Physiological signal processing method and body measurement device

Abstract

The application relates to a physiological signal processing method and a body measurement device. The method comprises the following steps: acquiring a synthetic shaking amount sequence for representing the body shaking condition of an object during weighing based on a weighing pressure value; acquiring a fitting interference signal sequence based on the synthetic shaking amount sequence and a weight coefficient sequence in a current period for the synthetic shaking amount sequence and a physiological signal sequence in the current period; acquiring a de-interference physiological signal sequence based on the fitting interference signal sequence and the physiological signal sequence in the current period; and adjusting the weight coefficient sequence in the current period based on the synthetic shaking amount sequence and the de-interference physiological signal sequence in the current period to obtain a weight coefficient sequence in a next period for de-interfering the physiological signal sequence in the next period. The method can improve the accuracy of physiological signal detection of an object without increasing additional costs.

Description

Technical Field

[0001] This application relates to the field of vital sign monitoring technology, and in particular to a physiological signal processing method, device, computer equipment, computer-readable storage medium, and computer program product. Background Technology

[0002] As people pay more and more attention to their health, smart body scales, especially smart body fat scales with body physiological parameter detection functions, are receiving more and more attention.

[0003] Existing high-end smart body fat scales typically come with an external handle for measuring whole-body bioelectrical impedance and electrocardiogram (ECG) signals. However, during measurement, users must remain standing while holding the handle. Due to the inherent postural sway of the human body, minute body movements are transmitted through the arm to the handle, causing unstable contact between the sensor and the skin, resulting in severe motion artifacts.

[0004] In related technologies, an accelerometer (IMU) is integrated inside the controller to detect jitter and assist in noise reduction. However, this approach not only increases hardware costs (BOM cost) and circuit design complexity but also adds to the weight of the controller. Summary of the Invention

[0005] Therefore, it is necessary to provide a physiological signal processing method and body measurement device that can reduce detection costs in response to the above-mentioned technical problems.

[0006] In a first aspect, this application provides a body measuring device, comprising:

[0007] Holding unit and weighing body;

[0008] The controller is used to determine the synthetic sway amount using the weighing pressure value detected by the weighing body, and to remove the sway interference signal from the physiological signal detected by the gripping unit based on the synthetic sway amount.

[0009] Secondly, this application provides a physiological signal processing method applied to a body measuring device, the body measuring device including a first sensor and a second sensor, the first sensor being used to detect the physiological signals of the object, and the second sensor being used to detect the weighing pressure value of the object; including:

[0010] Based on the weighing pressure value, a synthetic swaying sequence is obtained to characterize the body swaying of the object during weighing;

[0011] For time-synchronized synthetic sway sequences and physiological signal sequences, a fitted interference signal sequence is obtained based on the synthetic sway sequence and weight coefficient sequence in the current time period.

[0012] Based on the fitted interference signal sequence and the physiological signal sequence in the current time period, obtain the interference-free physiological signal sequence;

[0013] Based on the synthetic sway sequence and the de-interference physiological signal sequence in the current time period, the weight coefficient sequence in the current time period is adjusted to obtain the weight coefficient sequence in the next time period, which is used to de-interference the physiological signal sequence in the next time period.

[0014] The aforementioned physiological signal processing method and body measurement device first obtain a synthetic sway sequence characterizing the body swaying of the subject during weighing using the weighing pressure value. For the synthetic sway sequence and physiological signal sequence synchronized across time periods, a fitted interference signal sequence is obtained based on the synthetic sway sequence and weighting coefficient sequence for the current time period. Then, a de-interference physiological signal sequence is obtained based on the fitted interference signal sequence and the physiological signal sequence for the current time period. Finally, the weighting coefficient sequence for the current time period is adjusted based on the synthetic sway sequence and the de-interference physiological signal sequence to obtain the weighting coefficient sequence for the next time period, which is used to de-interference the physiological signal sequence for the next time period. This method eliminates the need for additional costs, directly determining the degree of body swaying of the subject through weighing data, identifying the interference caused by the subject's body swaying during the detection process, and continuously adjusting the weighting coefficients based on the interference to further adjust the original physiological signal. This process ensures that the final physiological signal continuously approaches a state without interference, resulting in more accurate detection results. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a diagram illustrating the application environment of a physiological signal processing method in one embodiment;

[0017] Figure 2 This is a schematic diagram of the body measurement device in one embodiment;

[0018] Figure 3 This is a flowchart illustrating a physiological signal processing method in one embodiment;

[0019] Figure 4 This is a flowchart illustrating a physiological signal processing method in another embodiment;

[0020] Figure 5 This is a structural block diagram of a physiological signal processing device in one embodiment;

[0021] Figure 6 This is an internal structural diagram of a computer device in one embodiment.

[0022] Explanation of icon numbers:

[0023] 21. Grip unit; 22. Connecting cable; 23. Weighing surface; 24. Foot electrodes; 25. Base shell; 31. Second sensor; 32. Analog-to-digital converter; 33. Main circuit board; 34. Signal processing chip; 35. Battery compartment. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0025] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0026] As modern people place increasing importance on health, smart body scales, as a common health monitoring device, have been widely used in various scenarios, especially smart body fat scales with physiological parameter detection functions, which have received increasing attention. Existing high-end smart body fat scales typically come with an external handle for measuring whole-body bioelectrical impedance and electrocardiogram signals. However, during measurement, users must maintain a standing posture while holding the handle. Due to the inherent postural sway of the human body, slight body movements are transmitted through the arm to the handle, causing unstable contact between the sensor and the skin, resulting in severe motion artifacts.

[0027] To address this issue, an accelerometer (IMU) can be integrated inside the controller to detect jitter and assist in noise reduction. However, this approach not only increases hardware costs (BOM cost) and circuit design complexity but also adds to the controller's weight.

[0028] To address the aforementioned problems, this application provides a physiological signal processing method that is a low-cost, high-precision solution that can effectively eliminate motion interference without adding extra sensors and by utilizing the existing hardware resources of body measurement devices. It can be applied to various application scenarios that require physiological signal detection, such as homes, medical institutions, and fitness venues.

[0029] For body measurement devices, the device can be various types of smart scales or other devices with weighing and physiological signal detection functions; this application does not limit the specific type. The body measurement device includes at least one sensor for detecting the physiological signals and weighing pressure values ​​of the subject. It is understood that the body measurement device can include multiple types of sensors. For example, it can include sensors for detecting weighing pressure values, and it can also include sensors for detecting physiological signals; each physiological signal can correspond to one sensor. As can be seen, the number of sensors can also be at least one. For example, the number of sensors for detecting weighing pressure values ​​can be multiple, and the number of sensors for detecting physiological signals can correspond to the types of physiological signals.

