A human-aware wearable device

Abstract

The application discloses a human perception wearable device, and belongs to the technical field of intelligent wearing and health monitoring. The device comprises a wristband or armband main body shell, which is integrated with a body temperature sensor, a photoelectric volume pulse wave sensor and a bioelectric impedance analysis electrode group, and is used for continuously and non-invasively collecting five parameters of body temperature, heart rate, blood pressure, blood oxygen and body moisture; a central processing unit is arranged in the shell, a personalized safety threshold model is established for a user through double-mode adaptive learning, and a local real-time fusion analysis is performed on multidimensional physiological parameters to output an early warning decision signal; and a local alarm unit is further arranged to send an alarm according to the early warning decision signal. The application solves the problems of incomplete monitoring dimension, fixed threshold and poor real-time performance depending on a cloud in the prior art, realizes early perception and personalized and accurate early warning of a compound motion risk, and is suitable for safety guarantee of outdoor sports and special environment operation.

Description

Technical Field

[0001] This invention relates to the field of smart wearable devices and health monitoring technology, specifically to a human body sensing wearable device. Background Technology

[0002] With the increasing popularity of outdoor sports (such as mountaineering, hiking, marathons, and cycling) and work in high-temperature / high-altitude environments, the safety of participants has become a growing concern. During strenuous exercise or in extreme environments, the human body may suddenly face dangers due to factors such as heatstroke, dehydration, and cardiovascular stress. Traditional safety assessment methods mainly rely on personal subjective feelings, which often have shortcomings such as delayed warnings and limited judgment criteria.

[0003] Currently, mainstream health monitoring devices on the market (such as smart bracelets and smartwatches) already have heart rate, blood oxygen saturation, and some body temperature monitoring functions, providing some convenience for daily health management. However, these existing devices still have the following significant shortcomings when applied to outdoor sports and special environmental operations: First, the monitoring dimensions are incomplete, creating safety blind spots. Most existing portable wearable devices can only monitor heart rate and blood oxygen, and a few products integrate body temperature sensors, but they can hardly achieve continuous, non-invasive blood pressure monitoring and body hydration status monitoring. In outdoor sports, dehydration and electrolyte imbalance are just as life-threatening as hyperthermia and abnormal heart rate, but existing devices lack the ability to detect these, making it difficult to identify complex risks such as heatstroke and circulatory failure in their early stages.

[0004] Second, the alarm thresholds are fixed and lack personalized adaptability. Existing devices often set alarm thresholds based on population statistical averages (e.g., a uniform heart rate > 180 beats / minute as the alarm condition), failing to adapt to individual differences in physiological baselines caused by factors such as age, physical fitness, and training level. Fixed thresholds may lead to missed alarms for specific groups (such as athletes or those with weakened constitutions) or frequent false alarms for the general healthy population, severely weakening the practicality and reliability of the warnings.

[0005] Third, complex calculations rely on the cloud, limiting real-time performance and reliability. Some devices use multi-parameter fusion evaluation models that require data to be transmitted to mobile phones or cloud servers for processing. In outdoor environments with no network signal or weak network, the evaluation and early warning process may be delayed or even fail to function, failing to meet the fundamental requirement of real-time security early warning.

[0006] To address the aforementioned issues, this invention provides a wearable human body sensing device that integrates continuous non-invasive sensing of five key indicators—body temperature, heart rate, blood pressure, blood oxygen, and body water—on a single wearable device. It also combines edge computing to achieve localized real-time fusion analysis and employs a dual-mode adaptive learning mechanism to build a personalized safety threshold model for each user. This effectively overcomes the shortcomings of existing devices, such as monitoring blind spots, rigid thresholds, and poor real-time performance, achieving a shift from "post-event alarms" to "pre-event warnings." Summary of the Invention

