Health monitoring method, apparatus, device, storage medium, and product

Abstract

The application discloses a health monitoring method, device, equipment, storage medium and product, relates to the technical field of health monitoring, and the method comprises the following steps: continuously collecting signal state parameters generated by user activities in a preset detection range through a wireless sensing module, and acquiring first vital sign parameters from the signal state parameters; when a preset measurement condition is met, collecting impedance data of the user through a bioelectrical impedance measurement module, and obtaining second vital sign parameters according to the impedance data; determining, from the first vital sign parameters, a first vital sign parameter corresponding to a collection time as an aligned first vital sign parameter, the collection time being a time when the bioelectrical impedance measurement module collects the impedance data; performing consistency comparison on the aligned first vital sign parameter and the second vital sign parameter, and obtaining a health monitoring result according to a comparison result.

Description

Technical Field

[0001] This application relates to the field of health monitoring technology, and in particular to a health monitoring method, device, equipment, storage medium and product. Background Technology

[0002] With the increasing demand for health management, bioelectrical impedance analysis (BIA) devices (such as body fat scales) have become common health monitoring tools in homes, providing discrete measurement data such as body composition. Meanwhile, wireless sensing technology based on Wi-Fi channel state information (CSI) is widely researched and applied in the field of vital sign monitoring, such as respiration and heart rate, due to its advantages of non-contact and continuous monitoring.

[0003] Currently, BIA measurement devices and Wi-Fi sensing devices typically operate independently. Existing body fat scales only perform a single measurement when the user steps on the scale, failing to acquire continuous health data between two measurements, resulting in data blind spots in health trend analysis. While independent Wi-Fi sensing systems can continuously monitor vital signs, their data belongs to different systems than BIA impedance data, lacking a unified time-series correlation and cross-validation mechanism. Due to fundamental differences in sampling frequency, physical meaning, and data format, the two types of data cannot be directly fused for analysis, making it difficult to form a complete and reliable assessment of the user's health status.

[0004] Therefore, how to effectively correlate and verify the consistency of continuous vital signs data sensed by Wi-Fi with discrete impedance data of BIA is an urgent problem to be solved. Summary of the Invention

[0005] The main objective of this application is to provide a health monitoring method, device, equipment, storage medium, and product, which aims to solve the technical problem of how to effectively correlate and verify the consistency of continuous vital sign data sensed by Wi-Fi with BIA discrete impedance data.

[0006] To achieve the above objectives, this application proposes a health monitoring method applied to a body fat scale, the body fat scale including a wireless sensing module and a bioelectrical impedance measurement module, the method comprising: The wireless sensing module continuously collects signal status parameters generated by the user's activities within a preset detection range, and obtains first vital sign parameters from each of the signal status parameters. When the preset measurement conditions are met, the user's impedance data is collected through the bioelectrical impedance measurement module, and the second vital sign parameter is obtained based on the impedance data; The first vital sign parameter corresponding to the acquisition time is determined from each of the first vital sign parameters as the aligned first vital sign parameter, wherein the acquisition time is the time when the bioelectrical impedance measurement module acquires the impedance data; The alignment of the first vital sign parameter with the second vital sign parameter is compared for consistency, and the health monitoring result is obtained based on the comparison result.

[0007] In one embodiment, after the step of continuously collecting signal state parameters generated by the user's activities within a preset detection range through the wireless sensing module, the method further includes: The user's gait and body shape characteristics are identified based on the signal state parameters, and a first user identifier is determined based on the gait and body shape characteristics. The segment impedance distribution characteristics of the user are identified based on the impedance data, and the second user identifier is determined based on the segment impedance distribution characteristics. When the first user identifier matches the second user identifier, the user is confirmed to be the user corresponding to the first user identifier.

[0008] In one embodiment, the step of acquiring the user's impedance data through the bioelectrical impedance measurement module when preset measurement conditions are met includes: When the load pressure is detected to reach a preset threshold, the user's static feature data is extracted from each of the signal state parameters; The static feature data is compared with preset static data; When the static characteristic data is consistent with the preset static data, the step of collecting the user's impedance data through the bioelectrical impedance measurement module is executed.

[0009] In one embodiment, the step of determining the first vital sign parameter corresponding to the acquisition time from each of the first vital sign parameters as the aligned first vital sign parameter includes: Obtain the acquisition time corresponding to when the bioelectrical impedance measurement module acquires the impedance data; Using the acquisition time as a reference time point, a first time interval corresponding to a first preset time length before the reference time point and a second time interval corresponding to a second preset time length after the reference time point are obtained. Based on the first time interval and the second time interval, a time alignment interval is obtained; Extract the first vital sign parameters located within the time alignment interval from each of the first vital sign parameters, and use them as the aligned first vital sign parameters.

[0010] In one embodiment, the step of comparing the aligned first vital sign parameters with the second vital sign parameters for consistency, and obtaining the health monitoring result based on the comparison result, includes: A first target indicator value is extracted from the aligned first vital sign parameters, and a second target indicator value is extracted from the second vital sign parameters, wherein the first target indicator value and the second target indicator value belong to the same physiological parameter type; Calculate the deviation between the first target indicator value and the second target indicator value; When the deviation value is less than or equal to a preset deviation threshold, a fusion index value is calculated based on the first target index value and the second target index value, and the fusion index value is used as the health monitoring result.

[0011] In one embodiment, after the step of calculating the deviation between the first target indicator value and the second target indicator value, the method further includes: When the deviation value is greater than the preset deviation threshold, a measurement anomaly identifier is generated; Based on the measurement anomaly identifier, a prompt message is output to remind the user to remeasure, and the aligned first vital sign parameter and second vital sign parameter are output as independent measurement results.