[0030] Based on the foregoing description, in one exemplary embodiment, such as Figure 1 As shown, a physiological signal processing method is provided, which can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the physiological signal data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed in the cloud or on another network server. Terminal 102 can acquire the object's physiological signals and weighing pressure values ​​and send them to server 104. Server 104 can acquire a synthetic sway sequence characterizing the object's body sway during weighing based on the weighing pressure values. For the time-synchronized synthetic sway sequence and physiological signal sequence, a fitted interference signal sequence is acquired based on the synthetic sway sequence and weight coefficient sequence for the current time period. Based on the fitted interference signal sequence and the physiological signal sequence for the current time period, a de-interference physiological signal sequence is acquired. Based on the synthetic sway sequence and the de-interference physiological signal sequence for the current time period, the weight coefficient sequence for the current time period is adjusted to obtain the weight coefficient sequence for the next time period, which is used to de-interference the physiological signal sequence for the next time period. Of course, the above process can also be entirely implemented by terminal 102, i.e., the body measurement device.

[0031] The terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, tablets, IoT devices, and portable wearable devices. IoT devices can include smart speakers, smart TVs, projectors, etc. Portable wearable devices can include smartwatches, smart bracelets, head-mounted displays, etc. Head-mounted displays can be virtual reality (VR) devices, augmented reality (AR) devices, smart glasses, etc. The server 104 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0032] In one exemplary embodiment, such as Figure 2 As shown, a body measuring device is provided, including a gripping unit 21 and a weighing body.

[0033] In one embodiment, the grip unit 21 includes a first sensor connected to the weighing body via a connecting cable 22. The grip unit 21 integrates multiple hand electrodes and has a display screen on its surface or top for detecting the physiological signals of the object.

[0034] In some embodiments, the weighing body includes a weighing surface 23, foot electrodes 24, a base housing 25, a second sensor 31, and a battery compartment 35. The weighing surface 23 is located above the base housing and is used to support the object being measured. The foot electrodes 24 are located on the upper surface of the weighing surface 23 and include multiple electrodes for direct contact with the object being measured to perform bioelectrical impedance measurement. The base housing 25 is used to house and protect the internal components of the weighing body and provides structural support. The second sensor 31 is located between the base housing 25 and the weighing surface 23 and is used to collect physical quantity signals related to the weight of the object. The battery compartment 35 is located inside the base housing 25 and is used to house the battery that powers the weighing body.

[0035] In some embodiments, the weighing device further includes a controller, which includes an analog-to-digital converter 32, a main circuit board 33, and a signal processing chip 34. The controller is located inside the base housing 25 and is electrically connected to the second sensor 31 and the foot electrode 24. The analog-to-digital converter 32 is used to convert the physical quantity signal collected by the second sensor 31 into a digital signal that can characterize the weighing pressure value of the object. The main circuit board 33 integrates the analog-to-digital converter 32, the signal processing chip 34, and connection circuits. The signal processing chip 34 is electrically connected to the analog-to-digital converter 32 and the foot electrode 24 and is used to process and analyze digital signals and physiological signals.

[0036] In one exemplary embodiment, such as Figure 3 As shown, a physiological signal processing method is provided, which can be applied to... Figure 1Taking the terminal in the example, the explanation includes the following steps 302 to 308. Wherein:

[0037] Step 302: Based on the weighing pressure value, obtain a synthetic sway sequence to characterize the body swaying of the object during weighing.

[0038] This step mainly completes the conversion from the weighing pressure value to the synthesized sway quantity. During the weighing process of the sensor, the object is unstable and swaying, and the data collected by the sensor will fluctuate. These fluctuations can be quantified by synthesizing the sway quantity sequence.

[0039] In some embodiments, the weighing pressure value refers to the converted value of the original electrical signal output by the sensor when it is subjected to force, which reflects the magnitude of the vertical force exerted on the body measuring device by the weight of the object, and provides data support for subsequent quantification of the object's body sway based on the weighing pressure value.

[0040] In some embodiments, the object may refer to an organism that requires physiological signal detection, and subsequent processing revolves around the object. The organism can be a human or other animal.

[0041] In some embodiments, the synthetic sway sequence is a set of values ​​determined by weighing pressure values, arranged in chronological order, used to quantify the degree of body sway, wherein the changes in different values ​​in the synthetic sway sequence represent the changes in the user's center of pressure at different times.

[0042] In some embodiments, before the weighing pressure value is detected by the sensor and before the synthetic sway sequence is determined based on the weighing pressure value, the weighing pressure value can be preprocessed. For example, the weighing pressure values ​​obtained by different sensors can be uniformly processed to facilitate the calculation of subsequent data.

[0043] In some embodiments, when obtaining a synthetic sway sequence characterizing the body sway of an object during weighing based on weighing pressure values, the corresponding dispersion values ​​can be obtained based on multiple weighing pressure values ​​obtained at each detection time. For each detection time, the root mean square operation is performed on the dispersion values ​​obtained up to that detection time to obtain the corresponding synthetic sway amount at that detection time, resulting in a synthetic sway sequence composed of multiple synthetic sway amounts. Of course, in actual implementation, other methods can also be used to obtain the synthetic sway sequence, and this application embodiment does not specifically limit this.

[0044] Step 304: For the synthetic swaying sequence and physiological signal sequence synchronized over the time period, obtain the fitted interference signal sequence based on the synthetic swaying sequence and weight coefficient sequence under the current time period.

[0045] In some embodiments, the physiological signal sequence can be a collection of constantly changing signal data collected from an organism (e.g., the human body) that reflects its life activities or physiological state, including real physiological signals and real interference signals. The physiological signals can be electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), blood pressure, respiratory amplitude, blood oxygen saturation, photoelectric pulse wave, blood glucose, or hormone levels, etc. "Time-slot synchronization" means that the synthetic sway sequence and the physiological signal sequence maintain a one-to-one correspondence in time; the synthetic sway at the same moment matches the physiological signal, ensuring the temporal consistency of subsequent operations.

[0046] In some embodiments, the weighting coefficient sequence can be a sequence formed by arranging weighting coefficients at multiple time points in chronological order. The weighting coefficient at each time point is used to perform a weighted calculation on the synthetic sway amount at the corresponding time point to match the intensity and characteristics of the actual interference in the physiological signal sequence. Specifically, the weighting coefficients at different times can amplify or reduce the synthetic sway amount at the corresponding time point, so that the fitted interference signal sequence calculated based on the synthetic sway amount is as close as possible to the actual sway interference in the physiological signal sequence.

[0047] In some embodiments, the fitted interference signal sequence is used to simulate the interference component introduced by body swaying in the physiological signal sequence, so that the interference in the physiological signal sequence can be subsequently canceled.

[0048] In some embodiments, when obtaining the fitted interference signal sequence based on the synthetic sway sequence and weighted coefficient sequence for the current time period, the synthetic sway at each moment of the current time period can be weighted and multiplied by the weighted coefficients to obtain the fitted interference signal at each moment. The fitted interference signals from multiple consecutive moments are then arranged to obtain the fitted interference signal sequence.

[0049] Step 306: Based on the fitted interference signal sequence and the physiological signal sequence in the current time period, obtain the interference-free physiological signal sequence.

[0050] In some embodiments, the interference-free physiological signal sequence can be a pure signal sequence obtained by removing swaying interference from the original physiological signal sequence, which is used to reflect the true physiological state of the organism and can improve the reliability of subsequent physiological signal analysis, diagnosis or monitoring.