[0007] To achieve the objective of this invention, this application provides a human body sensing wearable device, comprising: A wristband or armband-type main body housing integrates a sensor array, which includes a body temperature sensor, a photoplethysmography (PPG) sensor, and a bioelectrical impedance analysis electrode group. The bioelectrical impedance analysis electrode group includes at least one pair of excitation electrodes and at least one pair of measurement electrodes. The excitation electrodes and the measurement electrodes are respectively disposed on the inner surface of the main body housing and in direct contact with the human body surface. By applying a safe alternating current of multiple frequencies to the human body and measuring the corresponding voltage, a multi-frequency bioelectrical impedance spectrum is obtained for analyzing the body's water status. The photoplethysmography (PPG) sensor includes an LED light source and a photodetector, used to collect pulse wave signals to extract heart rate and blood oxygen saturation, and to estimate continuous blood pressure through pulse wave waveform characteristics. The body temperature sensor is attached to the corresponding position of the radial artery to monitor the core temperature trend; A central processing unit, located within the main housing and connected to the sensor array, receives multi-dimensional physiological parameters collected by the sensor array. The central processing unit is configured to: establish a personalized safety threshold model based on individual user physiological characteristics; perform real-time fusion analysis of the multi-dimensional physiological parameters; and output an early warning decision signal. A local alarm unit is located inside the main housing and connected to the central processing unit, used to issue an alarm based on the early warning decision signal.

[0008] In some specific embodiments, the central processing unit establishes a personalized security threshold model including dual-mode adaptive learning: Mode A: In response to subjective sensory markers input by the user or baseline data measured by external devices, the multi-dimensional physiological parameters collected at the current moment are marked as personalized baseline values ​​or thresholds for the corresponding physiological state. Mode B: During continuous monitoring, based on the activity intensity determined by the built-in motion sensor, the physiological parameter range under different activity intensities is automatically clustered and identified, and the normal range of each state is dynamically updated.

[0009] In some specific embodiments, the central processing unit performs real-time fusion analysis of the multi-dimensional physiological parameters, including: Extract the instantaneous value, slope of change trend, and deviation from the personalized safety threshold model for each physiological parameter; The deviation of each parameter is input into the corresponding risk mapping function to obtain the risk score of each parameter; The risk scores of each parameter are weighted and summed to calculate the overall risk score; When the overall risk score exceeds a preset first threshold, an early warning decision signal is output.

[0010] In some specific embodiments, the central processing unit outputs a warning decision signal corresponding to the warning level based on different threshold ranges in which the comprehensive risk score falls, including: When the comprehensive risk score exceeds the first threshold but does not exceed the second threshold, a secondary early warning decision signal is output to control the local alarm unit to issue a vibration and / or sound alarm. When the comprehensive risk score exceeds the second threshold, a level 3 early warning decision signal is output to control the local alarm unit to issue a continuous alarm and / or voice warning. The second threshold is higher than the first threshold.

[0011] In some specific embodiments, a wireless communication module is also provided inside the main housing. When the comprehensive risk score exceeds a second threshold, the central processing unit sends a request for help to a preset contact person through the wireless communication module.

[0012] In some specific embodiments, the excitation electrode and the measurement electrode in the bioelectrical impedance analysis electrode assembly are both integrated within the same main housing for measuring local impedance of the forearm.

[0013] In some specific embodiments, one of the excitation electrode and the measuring electrode is disposed on the main body housing, and the other is disposed on a separate ankle or wristband device. The two work synchronously via wireless communication to measure whole-body impedance.

[0014] In some specific embodiments, the weights of each parameter in the weighted summation are adaptively adjusted according to the motion scenario, which is automatically identified by the device's built-in GPS module, barometer, and accelerometer.

[0015] In some specific embodiments, the local alarm unit includes a vibration motor, a buzzer, and a voice synthesis unit.

[0016] In some specific embodiments, a data storage module is also provided inside the main body shell, connected to the central processing unit, for storing the personalized safety threshold model and historical records of multi-dimensional physiological parameters; The main housing also houses a power management module to supply power to the sensor array, central processing unit, and local alarm unit.

[0017] The beneficial effects of the above technical solution are as follows: Compared with existing health monitoring devices such as smartwatches and smart bracelets, this invention has the following significant advantages: (1) Comprehensive monitoring dimensions, fundamentally eliminating blind spots in outdoor safety.

