Medical intelligent early warning wrist strap integrating positioning and physiological parameter monitoring

Abstract

The invention relates to the technical field of medical wearable equipment, in particular to a medical intelligent early warning wrist strap integrating positioning and physiological parameter monitoring, which comprises a wrist strap main body, a functional module and an analysis module, the analysis module comprises a modeling unit and a judgment unit, the modeling unit divides the health state of a patient into three health levels according to historical medical data in the medical background management system, and determines a score model and score intervals corresponding to the three health levels according to a plurality of historical medical data groups of the patient corresponding to each health level. According to the method, the scoring system capable of comprehensively evaluating the health state of the patient according to the multi-dimensional data is constructed, the limitation that a traditional single parameter and a single threshold cannot adapt to different states of multiple patients in the actual use process is broken through, and the accuracy of a monitoring result is improved.

Description

Technical Field

[0001] This invention relates to the field of medical wearable device technology, and in particular to a medical smart early warning wristband that integrates positioning and physiological parameter monitoring. Background Technology

[0002] Medical smart early warning wristbands are commonly used in hospitals for real-time management and health alerts for special patients with cognitive impairments, mobility difficulties, or unstable conditions. Currently, hospital management of special patients mainly relies on manual patrols and single-function devices.

[0003] Existing positioning wristbands mostly use RFID or GPS technology, which can only track patient location and cannot obtain patient health status. Meanwhile, physiological monitoring devices are mostly independent portable instruments without positioning capabilities and require active patient cooperation. When patients go missing or experience sudden illness, medical staff cannot obtain crucial information immediately, leading to delays in rescue or treatment, resulting in low management efficiency and significant safety hazards. Furthermore, existing products generally suffer from high power consumption, poor battery life, insufficient wearing comfort, and ease of disassembly by patients, making it difficult to meet the long-term, stable usage needs of hospitals.

[0004] Therefore, the existing technology has defects and urgently needs improvement. Summary of the Invention

[0005] The purpose of this invention is to provide a medical intelligent early warning wristband that integrates positioning and physiological parameter monitoring. This addresses the shortcomings of existing technologies, which often employ RFID or GPS, enabling only patient location tracking but not monitoring of health status. Furthermore, physiological monitoring devices are mostly independent portable instruments lacking positioning capabilities and requiring active patient cooperation. When patients go missing or experience sudden illness, medical staff cannot obtain crucial information immediately, leading to delays in rescue or treatment, resulting in low management efficiency and significant safety hazards. In addition, existing products generally suffer from high power consumption, poor battery life, insufficient wristband comfort, and ease of disassembly by patients, making it difficult to meet the long-term, stable usage needs of hospitals.

[0006] This invention provides a medical smart early warning wristband that integrates positioning and physiological parameter monitoring, comprising: The main body of the wristband is used to secure the warning wristband. The functional modules include: a positioning unit for acquiring the wearer's location information, a physiological parameter monitoring unit for detecting the wearer's physiological parameters, a power supply unit for providing power to the warning wristband, a wireless communication unit for interconnecting with the hospital's intranet data, an alarm unit for issuing alarms, and an identity recognition unit for identifying the wearer's identity. The analysis module includes modeling units and decision units, wherein: The modeling unit is set in the medical back-end management system. Based on the historical medical data in the medical back-end management system, the patient's health status is divided into three health levels: sub-health, sub-clinical, and clinical disease status. Each health level corresponds to several groups of historical medical data of patients. The scoring model and the scoring intervals corresponding to the three health levels are determined based on the several groups of historical medical data of patients corresponding to each health level. The determination unit determines the wearer's health status based on the health status score. In response to the health status being subclinical, the detection frequency is adjusted based on the health status score and the first preset score.

[0007] As a preferred technical solution for a medical intelligent early warning wristband that integrates positioning and physiological parameter monitoring, the modeling unit is used to classify the patient's health status into three levels—sub-health, subclinical, and clinically ill—based on historical medical data in the medical back-end management system. This requires the following steps: Acquire historical medical data from the medical back-end management system; the historical medical data includes: changes in historical physiological parameters, changes in historical location distance, and historical medical information within a unit of time; the historical medical information includes: underlying disease type, past medical history, and medical order thresholds for physiological parameters; Based on historical medical information, the historical medical data is divided into three health levels: sub-health state, subclinical state, and clinical onset state.