[0012] Furthermore, to achieve the above objectives, this application also proposes a health monitoring device, the device comprising: The acquisition module is used to continuously acquire signal state parameters generated by the user's activities within a preset detection range through the wireless sensing module, and to obtain first vital sign parameters from each of the signal state parameters. The measurement module is used to collect the user's impedance data through the bioelectrical impedance measurement module when preset measurement conditions are met, and to obtain the second vital sign parameter based on the impedance data. An alignment module is used to determine the first vital sign parameter corresponding to the acquisition time from each of the first vital sign parameters as the aligned first vital sign parameter, wherein the acquisition time is the time when the bioelectrical impedance measurement module acquires the impedance data; The comparison module is used to compare the alignment of the first vital sign parameters with the second vital sign parameters and obtain the health monitoring results based on the comparison results.

[0013] In addition, to achieve the above objectives, this application also proposes a health monitoring device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the health monitoring method as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the health monitoring method described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the health monitoring method described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: This application continuously collects signal state parameters generated by a user's activities within a preset detection range using a wireless sensing module, and obtains first vital sign parameters from each signal state parameter. When preset measurement conditions are met, the user's impedance data is collected through a bioelectrical impedance measurement module, and second vital sign parameters are obtained based on the impedance data. The first vital sign parameter corresponding to the collection time is determined from each first vital sign parameter as the aligned first vital sign parameter, where the collection time is the moment the bioelectrical impedance measurement module collects the impedance data. The aligned first vital sign parameter and the second vital sign parameter are compared for consistency, and the health monitoring result is obtained based on the comparison result. This application, through the fusion of wireless sensing and bioelectrical impedance, achieves effective correlation and consistency verification between continuous dynamic data and discrete static data, thereby overcoming the shortcomings of traditional schemes where the two types of data cannot be fused, monitoring data is discontinuous, and cross-validation is lacking. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the health monitoring method of this application; Figure 2 This is a flowchart illustrating the second embodiment of the health monitoring method of this application; Figure 3 This is a flowchart illustrating the third embodiment of the health monitoring method of this application; Figure 4This is a schematic diagram of the module structure of the health monitoring device according to an embodiment of this application; Figure 5 This is a schematic diagram of the device structure of the hardware operating environment involved in the health monitoring method in this application embodiment.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] With the increasing demand for health management, bioelectrical impedance analysis (BIA) devices (such as body fat scales) have become common health monitoring tools in homes, providing discrete measurement data such as body composition. Meanwhile, wireless sensing technology based on Wi-Fi channel state information (CSI) is widely researched and applied in the monitoring of vital signs such as respiration and heart rate due to its advantages of non-contact and continuous monitoring.

[0024] Currently, BIA measurement devices and Wi-Fi sensing devices typically operate independently. Existing body fat scales only perform a single measurement when the user steps on the scale, failing to acquire continuous health data between two measurements, resulting in data blind spots in health trend analysis. While independent Wi-Fi sensing systems can continuously monitor vital signs, their data belongs to different systems than BIA impedance data, lacking a unified time-series correlation and cross-validation mechanism. Due to fundamental differences in sampling frequency, physical meaning, and data format, the two types of data cannot be directly fused for analysis, making it difficult to form a complete and reliable assessment of the user's health status.

[0025] Therefore, how to effectively correlate and verify the consistency of continuous vital signs data sensed by Wi-Fi with discrete impedance data of BIA is an urgent problem to be solved.

[0026] The main solution of this application embodiment is as follows: A wireless sensing module continuously collects signal state parameters generated by the user's activities within a preset detection range, and obtains first vital sign parameters from each signal state parameter; when preset measurement conditions are met, a bioelectrical impedance measurement module collects the user's impedance data, and obtains second vital sign parameters based on the impedance data; a first vital sign parameter corresponding to the collection time is determined from each first vital sign parameter as the aligned first vital sign parameter, where the collection time is the moment the bioelectrical impedance measurement module collects the impedance data; the aligned first vital sign parameter and the second vital sign parameter are compared for consistency, and a health monitoring result is obtained based on the comparison result. This application, through the fusion of wireless sensing and bioelectrical impedance, achieves effective correlation and consistency verification between continuous dynamic data and discrete static data, thereby overcoming the shortcomings of traditional solutions where the two types of data cannot be fused, monitoring data is discontinuous, and cross-validation is lacking.

[0027] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a body fat scale, tablet computer, personal computer, mobile phone, etc., or a health monitoring device capable of performing the above functions. The following description uses a body fat scale as an example to illustrate this embodiment and the subsequent embodiments.

[0028] Based on this, the embodiments of this application provide a fall detection and alarm method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the health monitoring method of this application.

[0029] In this embodiment, the method is applied to a body fat scale, which includes a wireless sensing module and a bioelectrical impedance measurement module. The method includes steps S10 to S40: Step S10: The wireless sensing module continuously collects signal status parameters generated by the user's activities within a preset detection range, and obtains the first vital sign parameters from each of the signal status parameters.

[0030] It should be noted that a body fat scale is a weight scale device that integrates wireless signal transmission and reception functions with bioelectrical impedance measurement functions, used to monitor users' health data.

[0031] The wireless sensing module refers to the hardware component in a body fat scale responsible for transmitting and receiving wireless signals and outputting channel status information. It can reuse the original Wi-Fi communication chip of the body fat scale. This wireless sensing module can use a Wi-Fi chip that supports CSI acquisition, such as the ESP32-S3 chip, with an operating frequency band of 2.4GHz or 5GHz, 56 to 114 CSI subcarriers, and a CSI sampling rate of 10Hz to 50Hz.

[0032] The bioelectrical impedance analysis (BIA) module in a body fat scale refers to the hardware component used to apply alternating current signals to the human body and measure the corresponding electrical response. Specifically, it includes multiple electrodes arranged on the scale surface and handle, a constant current source drive circuit for generating excitation current, an analog switch matrix for switching different electrode combinations, a differential amplifier for amplifying the measurement signal, and an analog-to-digital converter (ADC) for converting the analog signal into a digital signal. This ADC can have a resolution of 24 bits and a sampling rate of 1 kHz.