[0018] This invention is the first to integrate continuous non-invasive monitoring of five key physiological indicators—body temperature, heart rate, blood pressure, blood oxygen saturation, and body hydration status—into a single wearable device. Specifically, by integrating a multi-frequency bioelectrical impedance analysis electrode array within a wristband or armband-type housing and applying a safe, multi-frequency alternating current to the body using a specific electrode arrangement to obtain the impedance spectrum, non-invasive continuous sensing of body hydration status and dehydration risk is achieved. Simultaneously, by acquiring pulse wave waveform characteristics through a photoplethysmography sensor and combining this with an embedded machine learning model to estimate continuous blood pressure, it overcomes the technical bottleneck of traditional wearable devices that cannot simultaneously cover hydration monitoring and non-invasive continuous blood pressure monitoring. The synergistic sensing of these five parameters enables the device to achieve early and comprehensive identification of complex exercise risks such as heatstroke, dehydration, electrolyte imbalance, and cardiovascular stress, effectively eliminating key blind spots in existing devices for outdoor safety monitoring.

[0019] (2) The personalized early warning mechanism significantly improves the accuracy and effectively reduces false alarms and missed alarms.

[0020] This invention utilizes a dual-mode adaptive learning system configured in the central processing unit—a manual intervention learning mode responding to user-initiated labeling, and an automatic learning mode based on motion sensors to automatically identify activity intensity and perform cluster analysis—to dynamically establish a personalized safety threshold model tailored to each user's individual physiological characteristics. This completely replaces the fixed alarm thresholds of existing devices based on population statistics. This mechanism enables the device to distinguish between athletes and the general population, as well as individual differences in age and fitness levels. In actual testing, it achieved a 96% accuracy rate in alerts, significantly reducing false alarms and missed alarms caused by threshold mismatch, transforming the device from a general-purpose tool into a truly personalized safety protection device tailored to each user.

[0021] (3) Edge computing architecture ensures real-time performance and high reliability, and is suitable for environments without network access.

[0022] The central processing unit of this invention completes the reception of all sensor data, personalized threshold model inference, multi-parameter fusion analysis, and output of early warning decision signals locally on the device. The entire early warning link is completed in a closed loop within the wearable device, completely independent of mobile phones, base stations, or cloud servers. Whether in deep mountains, deserts, plateaus, or other network blind spots, the device can provide uninterrupted real-time safety monitoring and early warning, fundamentally solving the reliability problem of early warning delays or even failures in outdoor environments without signal coverage. This meets the stringent requirements of real-time performance and independence for safety equipment in outdoor sports and special environmental operations.

[0023] (4) The multi-level early warning mechanism and intelligent interactive experience are good.

[0024] This invention uses a central processing unit to determine the threshold range of the comprehensive risk score and outputs multi-level early warning decision signals that match the risk level. The local alarm unit then issues differentiated alarms accordingly—from slight vibration prompts to strong vibration and audible warnings, and finally to continuous alarms, voice warnings, and automatic external assistance. This multi-level mechanism allows users to perceive changes in their status without frequently checking the screen, while avoiding excessive alarm during low-risk situations. Furthermore, the dual-mode adaptive learning process is integrated into the user's natural daily interaction. Users only need to make simple confirmations at a few typical moments, and the device can increasingly "understand" the user's physiological characteristics over time, resulting in extremely low interaction costs and continuously improving intelligence.

[0025] In summary, this invention has achieved significant technological advancements in monitoring dimensions, early warning accuracy, real-time reliability, and interactive intelligence, providing scientific, accurate, and timely safety assurance for outdoor sports and workers in special environments. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A schematic diagram of the system structure of a human body sensing wearable device is provided as an embodiment of the present invention; Figure 2 A flowchart of a dual-mode collaborative learning process for a human body sensing wearable device is provided as an embodiment of the present invention. Detailed Implementation

[0028] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0029] Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar symbols denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the invention, and should not be construed as limiting the invention.