[0051] In some embodiments, when obtaining the de-interference physiological signal sequence based on the fitted interference signal sequence and the physiological signal sequence in the current time period, for each moment in the current time period, the physiological signal at the current moment can be subtracted from the fitted interference signal at the corresponding moment, and the signal can be calibrated by combining a preset baseline correction coefficient to obtain the de-interference physiological signal at each moment. The de-interference physiological signals at multiple consecutive moments can be arranged in chronological order to obtain the de-interference physiological signal sequence.

[0052] Step 308: Based on the synthetic sway sequence and the de-interference physiological signal sequence in the current time period, adjust the weight coefficient sequence in the current time period to obtain the weight coefficient sequence in the next time period, so as to de-interference the physiological signal sequence in the next time period.

[0053] In some embodiments, adjusting the weighting coefficient sequence is used to make the fitted interference signal sequence in subsequent time periods closer to the actual interference components in the physiological signal sequence, thereby improving the accuracy and adaptability of continuous interference removal. Specifically, the synthetic sway sequence in the current time period reflects the body sway characteristics at the current moment, and the interference-removed physiological signal sequence characterizes the signal deviation after interference cancellation, providing a basis for adjusting the weighting coefficients.

[0054] In some embodiments, adjusting the weight coefficient sequence can be achieved by adaptively modifying the weight coefficients at each moment based on the correlation between the synthetic sway amount in the current time period and the physiological signal after interference removal, so that the weight coefficients can be dynamically updated to follow the changes in the sway state.

[0055] In some embodiments, the weight coefficient sequence for the next time period obtained by adjusting the weight coefficient sequence for the current time period can be directly applied to the generation of the fitting interference signal for the next time period, thereby achieving continuous and adaptive de-interference of the subsequent physiological signal sequence and ensuring the stability of the de-interference effect in different time periods.

[0056] In some embodiments, when adjusting the weight coefficient sequence for the current time period based on the synthetic sway sequence and the de-interference physiological signal sequence for the current time period, the weight coefficient for each moment can be adjusted by performing a deviation correction operation on the synthetic sway and the de-interference physiological signal at the current moment to obtain the adjusted weight coefficient. The adjusted weight coefficients for multiple consecutive moments are then arranged in chronological order to form the weight coefficient sequence for the next time period.

[0057] In the aforementioned physiological signal processing method, a synthetic sway sequence is obtained based on the weighing pressure value, which quantifies the swaying of the object's body, providing a basis for removing interference from physiological signals that closely matches the actual motion state. By processing the synchronous synthetic sway sequence and physiological signal sequence, and fitting the weighted coefficient sequence, a fitted interference signal sequence is obtained. This can match and remove motion interference components from the physiological signal, ensuring the accuracy of interference removal. Then, based on the de-interference physiological signal sequence and the current synthetic sway sequence, the weighted coefficient sequence is iteratively adjusted so that the weighted coefficients for the next time period can dynamically adapt to the object's body swaying state at different times, achieving adaptive, high-precision, real-time, and continuous physiological signal interference removal processing. Since the original sensors of the body measurement device can be directly reused, interference elimination can be achieved using only the synchronously detected weighing pressure value and physiological signal without adding additional hardware such as accelerometers. This eliminates the need for increased hardware costs, complex circuit design, and increased handle weight, solving the problems of high hardware costs, complex structures, and increased weight associated with traditional solutions. Physiological signals with high signal-to-noise ratio and reliability can still be obtained on the basis of low-cost hardware, balancing the economy and detection accuracy of the solution.

[0058] In an exemplary embodiment, the weighing pressure values ​​are multiple and detected by sensors at a corresponding number of different locations. Based on the weighing pressure values, a synthetic sway sequence is obtained to characterize the body swaying of the object during weighing. This includes: obtaining the pressure center coordinates based on the multiple weighing pressure values ​​and the location coordinates of the corresponding sensors; obtaining the distance between every two consecutive pressure center coordinates for the pressure center coordinates obtained at multiple times; obtaining the ratio between the distance between every two consecutive pressure center coordinates and the acquisition interval duration as the corresponding synthetic sway, thus obtaining a synthetic sway sequence composed of multiple synthetic sway.

[0059] In some embodiments, "multiple" refers to at least two weighing pressure values, each independently detected by multiple sensors positioned at different locations on the body measurement device. "Corresponding quantity" means the number of sensors matches the number of weighing pressure values, with one sensor corresponding to one weighing pressure value output. Sensors at different locations collect pressure information from the object at different support points, providing a multi-dimensional pressure data foundation for subsequent center of gravity calculation. Multiple weighing pressure values ​​can more comprehensively and accurately reflect the pressure distribution exerted by the body on different locations of the detection device during the weighing process.

[0060] In some embodiments, the pressure center coordinates refer to the position coordinates of the point of application of the resultant force of the weighing pressure values ​​collected by all sensors on the body measuring device, used to characterize the instantaneous position of the center of application of the body weight when the object is on the weighing sensing surface of the body measuring device.

[0061] In some embodiments, when obtaining the pressure center coordinates based on multiple weighing pressure values ​​and the position coordinates of the corresponding sensors, the weighing pressure values ​​can be converted into weights, and the position coordinates of the corresponding sensors can be weighted and summed to obtain the pressure center coordinates at the current moment.

[0062] In some embodiments, "every two in sequence" refers to pairing two pressure center coordinates in chronological order as the basis for calculating the spacing. It should be noted that "every two" here can refer to two adjacent coordinates or two non-adjacent coordinates. "Spacing" can refer to the Euclidean distance between two pressure center coordinates, used to characterize the spatial offset of the object's instantaneous center of action.

[0063] For example, the pressure center coordinates determined at time t1 are (X_cop(t1), Y_cop(t1)), and the pressure center coordinates determined at time t2 are (X_cop(t2), Y_cop(t2)). t1 and t2 are different times collected in sequence, and the formula for calculating the composite sway is shown in formula (1).

[0064] (1)

[0065] in, The sum of the swaying values ​​is T, where T is the interval between t1 and t2.

[0066] In the above embodiments, by sequentially calculating the intervals between the pressure center coordinates at multiple moments and further comparing the intervals with the acquisition interval duration to obtain the synthetic sway quantity, the intensity of body sway can be quantitatively characterized from two dimensions: spatial offset and temporal rate of change. This makes the synthetic sway quantity sequence more sensitive to interference and more closely matches the sway interference in actual physiological signals. Simultaneously, the synthetic sway quantity sequence and the physiological signal sequence are acquired from the same source and have high temporal consistency, requiring no additional time calibration. This provides a reliable foundation for subsequent motion interference removal based on fitted interference signals, achieving low-cost, high-precision, and continuously stable physiological signal interference removal processing without increasing any hardware costs.

[0067] In an exemplary embodiment, obtaining the pressure center coordinates based on multiple weighing pressure values ​​and the location coordinates of the corresponding sensors includes: obtaining the pressure distribution ratio under multiple positioning references based on multiple weighing pressure values; and integrating the pressure distribution ratio under multiple positioning references and the location coordinates of the corresponding sensors to obtain the pressure center coordinates.