[0008] As a preferred technical solution for a medical intelligent early warning wristband that integrates positioning and physiological parameter monitoring, the historical medical data is divided into three health levels based on historical medical information, including: The health status of patients who have no recurrence of disease during the recovery period and whose final data record is discharge is recorded as sub-health status, and the historical medical data corresponding to the sub-health status is recorded as sub-health status data. The health status of patients who experienced disease recurrence during the disease recovery period and whose final data record was discharged from the hospital is recorded as subclinical status, and the historical medical data corresponding to the subclinical status is recorded as subclinical status data. The health status of patients whose disease worsens during the recovery period is recorded as the clinical disease status, and the historical medical data corresponding to the clinical disease status is recorded as the clinical disease status data.

[0009] As a preferred technical solution for a medical intelligent early warning wristband integrating positioning and physiological parameter monitoring, the modeling unit determines the scoring model and the scoring intervals corresponding to the three health levels based on several groups of historical medical data of patients corresponding to each health level, including: For a single health level, all historical medical data corresponding to that health level are preprocessed to obtain standard historical medical data. The changes in historical physiological parameters, the distance of historical location changes, and historical medical information in each group of standard historical medical data are mapped to standardized scores for changes in physiological parameters, distance of historical location changes, and historical medical information, respectively. Based on the standardized scores of the changes in physiological parameters, the distance of historical location changes, and historical medical information, and combined with the multiple linear regression analysis method, the initial weight coefficients of the changes in physiological parameters, the distance of historical location changes, and historical medical information are calculated. The above steps are repeated to obtain the initial weight coefficients of the changes in physiological parameters, the distance of historical location changes, and historical medical information for each health level. The initial weighting coefficients of physiological parameter changes, historical location change distances, and historical medical information for the three health levels are summarized. The weights are optimized through cross-validation to determine the scoring model and the score intervals corresponding to sub-health, subclinical, and clinical disease states.

[0010] As a preferred technical solution for a medical intelligent early warning wristband that integrates positioning and physiological parameter monitoring, the determination unit determines the wearer's health status based on the health status score. In response to the health status score being greater than or equal to the first preset score, the wearer's health status is determined to be sub-healthy. In response to the health status score being less than the first preset score but greater than the second preset score, the wearer's health status is determined to be subclinical, and the analysis module adjusts the detection frequency based on the health status score and the first preset score; In response to the health status score being less than or equal to the second preset score, the wearer's health status is determined to be a clinical illness state, and a pop-up window and sound reminder are sent to the medical staff's mobile APP or nurse station terminal through the hospital intranet.

[0011] As a preferred technical solution for a medical intelligent early warning wristband that integrates positioning and physiological parameter monitoring, the first preset score is the boundary value between the score range of sub-health state and the score range of subclinical state. The second preset score is the boundary value between the score range of the subclinical state and the score range of the clinical disease state.

[0012] As a preferred technical solution for a medical intelligent early warning wristband that integrates positioning and physiological parameter monitoring, the analysis module adjusts the detection frequency according to the difference between the health status score and the first preset score, records the difference between the first preset score and the health status score as the standard deviation, and increases the detection frequency according to the standard deviation. The larger the standard deviation, the greater the increase in the detection frequency.

[0013] As a preferred technical solution for a medical intelligent early warning wristband that integrates positioning and physiological parameter monitoring, the wristband body has an anti-disassembly buckle, which can only be opened with a special key. The anti-disassembly buckle is also equipped with a trigger sensor. In response to the buckle being opened abnormally, the trigger sensor is triggered, and the wireless communication module sends a notification of the buckle being opened abnormally and the positioning information to the medical management center. The positioning unit adopts dual-mode positioning technology of RFID and Bluetooth. The analysis module selects the positioning mode according to the positioning information and obtains positioning data through RFID in response to the wearer being located inside the building. In response to the wearer's location being outside the building, Bluetooth is used for positioning.