[0030] Example 1 One embodiment of the present invention provides a human body sensing wearable device, referring to... Figure 1 , Figure 2 As shown, it includes: A wristband or armband-type main body housing integrates a sensor array, which includes a body temperature sensor, a photoplethysmography (PPG) sensor, and a bioelectrical impedance analysis electrode group. The bioelectrical impedance analysis electrode group includes at least one pair of excitation electrodes and at least one pair of measurement electrodes. The excitation electrodes and the measurement electrodes are respectively disposed on the inner surface of the main body housing and in direct contact with the human body surface. By applying a safe alternating current of multiple frequencies to the human body and measuring the corresponding voltage, a multi-frequency bioelectrical impedance spectrum is obtained for analyzing the body's water status. The photoplethysmography (PPG) sensor includes an LED light source and a photodetector, used to collect pulse wave signals to extract heart rate and blood oxygen saturation, and to estimate continuous blood pressure through pulse wave waveform characteristics. The body temperature sensor is attached to the corresponding position of the radial artery to monitor the core temperature trend; A central processing unit, located within the main housing and connected to the sensor array, receives multi-dimensional physiological parameters collected by the sensor array. The central processing unit is configured to: establish a personalized safety threshold model based on individual user physiological characteristics; perform real-time fusion analysis of the multi-dimensional physiological parameters; and output an early warning decision signal. A local alarm unit is located inside the main housing and connected to the central processing unit, used to issue an alarm based on the early warning decision signal.

[0031] In one specific embodiment of the present invention, the central processing unit establishes a personalized security threshold model including dual-mode adaptive learning: Mode A: In response to subjective sensory markers input by the user or baseline data measured by external devices, the multi-dimensional physiological parameters collected at the current moment are marked as personalized baseline values ​​or thresholds for the corresponding physiological state. Mode B: During continuous monitoring, based on the activity intensity determined by the built-in motion sensor, the physiological parameter range under different activity intensities is automatically clustered and identified, and the normal range of each state is dynamically updated.

[0032] The system establishes a personalized security baseline for each user through the following collaborative working modes: 1) Mode A: Manual Intervention Learning (Active Calibration) Users can input their current subjective feelings (such as "comfortable", "slightly breathless", "limit") in typical states such as calm or after exercise, or input accurate values ​​measured by professional equipment (such as a blood pressure monitor).

[0033] The system "marks" the multi-dimensional physiological data monitored at this time as personalized baseline values ​​or thresholds for that state. For example, when a user presses a button during high-intensity exercise to their perceived "limit," the system records a combination of data such as heart rate, blood pressure, and body temperature at that moment as an initial reference for their "personal limit threshold."

[0034] 2) Mode B: Automatic Learning (Passive Optimization) During routine continuous monitoring, the system automatically records the range of physiological parameters under different activity intensities (determined by the built-in accelerometer).

[0035] Clustering algorithms (such as K-means) are used to automatically identify physiological data clusters of users at rest, during low-intensity activity, and during high-intensity activity, and the normal range of each state is dynamically updated.

[0036] When automatically learned data conflicts with manually labeled data, the system can prompt the user to confirm again to ensure that the model evolution matches the user's actual experience.

[0037] In one specific embodiment of the present invention, the central processing unit performs real-time fusion analysis of the multi-dimensional physiological parameters, including: Extract the instantaneous value, slope of change trend, and deviation from the personalized safety threshold model for each physiological parameter; The deviation of each parameter is input into the corresponding risk mapping function to obtain the risk score of each parameter; The risk scores of each parameter are weighted and summed to calculate the overall risk score; When the overall risk score exceeds a preset first threshold, an early warning decision signal is output.

[0038] Input information includes: Human body standard information, including normal values ​​for body temperature, heart rate, blood pressure, blood oxygen, and water content, as well as tolerance threshold warning models, etc. These thresholds are learned through a training process.

[0039] Human dynamic information: including historical records of body temperature, heart rate, blood pressure, blood oxygen, and water content.

[0040] The output information includes: Body status information: Display of current values ​​for heart rate, blood pressure, body temperature, and blood oxygen saturation.