[0033] The preset detection range refers to a specific area defined in space centered on the body fat scale. When the user is within this area, the wireless sensing module can effectively collect signal status parameters affected by the user's physical activity. This preset detection range can be set according to the transmission power and receiving sensitivity of the wireless sensing module, for example, a circular area with a radius of 2 to 3 meters centered on the body fat scale.

[0034] Signal state parameters refer to the physical quantities of wireless signals captured by the wireless sensing module within a preset detection range and affected by the user's physical activities. In specific implementations, they can be represented as channel state information (CSI), which is the set of amplitude attenuation and phase shift of the wireless signal along the propagation path.

[0035] First vital signs (CSI) parameters refer to indicators extracted from signal state parameters to characterize a user's physiological state, specifically including at least one of respiratory rate and heart rate. For example, when a user is within the detection range of a wireless sensing module, the chest rise and fall caused by human breathing and the micro-movements on the body surface caused by heartbeats will produce periodic perturbations to the CSI sequence. By filtering and periodic detection of the CSI sequence, respiratory rate and heart rate can be extracted.

[0036] In its implementation, the wireless sensing module continuously transmits radio frequency signals at a preset sampling rate (e.g., 10Hz to 50Hz). When the signal encounters the user's body surface during spatial propagation, it undergoes reflection, scattering, and transmission, which are then collected by the receiving antenna to form signal state parameters. After the body fat scale is powered on, the wireless sensing module remains operational. When the user enters the preset detection range of the wireless sensing module, the module continuously collects the signal state parameters that change due to the user's body movements. The body fat scale then processes the collected signal state parameters to extract the primary vital signs parameters. Specifically, the body fat scale extracts amplitude and phase sequences from the signal state parameters. For respiratory rate extraction, the body fat scale applies a bandpass filter of 0.1Hz to 0.5Hz to the phase sequence, extracting the periodic chest undulation signal caused by respiration, and calculates the respiratory rate using zero-crossing detection. For heart rate extraction, the body fat scale applies a bandpass filter of 0.8Hz to 2.0Hz to the phase sequence, extracting the weak signal fluctuations caused by heartbeats, and calculates the heart rate using zero-crossing detection. This primary vital sign parameter is continuously sampled time-series data, which can reflect the trend of changes in the user's vital signs over time.

[0037] For example, when a user moves around at home and passes near a body fat scale, the wireless sensing module inside the scale continuously transmits wireless signals and receives signals reflected by the user's body. Assuming the preset detection range of this wireless sensing module is an area with a radius of 2.5 meters centered on the body fat scale, when the user enters this area, the scale collects a series of CSI data and extracts a heart rate of 72 beats / minute and a respiratory rate of 16 breaths / minute as primary vital signs parameters.

[0038] Step S20: When the preset measurement conditions are met, the user's impedance data is collected through the bioelectrical impedance measurement module, and the second vital sign parameter is obtained based on the impedance data.

[0039] It should be noted that the preset measurement conditions refer to the conditions required to trigger the bioelectrical impedance measurement module to start measurement. Impedance data refers to the raw electrical parameters reflecting the resistance and reactance characteristics of different segments of the human body, obtained by the bioelectrical impedance measurement module after applying alternating current signals to the human body through multiple electrodes arranged on the scale surface and handle of the body fat scale.

[0040] Second vital signs parameters refer to physiological indicators calculated based on impedance data, which may include at least one of heart rate, water percentage, body fat percentage, and muscle mass.

[0041] In its implementation, the body fat scale continuously monitors its own status to determine whether it meets the preset measurement conditions. When the scale determines that the preset conditions are met, it collects the user's impedance data through a bioelectrical impedance analysis (BIA) module. This module applies alternating current signals to the body through multiple electrodes positioned on the scale surface and handle. A simulated switch matrix switches between different electrode combinations to measure the impedance values ​​of five segments: left upper limb, right upper limb, trunk, left lower limb, and right lower limb. After the impedance data is collected, the scale calculates the user's secondary vital signs based on this data. For example, it uses a bioelectrical impedance analysis algorithm to calculate body composition indicators such as water percentage, body fat percentage, and muscle mass. Because heartbeats cause periodic changes in blood volume, these changes result in minute periodic fluctuations in the impedance values ​​of the corresponding segments. Therefore, by analyzing the impedance data's change curve over time, heart rate can be extracted. In this way, the body fat scale obtains secondary vital signs parameters including at least one of heart rate, water percentage, body fat percentage, and muscle mass.

[0042] For example, after the body fat scale determines that the preset measurement conditions are met, it sequentially switches to the electrode combination corresponding to each segment via an analog switch matrix. A constant current source drives the output of a 500 microamp, 50 kHz AC current signal, which is amplified by a differential amplifier and then acquired by an analog-to-digital converter. This yields impedance data for the user's left upper limb (350 ohms), right upper limb (360 ohms), trunk (280 ohms), left lower limb (300 ohms), and right lower limb (310 ohms). Based on this impedance data, the body fat scale obtains the user's secondary vital signs parameters: the heart rate is extracted as 75 beats / minute based on the periodic fluctuations of the impedance values ​​over time; and the water percentage is calculated to be 58%, body fat percentage 22%, and muscle mass 48 kg using a bioelectrical impedance analysis algorithm.

[0043] Step S30: Determine the first vital sign parameter corresponding to the acquisition time from each of the first vital sign parameters as the aligned first vital sign parameter, wherein the acquisition time is the time when the bioelectrical impedance measurement module acquires the impedance data.

[0044] It should be noted that the acquisition time refers to the point in time when the bioelectrical impedance measurement module completes the impedance data acquisition.

[0045] Aligned first vital signs parameters refer to the portion of first vital signs parameters that corresponds to the time of collection from multiple continuously collected first vital signs parameters.