[0014] As a preferred technical solution for a medical intelligent early warning wristband that integrates positioning and physiological parameter monitoring, the functional module further includes a wireless communication unit, an alarm unit, and an identity recognition unit, wherein: The wireless communication unit is equipped with an NB-IoT communication module to achieve data connection with the hospital's intranet and transmit data of the medical intelligent early warning wristband during operation. The alarm unit includes: a buzzer and an LED indicator light disposed within the main body of the wristband; the buzzer emits a sound range of 80-100 decibels. The identity recognition unit has a built-in NFC chip with a reading distance of 2-5cm and supports integration with the hospital's HIS system. The identification unit is also equipped with a QR code set on the wristband body.

[0015] Compared with the prior art, the beneficial effects of the present invention are that by uniformly processing the location, physiological parameters and medical information in historical medical data and assigning corresponding weight coefficients to each, the present invention constructs a scoring system that can comprehensively evaluate the health status of patients based on multidimensional data. This overcomes the limitations of traditional single parameters and single thresholds in actual use, which cannot adapt to the different states of multiple patients. The present invention can dynamically adapt to the individual differences of different patients (such as underlying diseases, past medical history, etc.) and increase the accuracy of monitoring results.

[0016] For patients in a subclinical state, the detection frequency of each unit in the functional module is dynamically adjusted based on the difference between the health status score and the first preset score. This ensures the detection frequency for patients in a subclinical state while avoiding over-testing for patients with stable health status. It balances the periodicity of the test results while reducing energy consumption and increasing battery life. Attached Figure Description

[0017] Figure 1 This is a structural block diagram of a medical intelligent early warning wristband that integrates positioning and physiological parameter monitoring according to an embodiment of the present invention. Detailed Implementation

[0018] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended only to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0020] Please see Figure 1 As shown, it is a structural block diagram of a medical intelligent early warning wristband integrating positioning and physiological parameter monitoring according to an embodiment of the present invention, including: The main body of the wristband is used to secure the warning wristband. The functional modules include: a positioning unit for acquiring the wearer's location information, a physiological parameter monitoring unit for detecting the wearer's physiological parameters, a power supply unit for providing power to the warning wristband, a wireless communication unit for interconnecting with the hospital's intranet data, an alarm unit for issuing alarms, and an identity recognition unit for identifying the wearer's identity. The analysis module includes modeling units and decision units, wherein: The modeling unit is set in the medical back-end management system. Based on the historical medical data in the medical back-end management system, the health status of patients is divided into three health levels: sub-health, sub-clinical, and clinical disease. Each health level corresponds to several groups of historical medical data of patients. The scoring model and the scoring intervals corresponding to the three health levels are determined based on the several groups of historical medical data of patients corresponding to each health level. The judgment unit determines the wearer's health status based on the health status score. In response to the health status being subclinical, the detection frequency is adjusted based on the health status score and the first preset score.

[0021] Furthermore, the wristband body is made of medical-grade silicone and is adjustable within a range of 150-220mm. The inner side of the wristband body features anti-slip textures, and the surface has several ventilation holes. The wristband body is equipped with an anti-disassembly buckle, which can only be opened with a special key. The buckle also contains a trigger sensor; in response to abnormal opening of the buckle, the sensor is activated, and the wireless communication module sends a notification of the abnormal opening and location information to the medical management center. Functional modules are integrated within the wristband body, including: a positioning unit, a physiological parameter monitoring unit, a power supply unit, a wireless communication unit, an alarm unit, and an identity recognition unit. The positioning unit uses dual-mode positioning technology of RFID and Bluetooth. The analysis module selects the positioning mode based on the location information. If the wearer is located inside a building, RFID is used to acquire positioning data; if the wearer is located outside a building, Bluetooth is used for positioning. The physiological monitoring module detects the wearer's physiological parameters, including heart rate, blood oxygen saturation, and body temperature. The power supply unit is equipped with a 5000mAh rechargeable lithium battery. The wireless communication unit is equipped with an NB-IoT communication module, enabling data connection with the hospital's intranet and transmitting data during the operation of the medical smart warning wristband. The alarm unit includes a buzzer and LED indicator light embedded within the wristband body; the buzzer emits a sound range of 80-100 decibels. The identification unit includes an NFC chip built into the wristband body and a QR code on the wristband body; the NFC chip has a reading distance of 2-5cm and supports integration with the hospital's HIS system.