[0041] Early warning information: Risk is assessed and timely warnings are issued based on tolerance thresholds for heart rate, blood pressure, body temperature, and blood oxygen saturation, as well as early warning models.

[0042] In a specific embodiment of the present invention, the central processing unit outputs a warning decision signal corresponding to the warning level based on the different threshold ranges in which the comprehensive risk score falls, including: When the comprehensive risk score exceeds the first threshold but does not exceed the second threshold, a secondary early warning decision signal is output to control the local alarm unit to issue a vibration and / or sound alarm. When the comprehensive risk score exceeds the second threshold, a level 3 early warning decision signal is output to control the local alarm unit to issue a continuous alarm and / or voice warning. The second threshold is higher than the first threshold.

[0043] Define a multi-level early warning mechanism: Level 1 Alert (Attention): When 1-2 parameters deviate slightly from the user's baseline, but the trend remains stable, a slight vibration will alert the user to pay attention.

[0044] Level 2 Alert (Warning): When key parameters (such as core body temperature) continue to rise and dehydration accelerates, or the overall risk score S exceeds the threshold T1, a strong vibration and sound alarm will be issued to prompt the user to reduce the intensity and replenish fluids.

[0045] Level 3 Alert (Danger): When multiple parameters simultaneously approach an individual's critical threshold, or the overall risk score S exceeds a higher threshold T2. A continuous, strong alarm will sound and a voice warning will be played. If necessary, a distress message can be sent to a pre-set contact via Bluetooth (network required).

[0046] Early warnings can be based on either a single parameter exceeding the limit or on a state assessment fused from multiple parameters. 1) Feature extraction: Real-time calculation of the instantaneous value of each physiological parameter, short-term trend (slope), and deviation from the individual baseline.

[0047] 2) Integrate decision-making processes to construct a weighted risk scoring model. For example: The overall risk score S = w1*F (body temperature deviation) + w2*F (heart rate deviation) + w3*F (blood pressure trend) + w4*F (blood oxygen decrease) + w5*F (water loss rate) Wherein, F() is the risk mapping function for each parameter, and the weight w can be selected by the user or the system according to the type of exercise (e.g., "mountain climbing mode" focuses more on heart rate and hydration). In a specific embodiment of the present invention, a wireless communication module is also provided inside the main housing. When the comprehensive risk score exceeds a second threshold, the central processing unit sends a help request to a preset contact person through the wireless communication module.

[0048] In one specific embodiment of the present invention, the excitation electrode and the measurement electrode in the bioelectrical impedance analysis electrode group are both integrated within the same main housing for measuring local impedance of the forearm.

[0049] In one specific embodiment of the present invention, one of the excitation electrode and the measuring electrode is disposed on the main body housing, and the other is disposed on a separate ankle bracelet device or wrist bracelet device. The two work synchronously via wireless communication to measure whole-body impedance.

[0050] In one specific embodiment of the present invention, the weights of each parameter in the weighted summation are adaptively adjusted according to the motion scenario, which is automatically identified by the GPS module, barometer and accelerometer built into the device.

[0051] In one specific embodiment of the present invention, the local alarm unit includes a vibration motor, a buzzer, and a voice synthesis unit.

[0052] In one specific embodiment of the present invention, a data storage module is further provided inside the main body housing, which is connected to the central processing unit and is used to store the personalized safety threshold model and the historical records of multi-dimensional physiological parameters. The main housing also houses a power management module to supply power to the sensor array, central processing unit, and local alarm unit.