[0068] In some embodiments, the positioning reference refers to a positional reference dimension determined based on the location of the sensors. For example, the positioning reference can be specific location coordinates, such as the positional coordinates of each sensor within the body measurement device. Thus, the weighing pressure value detected by each sensor is compared to the sum of the weighing pressure values ​​detected by all sensors. The comparison result reflects the proximity of the instantaneous center of action of the object to the location of the sensors. In this case, the positioning reference is essentially the location of the sensors, reflecting the pressure distribution of the object at the locations of each sensor.

[0069] The positioning reference can also be a direction. For example, if there are four sensors positioned at the upper left, upper right, lower left, and lower right of the pressure detection surface of the body measuring device, the weighing pressure values ​​detected by the pressure sensors at the upper left and upper right positions (representing the top) compared to the weighing pressure values ​​detected by the pressure sensors at the lower left and lower right positions (representing the bottom) can reflect whether the instantaneous center of force of the object is slightly above or slightly below. In this case, the positioning reference is equivalent to the vertical direction on the pressure detection surface, reflecting the pressure distribution of the object in the vertical direction.

[0070] In some embodiments, the pressure distribution ratio of different locations under multiple positioning references is determined based on the weighing pressure values ​​measured by multiple sensors at different locations. The pressure distribution ratio under multiple positioning references and the location coordinates of the corresponding sensors are integrated to obtain the pressure center coordinates.

[0071] In some embodiments, when the pressure distribution ratio under multiple positioning references and the position coordinates of the corresponding sensors are integrated to obtain the pressure center coordinates, the sum of the weighing pressure values ​​measured by all sensors can be calculated. The ratio of the weighing pressure value measured by each sensor to the sum can be used as the dynamic weight of the corresponding sensor. The position coordinates of each sensor and the corresponding dynamic weight can be weighted and summed to obtain the pressure center coordinates.

[0072] For example, a body measurement device includes three sensors for measuring weighing pressure values, which are m, n, and s respectively. The sum of the three weighing pressure values ​​is calculated as r = m + n + s. The ratio of m to r is used as the dynamic weight of the first sensor. Similarly, the dynamic weights of the three sensors are calculated as q1, q2, and q3 respectively. The coordinates of the three sensors are multiplied by their respective dynamic weights and then summed to obtain the coordinates of the pressure center.

[0073] In the above embodiments, multiple weighing pressure values ​​are converted into pressure distribution proportions under multiple positioning references. These pressure distribution proportions are then weighted and integrated with the sensor position coordinates to obtain the pressure center coordinates. This approach reflects the instantaneous center point of the object's action from the perspective of pressure contribution, eliminating the need for additional hardware such as accelerometers. This fundamentally reduces hardware costs, simplifies circuit design, and does not increase equipment weight, solving the problems of high hardware costs and complex structures in traditional interference removal schemes. Furthermore, the pressure center coordinates obtained in this way are from the same source as the physiological signal, exhibiting high temporal consistency. This provides a highly reliable interference basis for subsequent calculation of synthetic sway, interference fitting, and removal. Without adding any hardware, low-cost, high-precision, and continuously stable physiological signal motion interference elimination is achieved.

[0074] In an exemplary embodiment, based on multiple weighing pressure values, the pressure distribution ratio under multiple positioning references is obtained; the pressure distribution ratio under multiple positioning references and the location coordinates of the corresponding sensors are integrated to obtain the pressure center coordinates, including: taking a portion of the weighing pressure values ​​as a first set of weighing pressure values, and taking the remaining weighing pressure values ​​as a second set of weighing pressure values; obtaining a first sum of the first set of weighing pressure values, a second sum of the second set of weighing pressure values, and the sum of the two; obtaining a first ratio between the first sum and the sum, and determining a first direction and a calibration distance in the first direction based on the location coordinates of the corresponding sensors of the first set of weighing pressure values; obtaining a second ratio between the second sum and the sum, and determining a second direction and a calibration distance in the second direction based on the location coordinates of the corresponding sensors of the second set of weighing pressure values; and integrating the calibration distances in the first direction and the second direction based on the first ratio and the second ratio to obtain the pressure center coordinates.

[0075] In some embodiments, the partial weighing pressure value and the remaining weighing pressure value refer to dividing the multi-channel weighing pressure values ​​collected by multiple sensors into two groups according to a preset grouping rule, so as to analyze the pressure distribution from different dimensions. The grouping method can be equal or non-equal. For example, if there are a total of 6 weighing pressure values, they can be divided into one group of 3, or one group of 2 and another group of 4. This application embodiment does not specifically limit this.

[0076] In some embodiments, the first sum is the cumulative result of all weighing pressure values ​​in the first group, and the second sum is the cumulative result of all weighing pressure values ​​in the second group. The total sum is the total pressure value obtained by adding the first sum and the second sum, which is used as a unified benchmark for subsequent ratio calculations.

[0077] The first direction is a reference direction determined based on the actual distribution of the first set of sensors on the device, such as horizontal or vertical. The calibration distance in the first direction is a reference length along the first direction, predetermined based on the position coordinates of the first set of sensors, used to map the pressure percentage to the actual coordinate offset. The second ratio refers to the proportion of the second sum in the total pressure value, essentially the pressure distribution percentage corresponding to the second set of sensors, used to reflect the degree of shift of the object's center of gravity in the second direction. The second direction differs from the first direction and can be perpendicular or orthogonal to each other, together forming a planar positioning coordinate system. The calibration distance in the second direction is a reference length along the second direction, predetermined based on the position coordinates of the second set of sensors, used to map the pressure percentage to the actual coordinate offset.

[0078] In some embodiments, when integrating the calibration distances in the first direction and the second direction based on the first ratio and the second ratio to obtain the pressure center coordinates, the first ratio and the second ratio can be used as weighting coefficients; "integration" can refer to multiplying the calibration distances in each direction by the pressure ratio in the corresponding direction to obtain the center of gravity offset in that direction, and then combining the offsets in different directions.

[0079] For example, the body measurement device includes four sensors for measuring the weighing pressure value. With the preset center point as the origin, the position coordinates of the four sensors are (-a, b), (a, b), (-a, -b) and (a, -b), respectively, and the weighing pressure values ​​are P1, P2, P3 and P4, respectively. P2 and P4 are used as the first set of weighing pressure values, and P1 and P3 are used as the second set of weighing pressure values. The total value is F_total=P1+P2+P3+P4. The X coordinate and Y coordinate of the pressure center coordinate are calculated respectively. The calculation formula of the X coordinate is shown in formula (2), and the calculation formula of the Y coordinate is shown in formula (3).

[0080] X_cop=a*(P2+P4-P3-P1) / F_total(2)

[0081] Y_cop=b*(P2+P1-P3-P4) / F_total (3)

[0082] In the above embodiments, by dividing the multiple weighing pressure values ​​into different groups and calculating the sum and total value of each group, the ratio of the sum of each group's pressure value to the total value is calculated to obtain the pressure distribution ratio in the corresponding direction. The reference direction and calibration distance are determined by combining the position coordinates of each group's sensors and then integrated to finally obtain the pressure center coordinates. The above processing has simple operational logic and low computational load, making it easy to implement in embedded devices. Furthermore, by independently solving for each direction, accurate center of gravity positioning in a two-dimensional plane can be achieved, providing stable characterization even for minor body swaying, effectively improving the accuracy and robustness of the pressure center coordinates.