[0046] In its implementation, the body fat scale records the corresponding acquisition time when the bioelectrical impedance analysis (BIA) module collects impedance data. The wireless sensing module continuously collects signal state parameters at a sampling rate of 10Hz to 50Hz, which is continuous sampling time-series data; while the duration of each measurement by the bioelectrical impedance analysis module is 5 to 30 seconds, which is discrete measurement data. Using the acquisition time as a reference, the body fat scale uses a timestamp mapping method to determine the portion of primary vital signs (PVS) parameters corresponding to the acquisition time from multiple PVS parameters, using a sliding window aggregation method. Specifically, the body fat scale can use a sliding window aggregation method, setting a time window centered on the acquisition time, and statistically aggregating multiple PVS parameters within this window (e.g., taking the average, median, etc.), using the aggregation result as the PVS parameter corresponding to the acquisition time. The body fat scale can also adaptively adjust the window size according to the WiFi data quality (e.g., signal-to-noise ratio, data integrity rate) and the BIA measurement duration; for example, a narrower window is used when the WiFi signal quality is good, and a wider window is used when the signal quality is poor to ensure sufficient data volume. The body fat scale will ultimately use this portion of the primary vital signs parameters as the aligned primary vital signs parameters.

[0047] For example, the bioelectrical impedance measurement module completes impedance data acquisition at 10:00:05, and the body fat scale records this moment as the acquisition time. The wireless sensing module continuously acquires primary vital sign parameters at multiple time points between 10:00:00 and 10:00:10. The body fat scale uses 10:00:05 as the acquisition time, selects the primary vital sign parameter corresponding to this acquisition time from the multiple primary vital sign parameters, and uses these selected primary vital sign parameters as the aligned primary vital sign parameters.

[0048] Step S40: Compare the alignment of the first vital sign parameter with the second vital sign parameter to obtain the health monitoring result based on the comparison result.

[0049] It should be noted that consistency comparison refers to comparing the aligned first vital sign parameters with the second vital sign parameters that belong to the same physiological parameter type to determine whether the deviation between the two is within a reasonable range.

[0050] Health monitoring results refer to the conclusive information related to the user's health status that the body fat scale ultimately outputs after consistency comparison, such as fused indicator values ​​or abnormal measurement alerts. Understandably, after obtaining the consistency comparison results, the body fat scale can combine first vital sign data collected continuously throughout 24 hours via WiFi with second vital sign parameters obtained from discrete BIA measurements to generate a timestamped health trend curve. In this health trend curve, the data corresponding to each BIA measurement time point serves as a baseline, and continuous WiFi data fills the trend lines between these baselines, thus forming a continuous health trend curve covering the entire day's timeline. The body fat scale can detect abnormal changes in health indicators based on this health trend curve and output corresponding alerts.

[0051] In its implementation, after obtaining the aligned first and second vital sign parameters, the body fat scale extracts indicator values ​​belonging to the same physiological parameter type from these two sets of parameters. These indicator values ​​are then compared one by one to calculate the deviation value. Based on the relationship between this deviation value and a preset deviation threshold, the body fat scale determines the consistency comparison result. Based on the comparison result, the body fat scale outputs the corresponding health monitoring result.

[0052] For example, the aligned first vital sign parameter includes a heart rate of 72 beats per minute, and the second vital sign parameter includes a heart rate of 75 beats per minute calculated based on impedance data. The body fat scale compares the 72 beats per minute and 75 beats per minute for consistency, calculating a deviation of 3 beats per minute. Assuming a preset deviation threshold of 10 beats per minute, the body fat scale determines that the deviation is less than the preset threshold. Therefore, it merges the 72 beats per minute and 75 beats per minute to obtain a merged heart rate value of 73.5 beats per minute, and outputs this merged heart rate value as the health monitoring result.

[0053] This embodiment continuously collects signal state parameters generated by the user's activities within a preset detection range using a wireless sensing module, and obtains first vital sign parameters from each signal state parameter. When preset measurement conditions are met, the user's impedance data is collected through a bioelectrical impedance measurement module, and second vital sign parameters are obtained based on the impedance data. The first vital sign parameter corresponding to the collection time is determined from each first vital sign parameter as the aligned first vital sign parameter, where the collection time is the moment the bioelectrical impedance measurement module collects the impedance data. The aligned first vital sign parameter and the second vital sign parameter are compared for consistency, and the health monitoring result is obtained based on the comparison result. This embodiment, through the fusion of wireless sensing and bioelectrical impedance, achieves effective correlation and consistency verification between continuous dynamic data and discrete static data, thereby overcoming the shortcomings of traditional schemes where the two types of data cannot be fused, monitoring data is discontinuous, and cross-validation is lacking.

[0054] Based on the first embodiment of this application, a second embodiment of this application is proposed. In the second embodiment, content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the health monitoring method of this application.

[0055] After the step of continuously collecting signal state parameters generated by the user's activities within a preset detection range through the wireless sensing module, the method further includes: Step S11: Identify the user's gait and body shape characteristics based on the signal state parameters, and determine the first user identifier based on the gait and body shape characteristics.

[0056] Step S12: Identify the segment impedance distribution characteristics of the user based on the impedance data, and determine the second user identifier based on the segment impedance distribution characteristics.

[0057] Step S13: When the first user identifier matches the second user identifier, confirm that the user is the user corresponding to the first user identifier.

[0058] It should be noted that gait characteristics refer to the physical parameters related to movement patterns exhibited by users during walking, which may include at least one of cadence, stride length, and foot landing pattern.

[0059] Body shape characteristics refer to the physical parameters related to the user's body shape and outline, which may include at least one of height and body width.

[0060] The first user identifier is a tag generated by the body fat scale after making a preliminary judgment on the user's identity based on gait and body shape characteristics. It is used to indicate the most matching user among multiple candidate users.

[0061] Segmental impedance distribution characteristics refer to the distribution pattern of impedance values ​​in different segments of a user's body. Differences in the body composition of different users will result in unique segmental impedance distribution patterns for each user.