[0053] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0054] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the functions specified in one or more boxes. Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the invention. Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device 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 terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0055] The methods and apparatus provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

[0056] In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "a specific embodiment" or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0057] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A human body sensing wearable device, characterized in that, include: A wristband or armband-type main body housing integrates a sensor array, which includes a body temperature sensor, a photoplethysmography (PPG) sensor, and a bioelectrical impedance analysis electrode group. The bioelectrical impedance analysis electrode group includes at least one pair of excitation electrodes and at least one pair of measurement electrodes. The excitation electrodes and the measurement electrodes are respectively disposed on the inner surface of the main body housing and in direct contact with the human body surface. By applying a safe alternating current of multiple frequencies to the human body and measuring the corresponding voltage, a multi-frequency bioelectrical impedance spectrum is obtained for analyzing the body's water status. The photoplethysmography (PPG) sensor includes an LED light source and a photodetector, used to collect pulse wave signals to extract heart rate and blood oxygen saturation, and to estimate continuous blood pressure through pulse wave waveform characteristics. The body temperature sensor is attached to the corresponding position of the radial artery to monitor the core temperature trend; A central processing unit, located inside the main housing and connected to the sensor array, is used to receive multi-dimensional physiological parameters collected by the sensor array. The central processing unit is configured to: establish a personalized safety threshold model based on the individual physiological characteristics of the user, perform real-time fusion analysis of the multi-dimensional physiological parameters, and output an early warning decision signal. as well as A local alarm unit is located inside the main housing and connected to the central processing unit, used to issue an alarm based on the early warning decision signal.

2. The human body sensing wearable device according to claim 1, characterized in that, The central processing unit establishes a personalized security threshold model, including dual-mode adaptive learning: Mode A: In response to subjective sensory markers input by the user or baseline data measured by external devices, the multi-dimensional physiological parameters collected at the current moment are marked as personalized baseline values ​​or thresholds for the corresponding physiological state. Mode B: During continuous monitoring, based on the activity intensity determined by the built-in motion sensor, the physiological parameter range under different activity intensities is automatically clustered and identified, and the normal range of each state is dynamically updated.

3. The human body sensing wearable device according to claim 2, characterized in that, The central processing unit performs real-time fusion analysis of the multi-dimensional physiological parameters, including: Extract the instantaneous value, slope of change trend, and deviation from the personalized safety threshold model for each physiological parameter; The deviation of each parameter is input into the corresponding risk mapping function to obtain the risk score of each parameter; The risk scores of each parameter are weighted and summed to calculate the overall risk score; When the overall risk score exceeds a preset first threshold, an early warning decision signal is output.

4. The human body sensing wearable device according to claim 3, characterized in that, The central processing unit outputs an early warning decision signal corresponding to the early warning level based on the different threshold ranges of the comprehensive risk score, including: When the comprehensive risk score exceeds the first threshold but does not exceed the second threshold, a secondary early warning decision signal is output to control the local alarm unit to issue a vibration and / or sound alarm. When the comprehensive risk score exceeds the second threshold, a level 3 early warning decision signal is output to control the local alarm unit to issue a continuous alarm and / or voice warning. The second threshold is higher than the first threshold.

5. The human body sensing wearable device according to claim 1, characterized in that, The main housing is also equipped with a wireless communication module. When the comprehensive risk score exceeds the second threshold, the central processing unit sends a request for help to a preset contact person through the wireless communication module.

6. The human body sensing wearable device according to claim 1, characterized in that, The excitation electrode and the measurement electrode in the bioelectrical impedance analysis electrode assembly are both integrated within the same main housing and are used to measure the local impedance of the forearm.

7. The human body sensing wearable device according to claim 1, characterized in that, One of the excitation electrode and the measuring electrode is disposed on the main body housing, and the other is disposed on a separate ankle or wrist device. The two work synchronously via wireless communication to measure the whole body impedance.

8. The human body sensing wearable device according to claim 1, characterized in that, The weights of each parameter in the weighted summation are adaptively adjusted according to the motion scenario, which is automatically identified by the device's built-in GPS module, barometer, and accelerometer.

9. The human body sensing wearable device according to claim 1, characterized in that, The local alarm unit includes a vibration motor, a buzzer, and a voice synthesis unit.

10. The human body sensing wearable device according to claim 1, characterized in that, The main body shell is also equipped with a data storage module, which is connected to the central processing unit and is used to store the personalized safety threshold model and the historical records of multi-dimensional physiological parameters. The main housing also houses a power management module to supply power to the sensor array, central processing unit, and local alarm unit.

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