[0022] Furthermore, the modeling unit is used to classify patients' health status into three levels—sub-health, subclinical, and clinically ill—based on historical medical data in the medical back-end management system. This requires the following steps: Retrieve historical medical data from the medical back-end management system; historical medical data includes: changes in historical physiological parameters within a unit of time, changes in historical location distance, and historical medical information; historical medical information includes: types of underlying diseases, past medical history, and medical order thresholds for physiological parameters; Based on historical medical information, historical medical data is divided into three health levels: sub-health state, subclinical state, and clinical disease state.

[0023] In implementation, the modeling unit obtains the patient's historical medical data within the past three years through the hospital's HIS system interface. The value of the unit of time is configured according to the system; in this embodiment, the unit of time is 1 hour. Historical physiological parameter changes include fluctuations in heart rate, body temperature, and blood oxygen saturation per unit of time. Historical location change distance refers to the distance the patient moves within a unit of time. In the historical medical information, three health levels for underlying disease types are obtained based on expert evaluation. The historical medical data corresponding to the three health levels are divided into three datasets. The K-means algorithm is used to perform clustering verification on the three datasets to ensure that there is no overlap.

[0024] In detail, based on historical medical information, historical medical data is divided into three health levels, including: The health status of patients who have no recurrence of disease during the recovery period and whose final data record is discharge is recorded as sub-health status, and the historical medical data corresponding to the sub-health status is recorded as sub-health status data. The health status of patients who experienced disease recurrence during the disease recovery period and whose final data record was discharged from the hospital is recorded as subclinical status, and the historical medical data corresponding to the subclinical status is recorded as subclinical status data. The health status of patients whose disease worsens during the recovery period is recorded as the clinical disease status, and the historical medical data corresponding to the clinical disease status is recorded as the clinical disease status data.

[0025] It should be noted that "no recurrence of disease" is defined as no follow-up visits during the recovery period (in the medical field, the recovery period is generally 1-3 months after surgery; in this embodiment, it is 2 months). "Recurrence of disease" is defined as physiological parameters exceeding the corresponding thresholds specified in the medical order two or more times during the recovery period, returning to normal after treatment, and no escalation of the condition. "Deterioration of disease" is defined as: during the recovery period, physiological parameters consistently exceeding the corresponding thresholds specified in the medical order, requiring adjustments to the treatment plan, transfer to the intensive care unit, or re-consultation.

[0026] Furthermore, the modeling unit determines the scoring model and the score intervals corresponding to the three health levels based on the historical medical data sets of several groups of patients corresponding to each health level, including: For a single health level, all historical medical data corresponding to that health level are preprocessed to obtain standard historical medical data. The changes in historical physiological parameters, the distance of historical location changes, and historical medical information in each group of standard historical medical data are mapped to standardized scores for changes in physiological parameters, distance of historical location changes, and historical medical information, respectively. Based on the standardized scores of physiological parameter changes, historical location change distance, and historical medical information, and combined with the multiple linear regression analysis method, the initial weight coefficients of physiological parameter changes, historical location change distance, and historical medical information are calculated. The above steps are repeated to obtain the initial weight coefficients of physiological parameter changes, historical location change distance, and historical medical information for each health level. The initial weighting coefficients of physiological parameter changes, historical location change distances, and historical medical information for the three health levels are summarized. The weights are optimized through cross-validation to determine the scoring model and the score intervals corresponding to sub-health, subclinical, and clinical disease states.

[0027] It should be noted that all historical medical data corresponding to this health level undergoes preprocessing, including: removing historical medical data where physiological parameters change by more than or equal to 50% within 10 seconds, and historical medical data where the location signal loss time is more than or equal to 5 minutes. The historical medical data for a single health level is standardized. For example, Z-score standardization is used to map the historical medical data for a single health level to a standardized score. Combined with multiple linear regression analysis, the above steps involve converting specific numerical ranges into standardized values ​​using a specific algorithm, and determining the weighting coefficients for changes in physiological parameters, historical location changes, and historical medical information based on known data. Those skilled in the art will understand that standardizing known datasets and assigning corresponding weighting coefficients to each parameter to evaluate a standard based on the calculated score can be achieved through the above steps. The specific algorithms and regression analysis methods involved are not limited in this embodiment.