[0062] The second user identifier is a tag generated by the body fat scale after further judging the user's identity based on the segmental impedance distribution characteristics.

[0063] In its implementation, the body fat scale continuously collects signal status parameters via a wireless sensing module. When a user enters a preset detection range and begins walking, the scale extracts gait and body shape features from these parameters. Specifically, the scale analyzes the user's stride frequency and stride length based on Doppler frequency shift information and height and width based on signal scattering cross-section information. The extracted gait and body shape features are compared with pre-stored reference features for multiple candidate users. Based on the comparison results, the scale determines the most matching candidate user and generates a corresponding first user identifier. Subsequently, after acquiring impedance data, the scale extracts the user's segmental impedance distribution features. Specifically, the scale constructs the user's segmental impedance distribution features based on the impedance values ​​of the left upper limb, right upper limb, trunk, left lower limb, and right lower limb. These features are compared with pre-stored reference impedance distribution features for multiple candidate users. Based on the comparison results, the scale determines the most matching candidate user and generates a corresponding second user identifier. The body fat scale compares the candidate users indicated by the first user identifier and the second user identifier to see if they match. If they match, the current user is confirmed as the user corresponding to that user identifier. Regarding recognition accuracy, the accuracy rate of recognition based solely on WiFi RF fingerprints is approximately 85%, the accuracy rate based solely on BIA impedance characteristics is approximately 90%, while the accuracy rate of dual recognition using both WiFi RF fingerprints and BIA impedance characteristics can reach approximately 98%.

[0064] For example, when user A approaches a body fat scale, the scale extracts the following parameters from the signal status: a step frequency of 110 steps / minute, a stride length of 0.6 meters, an estimated height of 175 centimeters, and an estimated body width of 45 centimeters. The scale compares these features with pre-stored reference features of three family members and finds that user A's features are the best match, with a matching confidence level of 90%. Therefore, it generates the first user identifier as user A. After user A steps onto the scale, it collects impedance data, obtaining the following impedances: left upper limb impedance 350 ohms, right upper limb impedance 360 ​​ohms, trunk impedance 280 ohms, left lower limb impedance 300 ohms, and right lower limb impedance 310 ohms, forming a segmental impedance distribution feature. The scale compares this segmental impedance distribution feature with the pre-stored reference impedance distribution features of the three family members and finds that user A's reference impedance distribution feature is the best match, with a matching confidence level of 95%. Therefore, it generates the second user identifier as user A. The first user identifier and the second user identifier are both user A. The body fat scale confirms that the current user is user A.

[0065] Furthermore, in this embodiment, the step of collecting the user's impedance data through the bioelectrical impedance measurement module when preset measurement conditions are met includes: Step S201: When the load pressure is detected to reach a preset threshold, extract the user's static feature data from each of the signal state parameters.

[0066] Step S202: Compare the static feature data with preset static data.

[0067] Step S203: When the static characteristic data is consistent with the preset static data, execute the step of collecting the user's impedance data through the bioelectrical impedance measurement module.

[0068] It should be noted that the bearing pressure refers to the vertical force exerted on the scale surface when the user stands on it, which is detected by the pressure sensor built into the scale.

[0069] The preset threshold refers to the pre-set critical value of the load-bearing pressure, which is used to determine whether the user has stood stably on the scale surface. For example, it can be set to 5 kg to avoid accidental triggering by pets or light items.

[0070] Static feature data refers to the physical quantities representing the user's posture extracted by the wireless sensing module from the signal state parameters when the user is standing on the body fat scale and in a relatively static state. Specifically, it may include at least one of the following: foot spacing, body tilt angle, arm position, and body sway.

[0071] Preset static data refers to pre-stored reference data that characterizes the standard measurement posture. The standard measurement posture is with feet shoulder-width apart, body upright, and hands gripping the handles.

[0072] In its implementation, the body fat scale uses a built-in pressure sensor to detect the load-bearing pressure on the scale surface in real time and compares this pressure with a preset threshold. When the load-bearing pressure reaches the preset threshold, the scale determines that the user is standing on the scale surface. At this point, the scale extracts static feature data of the user in a standing state from various signal status parameters. The scale then compares this extracted static feature data with preset static data. When all the criteria in the static feature data match the preset static data, the scale determines that the user's posture meets the requirements of the standard measurement posture and proceeds to collect the user's impedance data through the bioelectrical impedance measurement module. When any criterion in the static feature data does not match the preset static data, the scale does not activate the bioelectrical impedance measurement module but instead outputs targeted guidance prompts through the scale's display screen or a mobile application, prompting the user to adjust the corresponding posture defect.

[0073] For example, when a user stands on a body fat scale, the pressure sensor detects a load of 60 kg, exceeding the preset threshold by 5 kg, and determines that the user is standing on the scale surface. The body fat scale extracts static feature data from various signal state parameters, obtaining a foot spacing of 12 cm, a forward lean angle of 15 degrees, and an arm position where the user is not holding the handles. The preset static data is a foot spacing of 10 to 15 cm, a body lean angle of less than 5 degrees, and both hands holding the handles. The body fat scale compares the extracted static feature data with the preset static data and finds that the forward lean angle of 15 degrees exceeds the standard value by 5 degrees, and the arm position is inconsistent. Therefore, it determines that the two are inconsistent and does not activate the bioelectrical impedance measurement module, but outputs a prompt on the display screen asking the user to keep their body upright and hold the handles. After the user adjusts their posture, the body fat scale re-extracts static feature data, obtaining a foot spacing of 12 cm, a forward lean angle of 3 degrees, and an arm position where the user is holding the handles. At this time, the static feature data is consistent with the preset static data, and the body fat scale proceeds to collect the user's impedance data through the bioelectrical impedance measurement module.

[0074] Furthermore, in this embodiment, the step of determining the first vital sign parameter corresponding to the acquisition time from each of the first vital sign parameters as the aligned first vital sign parameter includes: Step S301: Obtain the acquisition time corresponding to when the bioelectrical impedance measurement module acquires the impedance data.