[0083] Meanwhile, this method reuses the original weighing sensor of the body measurement equipment, eliminating the need for additional hardware such as accelerometers. This reduces hardware costs from the source, simplifies circuit design, and does not increase the weight of the equipment. It solves the technical problems of high cost, complex structure, and excessive weight of traditional interference detection schemes. In addition, the pressure center coordinates and physiological signals are acquired from the same source and have high temporal consistency, which can provide a highly reliable interference basis for subsequent synthetic sway extraction, interference signal fitting, and physiological signal de-interference.

[0084] In an exemplary embodiment, based on the synthetic sway sequence and the de-interference physiological signal sequence for the current time period, the weight coefficient sequence for the current time period is adjusted to obtain the weight coefficient sequence for the next time period. This includes: using the de-interference physiological signal sequence as the error signal sequence between the fitted interference signal sequence and the actual interference signal; multiplying the error signal sequence with the synthetic sway sequence for the current time period in alignment, and correcting the product result by a preset step size to obtain the weight correction sequence; and integrating the weight correction sequence with the weight coefficient sequence for the current time period in alignment to obtain the weight coefficient sequence for the next time period.

[0085] In some embodiments, the current time period refers to the current time period in which interference fitting and removal processing are being performed. The removed physiological signal sequence refers to the signal sequence obtained after removing interference from the original physiological signal sequence using the fitted interference signal sequence. It essentially reflects the residual difference between the currently fitted interference signal sequence and the real interference signal in the physiological signal, and therefore is used as an error signal sequence.

[0086] "Position-wise multiplication" refers to multiplying the error signal at the same moment with the synthetic sway one by one, used to characterize the direction and degree of deviation of the weight coefficients at the current moment. The preset step size refers to a pre-set weight update amplitude coefficient used to control the speed and stability of weight adjustment, avoiding oscillations caused by excessively fast updates or slow convergence caused by excessively slow updates. "Correction" can refer to multiplying the product of the position-wise multiplication with the preset step size, thereby constraining and smoothing the weight update amplitude. The weight correction sequence is a time series used to adjust and compensate the current weight coefficient sequence, reflecting the magnitude and direction of the weight coefficient correction required at each moment.

[0087] In some embodiments, "alignment integration" can refer to the point-by-point superposition of the weighted correction sequence at the same moment with the weighted coefficient sequence of the current time period. The weighted coefficient sequence for the next time period is an updated and iteratively applied weighted parameter sequence suitable for the next time period, which can better reflect the actual changes in body sway and physiological disturbances at the next moment. Through the above adjustments, the weighted coefficient sequence can be adaptively and iteratively optimized as the object's state changes, continuously improving the matching degree between the subsequent fitted interference signal and the real interference signal.

[0088] In the above embodiments, by using the de-interference physiological signal sequence as an error signal sequence, multiplying it in alignment with the synthetic sway sequence, and combining it with a preset step size to obtain a weight correction sequence, and then integrating it with the current weight coefficient sequence, adaptive real-time iterative updates of the weight coefficient sequence are achieved. This allows for dynamic adjustments to subsequent weighting parameters based on the current interference removal effect, continuously narrowing the difference between the fitted interference signal and the actual interference signal, and improving the accuracy of interference fitting. The point-by-point alignment operation and preset step size constraint method ensures both the timeliness and sensitivity of weight updates, while effectively suppressing the impact of noise and abnormal fluctuations, making the weight adjustment process more stable, faster in convergence, and more robust. Furthermore, this adaptive adjustment process is entirely based on software algorithms, requiring no additional hardware detection units. Without increasing hardware costs, altering the original hardware structure, or increasing device weight, it further improves the accuracy and continuity of physiological signal de-interference, making the overall solution low-cost, high-real-time, and highly anti-interference, better adaptable to the physiological signal detection needs of different objects in different states.

[0089] In an exemplary embodiment, the method further includes: obtaining equilibrium state measurement parameters based on the pressure center coordinates obtained at multiple times, the equilibrium state measurement parameters including at least one of the following: the trajectory length formed by connecting multiple pressure center coordinates, the area of ​​the closed region obtained by fitting multiple pressure center coordinates, or the dispersion value of the coordinate values ​​on each coordinate axis; and obtaining the equilibrium score of the object based on the equilibrium state measurement parameters.

[0090] In some embodiments, the pressure center coordinates acquired at multiple moments refer to a series of pressure center coordinates calculated at consecutive different moments as the data basis. This series of coordinates completely records the object's center of gravity movement process during the detection process. The balance state measurement parameter is an evaluation parameter used to quantify the object's body stability and swaying amplitude from different dimensions, objectively reflecting the body's balance ability and swaying state. The trajectory length refers to the total length of the trajectory formed by smoothly connecting the sequentially acquired pressure center coordinates in chronological order. The longer the trajectory length, the larger the range of the object's center of gravity movement and the more obvious the body swaying. The area of ​​the closed region refers to the size of the closed region enclosed by the envelope fitting of multiple pressure center coordinates; the larger the area, the more dispersed the object's center of gravity distribution and the worse the balance stability. The dispersion value of the coordinate values ​​on each coordinate axis refers to the fluctuation range, variance, or standard deviation of the pressure center coordinate values ​​in the horizontal and vertical directions, used to characterize the dispersion and stability of the object's center of gravity shift in different directions. "At least one" means that any one of the trajectory length, region area, and dispersion value can be used alone, or multiple parameters can be combined to improve the comprehensiveness and accuracy of the equilibrium state evaluation.

[0091] In some embodiments, a balance score can refer to an intuitive and quantifiable numerical result obtained by normalizing, weighting, or mapping the balance state measurement parameters. This numerical result can be used to comprehensively characterize an individual's physical balance ability and stability. A higher balance score indicates less body swaying, a more stable center of gravity, and better balance during the testing process. Balance scores can be applied to various scenarios, such as individual health management, fall risk warning, and quantitative assessment of rehabilitation effects.

[0092] In some embodiments, when the balance score is applied to the object's health management, the balance score obtained for each measurement of the object is determined and a trend graph is generated. If the trend graph of the object's balance score deviates significantly from the individual baseline, a monitoring reminder is pushed.

[0093] In some embodiments, where balance scores are used to quantitatively assess rehabilitation effectiveness, patients' balance scores are measured periodically during rehabilitation training to plot rehabilitation progress curves, objectively evaluate the effectiveness of the rehabilitation program, and provide data support for adjusting the treatment plan.

