module. Conversely, outside the standard value range, after filtering first to filter out the interference signals, if the monitored value still cannot reach the standard value, it is determined as an abnormal signal. Specifically, the probability of the data point under the multivariate Gaussian distribution is calculated to identify the abnormal point.

[0031] The beneficial effects of the present invention are as follows: 1. Through the ear attachment module for wearing, the sensors for monitoring heart rate, respiratory rate, and blood oxygen saturation can be fixed to the user's ear through the structure of magnetic earrings. Through the structure of silicone material and flexible sensors, the softness of the ear attachment module can be maximally ensured, so that when the user wears it, it will not cause harm to the ear. During monitoring, through the data processing module, the signals monitored by the sensors can be processed and fed back, and the signals can be transmitted remotely through the wireless communication module, enabling remote and timely warning prompts or information transmission to guardians or nurses when used at home or in the intensive care unit. Thus, the user can be taken care of more accurately and reasonably;

[0032] 2. As described in 1, when the sensors are monitoring signals, in order to avoid fluctuations or errors in the transmission of the sensor monitoring signals caused by ambient noise or the short cries and turning over of infants, a filtering unit is set during signal transmission to filter out interference signals and avoid the problem of signal output being affected by noise interference during the monitoring and feedback instant. The filtering unit is set in two parts. The first filtering can filter out interference signals, and the second filtering can retain and pass the monitoring signals within the required normal frequency range, avoiding frequent feedback of incorrect signals caused by unstable wearing or incorrect use. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0034] Figure 1 It is a schematic diagram of a flexible magnetic earring type intelligent vital sign monitoring system of the present invention;

[0035] Figure 2 It is a schematic diagram of the data processing module of a flexible magnetic earring type intelligent vital sign monitoring system of the present invention;

[0036] Figure 3 It is a schematic diagram of the filtering unit of a flexible magnetic earring type intelligent vital sign monitoring system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The following will describe in detail the embodiments of the technical solutions of the present invention with reference to the drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present invention, so they are only examples and cannot be used to limit the protection scope of the present invention.

[0038] It should be noted that unless otherwise specified, the technical terms or scientific terms used in this application shall have the ordinary meanings understood by those skilled in the art to which this invention pertains.

[0039] As Figures 1 - 3 shown, a flexible magnetic earring type intelligent vital sign monitoring system, the intelligent vital sign monitoring system includes a sensor module, an ear attachment module, a wireless communication module, a data processing module, an alarm module and a display module;

[0040] The sensor module is a flexible sensor group, the flexible sensor group includes a photoplethysmography sensor, a capacitance respiration sensor and a pulse oximetry sensor, which can monitor the heart rate, respiration rate and blood oxygen saturation of the user respectively;

[0041] The ear attachment module is a magnetic earring structure, including a silica gel sleeve covering the sensor module and magnets embedded on the outer wall of the silica gel sleeve, and is adsorbed by two magnets to ensure the stable installation of the ear attachment module at the position of the auricle or earlobe. The Shore hardness of the silica gel structure is <20HA, and the magnetic adsorption structure is segmented, which can be adjusted according to the size of the user's ear contour, and the segmented area is not less than three segments;

[0042] The wireless communication module uses Bluetooth or WIFI to transmit the monitoring data to the data processing platform or mobile device in real time, adopts a low-power design, prolongs the battery life and reduces the replacement frequency;

[0043] The data processing module includes a real-time monitoring unit, a data analysis unit and a data storage unit;

[0044] Real-time monitoring unit: Through a dedicated software or application, the vital sign data of the user is displayed in real time;

[0045] Data analysis unit: Using artificial intelligence and machine learning algorithms, analyze the monitoring data, identify abnormal situations and issue alarms;

[0046] Data storage unit: Store the monitoring data in the cloud or local server, which is convenient for doctors and guardians to view and analyze at any time;

[0047] The alarm module includes abnormal monitoring and remote notification;

[0048] Abnormal monitoring: When the monitored vital sign data exceeds the normal range, the system will automatically issue an alarm to remind medical staff and guardians;

[0049] Remote notification: Notify through text messages, phone calls or applications to ensure timely response;

[0050] The display module displays the data of the user's heart rate, respiratory rate, and blood oxygen saturation through the corresponding display interface. It can not only monitor the user's situation remotely at home, but also enable nurses in the intensive care unit to understand and monitor the user's condition in a timely manner.

[0051] It should be noted that the users in this example include multiple age groups and can be widely applied to newborns, critically ill children, adults, etc. The system can also be applied to the usage scenarios in the intensive care unit, without any restrictions here. Among them, the sources of the display interface include smart watches, mini-programs, APPs, monitors, etc.