[0075] Step S302: Using the acquisition time as a reference time point, obtain the first time interval corresponding to the first preset duration before the reference time point and the second time interval corresponding to the second preset duration after the reference time point.

[0076] Step S303: Obtain a time alignment interval based on the first time interval and the second time interval.

[0077] Step S304: Extract the first vital sign parameters located within the time alignment interval from each of the first vital sign parameters, and use them as the aligned first vital sign parameters.

[0078] It should be noted that the acquisition time refers to the point in time when the bioelectrical impedance measurement module completes the impedance data acquisition.

[0079] A reference time point is a time location determined with reference to the time of data collection, and is used as the basis for dividing time intervals.

[0080] The first preset duration refers to a fixed duration that traces back from the reference time point, used to determine the starting boundary before the reference time point, for example, it can be set to 5 seconds; the second preset duration refers to a fixed duration that extends backward from the reference time point, used to determine the ending boundary after the reference time point, for example, it can be set to 5 seconds.

[0081] The first time interval refers to the time range covered by tracing back from the reference time point to the first preset duration. For example, if the reference time point is 10:00:05 and the first preset duration is 5 seconds, the first time interval is from 10:00:00 to 10:00:05.

[0082] The second time interval refers to the time range covered by extending the reference time point backward by a second preset duration. For example, if the reference time point is 10:00:05 and the second preset duration is 5 seconds, the second time interval is from 10:00:05 to 10:00:10.

[0083] A time alignment interval is a continuous time period comprised of a first time interval and a second time interval. It is used to define the range of data selected from multiple primary vital sign parameters. The start point of the time alignment interval is the start time of the first time interval, and the end point is the end time of the second time interval. For example, if the first time interval is 10:00:00 to 10:00:05 and the second time interval is 10:00:05 to 10:00:10, then the time alignment interval is 10:00:00 to 10:00:10.

[0084] In its implementation, when the bioelectrical impedance measurement module completes impedance data acquisition, the body fat scale records the current moment as the acquisition moment. The body fat scale uses this acquisition moment as a reference time point to obtain a pre-set first and second preset duration. The body fat scale traces backward from the reference time point for the first preset duration to obtain a first time interval; it then extends backward from the reference time point for the second preset duration to obtain a second time interval. Subsequently, the body fat scale merges the first and second time intervals to obtain a continuous time-aligned interval. The starting point of this time-aligned interval is the start time of the first time interval, and the ending point is the end time of the second time interval. Next, the body fat scale selects first vital sign parameters whose timestamps fall within this time-aligned interval from the multiple continuously acquired first vital sign parameters, and uses these selected first vital sign parameters as the aligned first vital sign parameters.

[0085] For example, the bioelectrical impedance measurement module completes impedance data acquisition at 10:00:05, and the body fat scale records the acquisition time as 10:00:05. The first preset duration is set to 5 seconds, and the second preset duration is also set to 5 seconds. Using 10:00:05 as the base time point, the body fat scale advances 5 seconds to obtain the first time interval (10:00:00 to 10:00:05), and extends 5 seconds forward to obtain the second time interval (10:00:05 to 10:00:10). These are then combined to obtain the time-aligned interval (10:00:00 to 10:00:10). The wireless sensing module continuously collected multiple vital signs parameters between 10:00:00 and 10:00:10, specifically including a heart rate of 72 beats / minute at 10:00:00, 73 beats / minute at 10:00:01, 71 beats / minute at 10:00:02, 72 beats / minute at 10:00:03, 74 beats / minute at 10:00:04, 72 beats / minute at 10:00:05, 73 beats / minute at 10:00:06, 71 beats / minute at 10:00:07, 72 beats / minute at 10:00:08, 74 beats / minute at 10:00:09, and 73 beats / minute at 10:00:10. The body fat scale extracts all first vital signs parameters with timestamps between 10:00:00 and 10:00:10 from the above multiple first vital signs parameters, and uses them as aligned first vital signs parameters.

[0086] Based on the first and second embodiments of this application, a third embodiment of this application is proposed. In this third embodiment, content that is the same as or similar to the first and second embodiments described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart illustrating the third embodiment of the health monitoring method of this application.

[0087] The step of comparing the aligned first vital sign parameters with the second vital sign parameters for consistency, and obtaining the health monitoring result based on the comparison result, includes: Step S401: Extract a first target indicator value from the aligned first vital sign parameters and extract a second target indicator value from the second vital sign parameters, wherein the first target indicator value and the second target indicator value belong to the same physiological parameter type.

[0088] Step S402: Calculate the deviation between the first target index value and the second target index value.

[0089] Step S403: When the deviation value is less than or equal to a preset deviation threshold, calculate the fusion index value based on the first target index value and the second target index value, and use the fusion index value as the health monitoring result.

[0090] It should be noted that the first target index value refers to the value of a specific physiological parameter extracted from the aligned first vital sign parameters and used for consistency comparison.

[0091] The second target indicator value refers to the value of a specific physiological parameter extracted from the second vital sign parameters and used for consistency comparison.

[0092] The same type of physiological parameter means that the first target index value and the second target index value represent the same physiological meaning, such as both being heart rate values ​​or both being respiratory rate values.

[0093] The deviation value refers to the absolute value of the difference between the first target indicator value and the second target indicator value.

[0094] A preset deviation threshold is a pre-set maximum allowable deviation threshold used to determine whether a deviation value is within an acceptable range. For example, when the physiological parameter type is heart rate, the preset deviation threshold can be set to 10 beats / minute; when the physiological parameter type is respiratory rate, the preset deviation threshold can be set to 5 beats / minute; and when the physiological parameter type is water content, the preset deviation threshold can be set to 3%.