[0094] In the above embodiments, by extracting balance state measurement parameters such as trajectory length, closed region area, or coordinate axis dispersion value based on the pressure center coordinates at multiple consecutive time points, and further obtaining the object's balance score, it is possible to simultaneously achieve physiological signal interference removal and quantitative assessment of body balance state without adding any hardware or changing the original detection process. This integrates multiple detection functions on a single device, enhancing its practicality and application value. Specifically, using at least one of trajectory length, region area, and coordinate dispersion value as the evaluation criterion can comprehensively and accurately reflect the center of gravity movement pattern and body stability from multiple dimensions, making the balance score objective, reliable, and highly valuable. Furthermore, the balance score has wide applications, expanding the device's application scenarios.

[0095] In an exemplary embodiment, the physiological signals include electrocardiogram (ECG) signals and pulse signals; the physiological signal processing method further includes: extracting a portion of the pulse signal generated after the occurrence time of the R wave in the interference-free ECG signal; obtaining the ratio between the pulse ratio of the red light channel and the pulse ratio of the infrared light channel in the portion of the pulse signal; and obtaining the blood oxygen saturation based on the ratio.

[0096] In some embodiments, the de-interference physiological signals include de-interference electrocardiogram (ECG) signals and de-interference pulse signals. The R-wave in the ECG is the most prominent and easily identifiable positive peak. The occurrence time of the R-wave is used to characterize the accurate onset time of the heartbeat, providing a reliable time reference for subsequent pulse signal analysis. Extracting a portion of the pulse signal generated after the occurrence time refers to extracting a segment of the pulse signal after the R-wave occurrence time. This portion of the signal has a strict physiological correspondence with the heartbeat and can effectively improve the accuracy of subsequent blood oxygen calculations. Specifically, a portion of the pulse signal can be obtained based on PPG (Photoplethysmography) measurements taken within a specific delay window after R-wave extraction. Based on this portion of the pulse signal, the AC and DC components can be further obtained, and the peak-to-peak value in the AC component can be determined, representing the change in projected light caused by changes in arterial blood volume; the minimum value in the DC component can be determined as the limit value, at which point the contribution of arterial blood is minimal. The red light channel and the infrared light channel are different wavelength optical paths used to collect pulse signals. The two types of channels have different absorption characteristics for oxyhemoglobin and deoxyhemoglobin in the blood. By using the R wave as the gating reference, irrelevant bands can be excluded, thereby improving the calculation stability of the red light / infrared pulse ratio.

[0097] In some embodiments, for the red light channel and the infrared light channel in a portion of the pulse signal, the pulse ratio Φ_red of the red light channel is obtained, and the specific calculation formula is shown in formula (4). The pulse ratio Φ_ir of the infrared light channel is calculated as shown in formula (5). The ratio R between the pulse ratio of the red light channel and the pulse ratio of the infrared light channel is determined, and the specific calculation formula is shown in formula (6). The ratio R reflects the difference in absorption of arterial blood at the two wavelengths. Based on the ratio R, the blood oxygen saturation SpO2 is obtained, and the specific calculation formula is shown in formula (7).

[0098] Φ_red = AC_red / DC_red (4)

[0099] Φ_ir = AC_ir / DC_ir (5)

[0100] R = Φ_red / Φ_ir (6)

[0101] SpO2 = A + B * R (7)

[0102] Wherein, AC_red is the AC component of a portion of the pulse signal determined under the red light channel, DC_red is the DC component of a portion of the pulse signal determined under the red light channel, AC_ir is the AC component of a portion of the pulse signal determined under the infrared light channel, DC_ir is the DC component of a portion of the pulse signal determined under the infrared light channel, and A and B are obtained by least squares fitting through a large number of sample tests and with reference to standard test data (for example, under normal circumstances, A and B are 110 and -25, respectively). They can also be manually set by the user according to specific circumstances, and this application embodiment does not limit this.

[0103] In the above embodiments, by using interference-free ECG and pulse signals to calculate blood oxygen saturation, and accurately extracting corresponding pulse signal segments based on the R-wave timing of the ECG signal, the inherent correlation between physiological signals can be fully utilized, improving the targeting and effectiveness of pulse signal selection and avoiding interference from invalid signal segments in blood oxygen calculation. By calculating the pulse ratio of the red light channel and the infrared light channel separately and obtaining their ratio values, the differences in absorption of blood oxygen components by different wavelengths of light are fully utilized, making the basis for blood oxygen saturation calculation more reliable and the results more stable. At the same time, the entire blood oxygen calculation is based on high-quality physiological signals after motion interference removal, which can effectively avoid waveform distortion and calculation errors caused by body swaying. Without adding additional sensors, increasing hardware costs, or increasing equipment weight, the anti-interference capability and measurement accuracy of blood oxygen detection are improved. In addition, this method organically integrates physiological signal interference removal, ECG R-wave recognition, pulse signal segmentation, and blood oxygen calculation to form a coherent and integrated physiological signal processing flow. While reusing existing hardware resources, it simultaneously achieves high-precision anti-interference and high-reliability blood oxygen detection, expanding the detection function of the device.

[0104] In an exemplary embodiment, the physiological signals include electrocardiogram (ECG) signals and pulse signals; the physiological signal processing method further includes: acquiring the subject's height, age, body water percentage, the occurrence time of the R wave in the interference-free ECG signal, the heart rate obtained from the interference-free ECG signal, and the peak time in the interference-free pulse signal; acquiring the time difference between the occurrence time and the peak time; and integrating the height, age, body water percentage, heart rate, and time difference to obtain blood pressure.

[0105] In some embodiments, "acquisition" refers to obtaining a series of basic parameters for calculating blood pressure through methods such as data collection by the original sensors of the body measurement device (height and body water percentage can be derived from a weighing sensor combined with a preset algorithm, or obtained from other original detection modules integrated into the device), user input (age), or extraction from interference-free physiological signals (R-wave occurrence time, heart rate, peak time). Height is the longitudinal length of the subject's body, and age is the subject's physiological age; both are basic physiological factors affecting blood pressure. Body water percentage is the proportion of water in the subject's body, reflecting blood viscosity and circulatory status, and directly related to blood flow velocity. Heart rate is the heart rate calculated from interference-free electrocardiogram signals, reflecting the rhythm of the heart's circulation. The peak time in the interference-free pulse signal is the peak time when blood flow reaches the detection site after the heart pumps blood, characterizing the time node of blood flow transmission.

[0106] The time difference refers to the time interval between the occurrence of the R wave and the corresponding peak of the pulse wave within the same cardiac cycle. This time difference is essentially the pulse wave conduction time, which directly reflects the speed at which blood is transported from the heart to the detection site and is a dynamic parameter for blood pressure calculation.

[0107] In some embodiments, during the determination of blood pressure, the subject's height is incorporated to correct for the hydrostatic pressure of the subject when standing during the test, and the subject's body water content is incorporated to correct for the vascular stiffness of the subject during the test.

[0108] In some embodiments, "integration" can refer to substituting height, age, body water percentage, heart rate (static physiological parameter), and pulse wave transit time (dynamic parameter) into a preset blood pressure calculation model, and then fusing the multi-dimensional parameters into a quantitative value that can characterize the vascular pressure state of the subject through weighted calculations, fitting mapping, and other methods. Blood pressure is used to characterize the pressure exerted on the blood vessel walls by the heart when pumping blood, and can refer to the output of specific values ​​reflecting the subject's arterial systolic and diastolic blood pressure.