[0052] The data analysis unit also includes a filtering unit, which is divided into primary filtering and secondary filtering. Due to the different degrees of interference caused by the user's crying, turning over, and activities, when the multi-group high-precision sensors in the sensor module output signals, fluctuations are likely to occur. The filtering unit filters the signals sent by the sensor module.

[0053] The primary filtering process is based on Kalman filtering, specifically as follows:

[0054] Time update:

[0055]

[0056] State update:

[0057]

[0058] and respectively represent the posterior state estimation values at the (k - 1)th and kth moments, which are one of the results of filtering, that is, the updated results, also called the optimal estimation.

[0059] The prior state estimation value at the kth moment is the intermediate calculation result of filtering, that is, the result at the kth moment predicted based on the optimal estimation at the previous moment ((k - 1)th moment), and it is the result of the prediction equation.

[0060] P k-1 and P k : represent the posterior estimation covariance at the (k - 1)th and kth moments (that is, and covariance, representing the uncertainty of the state), which is one of the results of filtering.

[0061] The prior estimation covariance at the kth moment ( covariance) is the intermediate calculation result of filtering.

[0062] H: The conversion matrix from the state variable to the measurement (observation), which represents the relationship connecting the state and the observation. In Kalman filtering, it is a linear relationship. It is responsible for converting the m-dimensional measurement value to the n-dimensional one to conform to the mathematical form of the state variable, and it is one of the prerequisite conditions for filtering;

[0063] z k : The measurement value (observation value), which is the input of the filter;

[0064] K k : The filter gain matrix, which is the intermediate calculation result of the filter, the Kalman gain, or the Kalman coefficient;

[0065] A: The state transition matrix, which is actually a conjectural model for the target state transition;

[0066] Q: The process excitation noise covariance (the covariance of the system process), and this parameter is used to represent the error between the state transition matrix and the actual process;

[0067] R: The measurement noise covariance. When the filter is actually implemented, the measurement noise covariance R can generally be observed and is a known condition of the filter;

[0068] B: The matrix that converts the input to the state;

[0069] The residual between the actual observation and the predicted observation, together with the Kalman gain, corrects the prior (prediction) to obtain the posterior.

[0070] The secondary filtering is based on band-pass filtering, specifically:

[0071] Δf = f 2 -f 1

[0072] Δf: The passband;

[0073] f 2 : The high cut-off frequency;

[0074] f 1 : The low cut-off frequency;

[0075] The filtering steps based on the user's vital sign monitoring signal are as follows:

[0076] A1. Obtain the monitoring signal of the user at any time period, monitor it through the sensor module, and transmit it to the data processing module for analysis and processing;

[0077] A2. Filter the noise caused by the user's crying, turning over, and the noise from surrounding people or the environment through the first filtering in the filtering unit to ensure the stability of the transmitted signals of each high-precision sensor in the sensor module;

[0078] A3. By setting the passband, the signal frequencies that the user normally monitors and the frequencies of accidents can pass through normally;

[0079] A4. Set the judgment for monitoring interference signals, that is, when the filtering unit filters the signal, if the time duration of the interference signal exceeds 60 seconds, the alarm module is directly activated.

[0080] According to the signals passing through the passband in A3, a risk rating is carried out. When reaching the critical value of the dangerous situation, the alarm module can be activated to give a warning. All kinds of signals are analyzed by the event tree method. The probability of the top event (fault) is calculated by combining the probabilities of basic events through logic gates (such as AND gates, OR gates, etc.). The event tree analysis is a top-down analysis method used for the probability of risk. Specifically:

[0081] The occurrence probability of the top event connected by an "AND gate" is:

[0082]

[0083] The occurrence probability of the top event connected by an "OR gate" is:

[0084]

[0085] Where: q i : The occurrence probability of the i-th basic event (i = 1, 2, 3, 4....n);

[0086] According to the signal data passing through the passband in A3, statistics are carried out. Taking 0:00 to 24:00 every day as a time cycle, a time interval t is set for the time cycle. In each time interval t, the monitoring frequency is once every 3 minutes. Observe the user's heart rate, blood oxygen saturation and breathing frequency. Every 3 days is a settlement node. If there are more accident signals in the time interval t, the monitoring frequency in this interval is increased to once every 2 minutes. Conversely, if there are more signals with normal vital signs, the monitoring frequency in this interval is reduced to once every 5 minutes;

[0087] Based on the accident occurrence signals in A3 as abnormal signal points, and using each sensor in the sensor module as a test tool. Each sensor is set with a standard value range. Within the standard value range, the alarm module does not give a warning, and only through the display module, information is transmitted to the user. Conversely, outside the standard value range, after filtering first to filter out interference signals, if the monitored value still does not reach the standard value, it is judged as an abnormal signal. Specifically, by calculating the probability of data points under the multivariate Gaussian distribution, abnormal points are identified;

[0088] The independent multivariate Gaussian distribution is based on the fact that the features of each dimension are independent of each other and all follow the normal distribution. Its probability density is:

[0089]

[0090] Among them, μ is a vector composed of the means of the feature values in each dimension, and Σ is the covariance matrix of the variable X. If the variables are independent of each other, the main diagonal is the variance of the feature values in each dimension, and the rest are all 0.