[0095] The fusion index value refers to a comprehensive value calculated based on the first target index value and the second target index value when the deviation value does not exceed the preset deviation threshold. This comprehensive value is used as the final health monitoring result output to the user.

[0096] In its implementation, after obtaining the aligned first vital sign parameters, the body fat scale extracts the first target indicator value for consistency comparison from these parameters. Simultaneously, it extracts the second target indicator value, belonging to the same physiological parameter type as the first target indicator value, from the second vital sign parameters. The body fat scale calculates the deviation between the first and second target indicator values ​​and compares this deviation value with a preset deviation threshold. When the deviation value is less than or equal to the preset deviation threshold, the body fat scale determines that the consistency between the first and second target indicator values ​​is good, and both sets of data are relatively reliable. Therefore, it calculates a fusion indicator value (e.g., taking the arithmetic mean or weighted average) based on the first and second target indicator values ​​and outputs the fusion indicator value as the health monitoring result.

[0097] For example, the aligned first vital sign parameter includes a heart rate of 72 beats / minute, and the second vital sign parameter includes a heart rate of 75 beats / minute calculated based on impedance data. The body fat scale extracts the first target indicator value of 72 beats / minute from the aligned first vital sign parameter and the second target indicator value of 75 beats / minute from the second vital sign parameter. Both target indicator values ​​are heart rate values ​​and belong to the same physiological parameter type. The body fat scale calculates a deviation value of 3 beats / minute. Assuming the preset deviation threshold is 10 beats / minute, the body fat scale determines that 3 beats / minute is less than 10 beats / minute. Therefore, it calculates a fusion indicator value based on 72 beats / minute and 75 beats / minute, for example, taking the arithmetic mean, resulting in a fusion indicator value of 73.5 beats / minute. This fusion indicator value is then output as the health monitoring result.

[0098] Furthermore, in this embodiment, after the step of calculating the deviation between the first target indicator value and the second target indicator value, the method further includes: Step S4021: When the deviation value is greater than the preset deviation threshold, a measurement anomaly identifier is generated.

[0099] Step S4022: Based on the measurement anomaly identifier, output a prompt message to remind the user to remeasure, and output the aligned first vital sign parameter and the second vital sign parameter as independent measurement results.

[0100] It should be noted that the measurement anomaly indicator refers to the marking information generated by the body fat scale to indicate that the measurement result is unreliable when the deviation value is greater than the preset deviation threshold.

[0101] The prompt message refers to the information output by the body fat scale to the user to inform them of measurement abnormalities and guide them to take appropriate actions, such as "The measurement result is abnormal, please measure again."

[0102] Independent measurement results refer to measurement results in which the first vital sign parameter and the second vital sign parameter are output separately without fusion processing.

[0103] In its implementation, the body fat scale calculates the deviation between the first and second target indicator values ​​and then compares this deviation with a preset deviation threshold. When the deviation exceeds the threshold, the scale determines that the consistency between the two sets of data is poor, with at least one set containing a significant error. In this case, the scale generates a measurement anomaly flag to mark the measurement result as abnormal in subsequent processes. Based on this flag, the scale outputs a prompt to alert the user that the measurement result is abnormal and suggests re-measuring to obtain reliable health monitoring data. Simultaneously, the scale does not perform data fusion; instead, it outputs the aligned first and second vital sign parameters as independent measurement results for reference by the user or medical professionals.

[0104] For example, the aligned first vital sign parameter includes a heart rate of 72 beats / minute, the second vital sign parameter includes a heart rate of 95 beats / minute calculated based on impedance data, and the body fat scale's calculated deviation is 23 beats / minute. Assuming a preset deviation threshold of 10 beats / minute, the body fat scale determines that 23 beats / minute is greater than 10 beats / minute, and generates a measurement anomaly indicator. Based on this indicator, the body fat scale displays a message indicating an abnormal measurement result and requesting a re-measurement. Simultaneously, the body fat scale outputs the aligned first vital sign parameter (heart rate of 72 beats / minute) and the second vital sign parameter (heart rate of 95 beats / minute) as independent measurement results, without merging them. The user then re-steps onto the body fat scale to perform the measurement based on the aforementioned prompt.

[0105] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the health monitoring method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0106] This application also provides a health monitoring device, please refer to... Figure 4 The device includes: The acquisition module 10 is used to continuously acquire signal state parameters generated by the user's activities within a preset detection range through the wireless sensing module, and to obtain first vital sign parameters from each of the signal state parameters. The measurement module 20 is used to collect the user's impedance data through the bioelectrical impedance measurement module when preset measurement conditions are met, and to obtain the second vital sign parameter based on the impedance data. Alignment module 30 is used to determine the first vital sign parameter corresponding to the acquisition time from each of the first vital sign parameters as the aligned first vital sign parameter, wherein the acquisition time is the time when the bioelectrical impedance measurement module acquires the impedance data; The comparison module 40 is used to compare the alignment of the first vital sign parameter with the second vital sign parameter and obtain the health monitoring result based on the comparison result.

[0107] The health monitoring device provided in this application, employing the health monitoring method described in the above embodiments, can solve the technical problem of how to effectively correlate and verify the consistency of continuous vital sign data sensed by Wi-Fi with BIA discrete impedance data. Compared with the prior art, the beneficial effects of the health monitoring device provided in this application are the same as those of the health monitoring method provided in the above embodiments, and other technical features in the health monitoring device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0108] This application provides a health monitoring device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the health monitoring method in Embodiment 1 above.

[0109] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing the health monitoring device in the embodiments of this application. The health monitoring device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The health monitoring device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0110] like Figure 5As shown, the health monitoring device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the health monitoring device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the health monitoring device to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show health monitoring devices with various systems, it should be understood that implementing or having all of the systems shown is not required. More or fewer systems may be implemented alternatively.