[0109] In the above embodiments, by integrating basic physiological parameters such as height, age, and body composition / water percentage, combined with the R-wave occurrence time and heart rate extracted from the interference-free electrocardiogram signal, and the peak time extracted from the interference-free pulse signal, the time difference (pulse wave conduction time) between the two is calculated and further integrated to obtain blood pressure. This eliminates the need for additional blood pressure sensors, fully reusing the existing hardware resources of the body measurement device and the interference-free physiological signals. This avoids the increased costs, complex circuit design, and increased device weight associated with additional hardware, effectively maintaining the advantages of a low-cost, simplified hardware solution. It also solves the problems of traditional blood pressure detection requiring additional equipment and being out of sync with physiological signal detection. Furthermore, by combining multi-dimensional physiological parameters and dynamic pulse wave conduction time for blood pressure calculation, the influence of individual physiological differences on blood pressure is fully considered. Compared to single-parameter calculation methods, this improves the accuracy and specificity of blood pressure detection. Moreover, all parameters are based on interference-free signals and existing hardware acquisition, ensuring data consistency and reliability.

[0110] Furthermore, this blood pressure detection process is organically integrated with the previously mentioned functions such as physiological signal interference removal, balance state assessment, and blood oxygen saturation detection, achieving integrated detection of multi-dimensional physiological indicators on a single device without additional operation or data collection. This enhances the practicality and application value of the device and further expands its detection scenarios.

[0111] In an exemplary embodiment, blood pressure is obtained by integrating height, age, body water percentage, heart rate, and time difference, including: obtaining the model coefficients for each of height, age, body water percentage, heart rate, and time difference; obtaining the ratio between the model coefficient corresponding to the time difference and the time difference; obtaining the product between height, age, body water percentage, and heart rate and their respective model coefficients; obtaining the sum of the ratios and products; and obtaining blood pressure based on the sum.

[0112] In some embodiments, the model coefficients a2 for height, a4 for age, a3 for body water content, a5 for heart rate, and a1 for time difference are determined; the ratio between the model coefficient corresponding to the time difference and the time difference is obtained; the product between height, age, body water content, and heart rate and the corresponding model coefficient is obtained; the sum of the ratio and the product is obtained; based on the sum, blood pressure is obtained; the specific calculation formula for systolic blood pressure BP_sys is shown in formula (8); the specific calculation formula for diastolic blood pressure BP_dia is shown in formula (9).

[0113] BP_sys=a1 / PTT+a2×height+a3×TBW%+a4×age+a5×HR+b(8)

[0114] BP_dia=c1 / PTT+c2×height+c3×TBW%+c4×age+c5×HR+d(9)

[0115] Wherein, PTT is time difference, TBW% is body water percentage, HR is heart rate, and a1, a2, a3, a4, a5, b, c1, c2, c3, c4, c5, and d are obtained by curve fitting from a large number of sample tests and comparison with standard blood pressure measurement results. These values ​​can also be set by the user according to specific circumstances, and the implementation example itself does not limit this.

[0116] In the above embodiments, by configuring corresponding model coefficients for height, age, body composition water percentage, heart rate, and time difference, and using a weighted fusion method combining ratios and products, differentiated and refined weighted processing of different physiological parameters can be achieved. This ensures that the contribution of each parameter to blood pressure is reasonably reflected, and the calculation model has clear physical meaning and strong numerical stability. By using a ratio form for the time difference alone and a product form for the other parameters, the core role of pulse wave transit time in blood pressure calculation is strengthened, and the effective fusion of multiple human characteristic parameters is achieved, improving the accuracy and individual adaptability of blood pressure calculation. Finally, the entire calculation process is based on fixed operation rules, with simple logic and low computational load, making it easy to execute in real time in embedded devices. At the same time, the interference-free physiological signals and basic human parameters are reused throughout the process, without the need for additional sensors and detection modules. This further consolidates the advantages of low cost, low hardware complexity, and no increase in device weight, enabling blood pressure detection to maintain high reliability and practicality even in motion interference scenarios, and achieving integrated, multi-parameter, and high-precision physiological signal processing.

[0117] To illustrate the physiological signal processing method in this application in detail, an embodiment is described below. For example, this application describes a physiological signal processing method in a specific scenario, as shown in the detailed flowchart below. Figure 4 As shown.

[0118] First, the weighing pressure values ​​collected by the sensors at different times and the coordinates of the sensors that collected the corresponding weighing pressure values ​​are determined by the weighing body. The coordinates of the pressure center at different times are also determined. The distance between every two consecutive pressure center coordinates is determined. The ratio between the distance and the acquisition interval is used as the corresponding synthetic sway. All synthetic sway within the preset time period are calculated and integrated in chronological order to obtain the synthetic sway sequence.

[0119] Determine the synthetic sway sequence and physiological signal sequence for the time period synchronization, and determine the fitting interference signal sequence based on the synthetic sway sequence and weighting coefficient sequence for the current time period.

[0120] Subtracting the calculated fitted interference signal sequence from the current physiological signal sequence yields the noise-reduced, or interference-free, physiological signal sequence. Using the synthetic sway sequence and the interference-free physiological signal sequence from the current time period, the fitted interference signal sequence is determined. Then, using the fitted interference signal sequence, the synthetic sway sequence, and the weighted coefficient sequence from the current time period, the weighted coefficient sequence for the next time period is determined, along with a new fitted interference signal sequence. Subtracting this new fitted interference signal sequence from the physiological signal sequence yields the interference-free physiological signal sequence for the next time period.

[0121] Based on the pressure center coordinates obtained at multiple times, equilibrium state measurement parameters are obtained. The equilibrium state measurement parameters include at least one of the following: the trajectory length formed by connecting multiple pressure center coordinates, the area of ​​the closed region obtained by fitting multiple pressure center coordinates, or the dispersion value of the coordinate values ​​on each coordinate axis. Based on the equilibrium state measurement parameters, the equilibrium score of the object is obtained.

[0122] Physiological signals include electrocardiogram (ECG) signals and pulse signals. Based on the occurrence time of the R wave in the interference-free ECG signal, a portion of the pulse signal generated after the occurrence time is extracted from the interference-free pulse signal. For the red light channel and infrared light channel in the portion of the pulse signal, the ratio between the pulse ratio of the red light channel and the pulse ratio of the infrared light channel is obtained. Based on the ratio, blood oxygen saturation is obtained.

[0123] The system obtains the subject's height, age, body water percentage, the time of R wave occurrence in the interference-free ECG signal, the heart rate obtained from the interference-free ECG signal, and the peak time in the interference-free pulse signal; it also obtains the time difference (PTT) between the occurrence time and the peak time; and it integrates the height, age, body water percentage, heart rate, and time difference to obtain the subject's systolic and diastolic blood pressure.

[0124] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0125] Based on the same inventive concept, this application also provides a physiological signal processing device for implementing the physiological signal processing method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more physiological signal processing device embodiments provided below can be found in the limitations of the physiological signal processing method described above, and will not be repeated here.