[0091] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered by the scope of the claims and the description of the present invention.

Claims

1. A flexible magnetic earring type intelligent vital signs monitoring system, characterized by: The intelligent vital signs monitoring system includes a sensor module, an ear-attached module, a wireless communication module, a data processing module, an alarm module and a display module; The sensor module is a flexible sensor group, which includes a photoplethysmography sensor, a capacitive respiration sensor, and a pulse oximetry sensor; The ear-attached module is a magnetic earring; The wireless communication module uses Bluetooth or WIFI transmission; The data processing module includes a real-time monitoring unit, a data analysis unit and a data storage unit; The alarm module includes abnormality monitoring and remote notification; The display module displays the user's heart rate, respiratory rate and blood oxygen saturation data through the corresponding display interface.

2. The flexible magnetic earring type intelligent vital signs monitoring system according to claim 1, characterized in that: The real-time monitoring unit: displays the user's vital sign data in real time through dedicated software or application; Data analysis unit: uses artificial intelligence and machine learning algorithms to analyze monitoring data, identify abnormal situations and issue alarms; Data storage unit: stores monitoring data in the cloud or local server, making it easy for doctors and guardians to view and analyze at any time.

3. The flexible magnetic earring type intelligent vital signs monitoring system according to claim 1, characterized in that: Abnormal monitoring: When the monitored vital sign data exceeds the normal range, the system will automatically sound an alarm to alert medical staff and guardians; Remote Notification: Notification via SMS, phone call or app ensures timely response.

4. The flexible magnetic earring type intelligent vital signs monitoring system according to claim 2, characterized in that: The data analysis unit also includes a filtering unit, and the filtering unit is divided into a primary filtering and a secondary filtering.

5. The flexible magnetic earring type intelligent vital signs monitoring system according to claim 4, characterized in that: The filtering steps based on the user's vital signs monitoring signal are as follows: A1. Obtain the monitoring signal of the user at any time period, monitor it through the sensor module, and transmit it to the data processing module for analysis and processing; A2. Filter the noise caused by the user crying, turning over, and surrounding people or environment through the first filtering in the filtering unit to ensure the stability of the transmission signal of each high-precision sensor in the sensor module; A3. By setting the passband, the signal frequency that the user normally monitors and the frequency where accidents occur can pass normally; A4. Set the judgment of interference signal monitoring, that is, when the filtering unit filters the signal, if the interference signal lasts for more than 60 seconds, the alarm module will be directly activated.

6. The flexible magnetic earring type intelligent vital signs monitoring system according to claim 5, characterized in that: The danger level is rated according to the signal passing through the passband in A3. When the critical value of the dangerous situation is reached, the alarm module can be activated to inform and warn.

7. The flexible magnetic earring type intelligent vital signs monitoring system according to claim 6, characterized in that: The signal data passing through the passband in A3 is statistically analyzed, with 0:00 to 24:00 every day as a time cycle, and a time interval t is set in the time cycle. In each time interval t, the user's heart rate, blood oxygen saturation and respiratory rate are observed at an interval frequency of once every 3 minutes.

8. The flexible magnetic earring type intelligent vital signs monitoring system according to claim 7, characterized in that: By setting the time interval, the settlement nodes are counted, and every 3 days is a settlement node. If there are more accident signals within the time interval t, the interval frequency within the interval will increase to once every 2 minutes. Conversely, if there are more signals with normal life characteristics, the interval frequency within the interval will be reduced to once every 5 minutes.

9. The flexible magnetic earring type intelligent vital sign monitoring system according to claim 8, characterized in that: Based on the accident occurrence signal in A3 as the abnormal signal point, each sensor in the sensor module is used as a test tool, and each sensor is set with a standard value range.

10. The flexible magnetic earring type intelligent vital signs monitoring system according to claim 9, characterized in that: According to the setting of the standard value range, within the standard value range, the alarm module does not issue an early warning and only transmits information to the user through the display module. On the contrary, outside the standard value range, the interference signal is first filtered out. If the monitored value still does not reach the standard value, it is judged as an abnormal signal. Specifically, the abnormal point is identified by calculating the probability of the data point under the multivariate Gaussian distribution.

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