[0028] Furthermore, the determination unit determines the wearer's health status based on the health status score. In response to the health status score being greater than or equal to the first preset score, the wearer's health status is determined to be sub-healthy. In response to a health status score that is less than a first preset score but greater than a second preset score, the wearer's health status is determined to be subclinical. The analysis module then adjusts the detection frequency based on the health status score and the first preset score. If the health status score is less than or equal to the second preset score, the wearer's health status is determined to be a clinical illness state. A pop-up window and sound reminder are sent to the medical staff's mobile APP or nurse station terminal through the hospital intranet.

[0029] Furthermore, the first preset score is the boundary value between the score range of sub-health state and the score range of subclinical state; The second preset score is the boundary value between the score range of the subclinical state and the score range of the clinical disease state.

[0030] Furthermore, the analysis module adjusts the detection frequency based on the difference between the health status score and the first preset score. The difference between the first preset score and the health status score is recorded as the standard deviation. The detection frequency is increased based on the standard deviation, and the larger the standard deviation, the greater the increase in the detection frequency.

[0031] In implementation, the increase in detection frequency based on the standard deviation is determined according to the actual situation. This invention provides a method for increasing the detection frequency based on the standard deviation, including: Obtain the range of detection frequencies for each unit in the functional unit, and determine the frequency adjustment range based on the current detection frequency; Calculate the range of differences between the first preset score and the second preset score, and the standard deviation will definitely fall within the range of differences; Establish a mapping relationship between the difference range and the frequency adjustment range, satisfying the condition that the larger the standard deviation, the greater the increase in the detection frequency.

[0032] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. Those skilled in the art can make other variations or modifications based on the above description. It is neither necessary nor possible to exhaustively describe all embodiments here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the claims of the present invention.

Claims

1. A medical intelligent early warning wristband integrating positioning and physiological parameter monitoring, characterized in that, include: The main body of the wristband is used to secure the warning wristband. The functional modules include: a positioning unit for acquiring the wearer's location information, a physiological parameter monitoring unit for detecting the wearer's physiological parameters, a power supply unit for providing power to the warning wristband, a wireless communication unit for interconnecting with the hospital's intranet data, an alarm unit for issuing alarms, and an identity recognition unit for identifying the wearer's identity. The analysis module includes modeling units and decision units, wherein: The modeling unit is set in the medical back-end management system. Based on the historical medical data in the medical back-end management system, the patient's health status is divided into three health levels: sub-health, sub-clinical, and clinical disease status. Each health level corresponds to several groups of historical medical data of patients. The scoring model and the scoring intervals corresponding to the three health levels are determined based on the several groups of historical medical data of patients corresponding to each health level. The determination unit determines the wearer's health status based on the health status score. In response to the health status being subclinical, the detection frequency is adjusted based on the health status score and the first preset score.

2. The medical intelligent early warning wristband integrating positioning and physiological parameter monitoring according to claim 1, characterized in that, The modeling unit is used to classify patients' health status into three levels—sub-health, subclinical, and clinically ill—based on historical medical data in the medical back-end management system. The following steps are required: Retrieve historical medical data from the medical back-end management system; The historical medical data includes: changes in historical physiological parameters, changes in historical location distance, and historical medical information within a unit of time; the historical medical information includes: the type of underlying disease, past medical history, and medical advice thresholds for physiological parameters. Based on historical medical information, the historical medical data is divided into three health levels: sub-health state, subclinical state, and clinical onset state.

3. The medical intelligent early warning wristband integrating positioning and physiological parameter monitoring according to claim 2, characterized in that, The historical medical data is divided into three health levels based on historical medical information, including: The health status of patients who have no recurrence of disease during the recovery period and whose final data record is discharge is recorded as sub-health status, and the historical medical data corresponding to the sub-health status is recorded as sub-health status data. The health status of patients who experienced disease recurrence during the disease recovery period and whose final data record was discharged from the hospital is recorded as subclinical status, and the historical medical data corresponding to the subclinical status is recorded as subclinical status data. The health status of patients whose disease worsens during the recovery period is recorded as the clinical disease status, and the historical medical data corresponding to the clinical disease status is recorded as the clinical disease status data.