[0126] In one exemplary embodiment, such as Figure 5 As shown, a physiological signal processing device is provided, including: an acquisition module 501 and an adjustment module 502, wherein:

[0127] The acquisition module 501 is used to acquire a synthetic sway sequence based on the weighing pressure value to characterize the body sway of the object during weighing.

[0128] The acquisition module 502 is further configured to acquire a fitted interference signal sequence based on the synthetic sway sequence and weight coefficient sequence of the current time period, for the time-synchronized synthetic sway sequence and physiological signal sequence.

[0129] The acquisition module is further configured to acquire the de-interference physiological signal sequence based on the fitted interference signal sequence and the physiological signal sequence in the current time period.

[0130] The adjustment module is used to adjust the weight coefficient sequence of the current time period based on the synthetic sway sequence and the de-interference physiological signal sequence to obtain the weight coefficient sequence of the next time period, which is then used to de-interference the physiological signal sequence of the next time period.

[0131] Each module in the aforementioned physiological signal processing device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0132] In one exemplary embodiment, a body measurement device is provided, including a memory, a processor, and at least one sensor. The memory stores a computer program, and the at least one sensor is used to detect physiological signals and weighing pressure values ​​of an object. When the processor executes the computer program, it implements the steps of any physiological signal processing method.

[0133] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the above-described physiological signal processing methods.

[0134] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described physiological signal processing methods.

[0135] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of any of the above-described physiological signal processing methods.

[0136] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a physiological signal processing method.

[0137] The display unit of this computer device is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of this computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad set on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0138] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0139] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0140] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0141] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0142] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A body measurement device, characterized in that, include: Holding unit and weighing body; The controller is used to determine the synthetic sway amount using the weighing pressure value detected by the weighing body, and to remove the sway interference signal from the physiological signal detected by the gripping unit based on the synthetic sway amount.

2. The device according to claim 1, characterized in that, The gripping unit includes a first sensor, which is used to detect physiological signals of the object; The weighing body includes a second sensor, which is used to detect the weighing pressure value of the object.

3. A physiological signal processing method, characterized in that, Applied to a body measurement device, the body measurement device includes a first sensor and a second sensor, the first sensor being used to detect physiological signals of the subject, and the second sensor being used to detect the subject's weighing pressure value; including: Based on the weighing pressure value, a synthetic swaying sequence is obtained to characterize the body swaying of the object during weighing; For time-synchronized synthetic sway sequences and physiological signal sequences, a fitted interference signal sequence is obtained based on the synthetic sway sequence and weight coefficient sequence in the current time period. Based on the fitted interference signal sequence and the physiological signal sequence in the current time period, obtain the interference-free physiological signal sequence; Based on the synthetic sway sequence and the de-interference physiological signal sequence in the current time period, the weight coefficient sequence in the current time period is adjusted to obtain the weight coefficient sequence in the next time period, which is used to de-interference the physiological signal sequence in the next time period.

4. The method according to claim 3, characterized in that, The weighing pressure values ​​are multiple and detected by sensors at a corresponding number of different locations; the step of obtaining a synthetic sway sequence based on the weighing pressure values ​​to characterize the body swaying of the object during weighing includes: Based on multiple weighing pressure values ​​and the location coordinates of the corresponding sensors, the coordinates of the pressure center are obtained; For pressure center coordinates acquired at multiple times, the distance between every two consecutive pressure center coordinates is obtained. The ratio between the distance between any two consecutive pressure center coordinates and the acquisition interval is obtained as the corresponding synthetic sway quantity, resulting in a synthetic sway quantity sequence composed of multiple synthetic sway quantities.

5. The method according to claim 4, characterized in that, The process of obtaining the pressure center coordinates based on multiple weighing pressure values ​​and the corresponding sensor location coordinates includes: Based on multiple weighing pressure values, the pressure distribution ratio under multiple positioning references is obtained; By integrating the pressure distribution ratios from multiple positioning references with the location coordinates of the corresponding sensors, the pressure center coordinates are obtained.

6. The method according to claim 5, characterized in that, The process involves obtaining the pressure distribution percentage under multiple positioning references based on multiple weighing pressure values; integrating the pressure distribution percentages under multiple positioning references with the location coordinates of the corresponding sensors to obtain the pressure center coordinates, including: Take a portion of the weighing pressure values ​​as the first set of weighing pressure values, and take the remaining weighing pressure values ​​as the second set of weighing pressure values; obtain the first sum of the first set of weighing pressure values, the second sum of the second set of weighing pressure values, and the sum of the two; Obtain the first ratio between the first sum and the total sum, and determine the first direction and the calibration distance in the first direction based on the location coordinates of the corresponding sensors of the first set of weighing pressure values; Obtain the second ratio between the second sum and the total sum, and determine the second direction and the calibration distance in the second direction based on the location coordinates of the corresponding sensors of the second set of weighing pressure values; Based on the first ratio and the second ratio, the calibration distances in the first direction and the second direction are integrated to obtain the pressure center coordinates.

7. The method according to claim 3, characterized in that, The process involves adjusting the weighting coefficient sequence for the current time period based on the synthetic sway sequence and the de-interferenced physiological signal sequence to obtain the weighting coefficient sequence for the next time period, including: The physiological signal sequence after interference removal is used as the error signal sequence between the fitted interference signal sequence and the real interference signal. The error signal sequence is multiplied with the synthetic sway sequence in the current time period, and the product result is corrected according to a preset step size to obtain the weighted correction sequence. The weight correction sequence is aligned and integrated with the weight coefficient sequence of the current time period to obtain the weight coefficient sequence of the next time period.

8. The method according to claim 4, characterized in that, The method further includes: Based on the pressure center coordinates obtained at multiple times, equilibrium state measurement parameters are obtained. The equilibrium state measurement parameters include at least one of the following: the trajectory length formed by connecting multiple pressure center coordinates, the area of ​​the closed region obtained by fitting multiple pressure center coordinates, or the dispersion value of the coordinate values ​​on each coordinate axis. Based on the equilibrium state measurement parameters, the balance score of the object is obtained.

9. The method according to any one of claims 3 to 8, characterized in that, The physiological signals include electrocardiogram signals and pulse signals; the method further includes: Based on the occurrence time of the R wave in the interference-free ECG signal, a portion of the pulse signal generated after the occurrence time is extracted from the interference-free pulse signal. For the red light channel and infrared light channel in the aforementioned pulse signal, the ratio between the pulse ratio of the red light channel and the pulse ratio of the infrared light channel is obtained; Based on the ratio, blood oxygen saturation is obtained.

10. The method according to any one of claims 3 to 8, characterized in that, The physiological signals include electrocardiogram signals and pulse signals; the method further includes: The height, age, body composition (water percentage), occurrence time of the R wave in the interference-free electrocardiogram (ECG) signal, heart rate obtained from the interference-free ECG signal, and peak time in the interference-free pulse signal of the subject are obtained. Obtain the time difference between the occurrence time and the peak time; integrate the height, age, body water content, heart rate and the time difference to obtain blood pressure.

Images