[0111] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0112] The health monitoring device provided in this application, employing the health monitoring method described in the above embodiments, can solve the technical problem of how to effectively correlate and verify the consistency of continuous vital sign data sensed by Wi-Fi with BIA discrete impedance data. Compared with the prior art, the beneficial effects of the health monitoring device provided in this application are the same as those of the health monitoring method provided in the above embodiments, and other technical features of this health monitoring device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0113] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0114] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0115] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the health monitoring method described in the above embodiments.

[0116] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0117] The aforementioned computer-readable storage medium may be included in the health monitoring device; or it may exist independently and not assembled into the health monitoring device.

[0118] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the health monitoring device, the health monitoring device: continuously collects signal state parameters generated by the user's activities within a preset detection range via a wireless sensing module, and obtains first vital sign parameters from each signal state parameter; when preset measurement conditions are met, collects the user's impedance data via a bioelectrical impedance measurement module, and obtains second vital sign parameters based on the impedance data; determines the first vital sign parameter corresponding to the collection time from each first vital sign parameter as the aligned first vital sign parameter, where the collection time is the moment when the bioelectrical impedance measurement module collects the impedance data; compares the alignment first vital sign parameter with the second vital sign parameter for consistency, and obtains a health monitoring result based on the comparison result.

[0119] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0120] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0121] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0122] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described health monitoring method. This solves the technical problem of how to effectively correlate and verify the consistency of continuous vital sign data sensed by Wi-Fi with BIA discrete impedance data. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the health monitoring method provided in the above embodiments, and will not be repeated here.

[0123] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the health monitoring method described above.

[0124] The computer program product provided in this application solves the technical problem of how to effectively correlate and verify the consistency of continuous vital sign data sensed by Wi-Fi with BIA discrete impedance data. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the health monitoring method provided in the above embodiments, and will not be repeated here.

[0125] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A health monitoring method, characterized in that, The method is applied to a body fat scale, which includes a wireless sensing module and a bioelectrical impedance measurement module. The method includes: The wireless sensing module continuously collects signal status parameters generated by the user's activities within a preset detection range, and obtains first vital sign parameters from each of the signal status parameters. When the preset measurement conditions are met, the user's impedance data is collected through the bioelectrical impedance measurement module, and the second vital sign parameter is obtained based on the impedance data; The first vital sign parameter corresponding to the acquisition time is determined from each of the first vital sign parameters as the aligned first vital sign parameter, wherein the acquisition time is the time when the bioelectrical impedance measurement module acquires the impedance data; The alignment of the first vital sign parameter with the second vital sign parameter is compared for consistency, and the health monitoring result is obtained based on the comparison result.

2. The method as described in claim 1, characterized in that, After the step of continuously collecting signal state parameters generated by the user's activities within a preset detection range through the wireless sensing module, the method further includes: The user's gait and body shape characteristics are identified based on the signal state parameters, and a first user identifier is determined based on the gait and body shape characteristics. The segment impedance distribution characteristics of the user are identified based on the impedance data, and the second user identifier is determined based on the segment impedance distribution characteristics. When the first user identifier matches the second user identifier, the user is confirmed to be the user corresponding to the first user identifier.

3. The method as described in claim 2, characterized in that, The step of acquiring the user's impedance data through the bioelectrical impedance measurement module when preset measurement conditions are met includes: When the load pressure is detected to reach a preset threshold, the user's static feature data is extracted from each of the signal state parameters; The static feature data is compared with preset static data; When the static characteristic data is consistent with the preset static data, the step of collecting the user's impedance data through the bioelectrical impedance measurement module is executed.

4. The method as described in claim 1, characterized in that, The step of determining the first vital sign parameter corresponding to the acquisition time from each of the first vital sign parameters as the aligned first vital sign parameter includes: Obtain the acquisition time corresponding to when the bioelectrical impedance measurement module acquires the impedance data; Using the acquisition time as a reference time point, a first time interval corresponding to a first preset time length before the reference time point and a second time interval corresponding to a second preset time length after the reference time point are obtained. Based on the first time interval and the second time interval, a time alignment interval is obtained; Extract the first vital sign parameters located within the time alignment interval from each of the first vital sign parameters, and use them as the aligned first vital sign parameters.

5. The method as described in claim 1, characterized in that, The step of comparing the aligned first vital sign parameters with the second vital sign parameters for consistency, and obtaining the health monitoring result based on the comparison result, includes: A first target indicator value is extracted from the aligned first vital sign parameters, and a second target indicator value is extracted from the second vital sign parameters, wherein the first target indicator value and the second target indicator value belong to the same physiological parameter type; Calculate the deviation between the first target indicator value and the second target indicator value; When the deviation value is less than or equal to a preset deviation threshold, a fusion index value is calculated based on the first target index value and the second target index value, and the fusion index value is used as the health monitoring result.

6. The method as described in claim 5, characterized in that, After the step of calculating the deviation between the first target indicator value and the second target indicator value, the method further includes: When the deviation value is greater than the preset deviation threshold, a measurement anomaly identifier is generated; Based on the measurement anomaly identifier, a prompt message is output to remind the user to remeasure, and the aligned first vital sign parameter and second vital sign parameter are output as independent measurement results.

7. A health monitoring device, characterized in that, The device includes: The acquisition module is used to continuously acquire signal state parameters generated by the user's activities within a preset detection range through the wireless sensing module, and to obtain first vital sign parameters from each of the signal state parameters. The measurement module is used to collect the user's impedance data through the bioelectrical impedance measurement module when preset measurement conditions are met, and to obtain the second vital sign parameter based on the impedance data. An alignment module is used to determine the first vital sign parameter corresponding to the acquisition time from each of the first vital sign parameters as the aligned first vital sign parameter, wherein the acquisition time is the time when the bioelectrical impedance measurement module acquires the impedance data; The comparison module is used to compare the alignment of the first vital sign parameters with the second vital sign parameters and obtain the health monitoring results based on the comparison results.

8. A health monitoring device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the health monitoring method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the health monitoring method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the health monitoring method as described in any one of claims 1 to 6.

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