4. The medical intelligent early warning wristband integrating positioning and physiological parameter monitoring according to claim 3, characterized in that, The modeling unit determines the scoring model and the scoring intervals corresponding to the three health levels based on the historical medical data sets of several groups of patients corresponding to each health level, including: For a single health level, all historical medical data corresponding to that health level are preprocessed to obtain standard historical medical data. The changes in historical physiological parameters, the distance of historical location changes, and historical medical information in each group of standard historical medical data are mapped to standardized scores for changes in physiological parameters, distance of historical location changes, and historical medical information, respectively. Based on the standardized scores of the changes in physiological parameters, the distance of historical location changes, and historical medical information, and combined with the multiple linear regression analysis method, the initial weight coefficients of the changes in physiological parameters, the distance of historical location changes, and historical medical information are calculated. The above steps are repeated to obtain the initial weight coefficients of the changes in physiological parameters, the distance of historical location changes, and historical medical information for each health level. The initial weighting coefficients of physiological parameter changes, historical location change distances, and historical medical information for the three health levels are summarized. The weights are optimized through cross-validation to determine the scoring model and the score intervals corresponding to sub-health, subclinical, and clinical disease states.

5. The medical intelligent early warning wristband integrating positioning and physiological parameter monitoring according to claim 4, characterized in that, The determination unit determines the wearer's health status based on the health status score. In response to the health status score being greater than or equal to the first preset score, the wearer's health status is determined to be sub-healthy. In response to the health status score being less than the first preset score but greater than the second preset score, the wearer's health status is determined to be subclinical, and the analysis module adjusts the detection frequency based on the health status score and the first preset score; In response to the health status score being less than or equal to the second preset score, the wearer's health status is determined to be a clinical illness state, and a pop-up window and sound reminder are sent to the medical staff's mobile APP or nurse station terminal through the hospital intranet.

6. The medical intelligent early warning wristband integrating positioning and physiological parameter monitoring according to claim 5, characterized in that, The first preset score is the boundary value between the score range of sub-health state and the score range of subclinical state. The second preset score is the boundary value between the score range of the subclinical state and the score range of the clinical disease state.

7. The medical intelligent early warning wristband integrating positioning and physiological parameter monitoring according to claim 5, characterized in that, The analysis module adjusts the detection frequency based on the difference between the health status score and the first preset score. The difference between the first preset score and the health status score is recorded as the standard deviation. The detection frequency is increased based on the standard deviation, and the larger the standard deviation, the greater the increase in the detection frequency.

8. The medical intelligent early warning wristband integrating positioning and physiological parameter monitoring according to claim 1, characterized in that, The wristband body has an anti-disassembly buckle, which can only be opened with a special key. The anti-disassembly buckle is also equipped with a trigger sensor. In response to the buckle being opened abnormally, the trigger sensor is triggered, and the wireless communication module sends a notification of the buckle being opened abnormally and location information to the medical management center.

9. The medical intelligent early warning wristband integrating positioning and physiological parameter monitoring according to claim 1, characterized in that, The positioning unit adopts dual-mode positioning technology of RFID and Bluetooth. The analysis module selects the positioning mode according to the positioning information. In response to the wearer being located inside the building, the positioning data is obtained through RFID. In response to the wearer's location being outside the building, Bluetooth is used for positioning.

10. The medical intelligent early warning wristband integrating positioning and physiological parameter monitoring according to claim 1, characterized in that, The functional module also includes a wireless communication unit, an alarm unit, and an identity recognition unit, wherein: The wireless communication unit is equipped with an NB-IoT communication module to achieve data connection with the hospital's intranet and transmit data of the medical intelligent early warning wristband during operation. The alarm unit includes: a buzzer and an LED indicator light disposed within the main body of the wristband; the buzzer emits a sound range of 80-100 decibels. The identity recognition unit has a built-in NFC chip with a reading distance of 2-5cm and supports integration with the hospital's HIS system. The identification unit is also equipped with a QR code set on the wristband body.

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