A calibration device and calibration method for a bioelectrical impedance analyzer

Abstract

This invention relates to the field of bioelectrical impedance analysis (BIA) technology, and more particularly to a calibration device and method for a BIA. The technical solution is as follows: A calibration device for a BIA, the device being connected to a BIA host via a communication interface and a calibration port. The device includes: a main control unit for communicating with the host and parsing instructions from the host; a high-speed multiplexer, the control terminal of which is connected to the main control unit, and the output terminal connected to the calibration port; and a standard impedance network composed of multiple high-precision passive components, including resistors, capacitors, inductors, and their series-parallel combinations, wherein each impedance component is connected to a different input channel of the high-speed multiplexer. Through a highly integrated standard impedance network and an automatic switching mechanism, the calibration process is fully automated, completely eliminating human error and greatly improving calibration efficiency and consistency.

Description

Technical Field

[0001] This invention relates to the field of bioelectrical impedance analysis (BIA) technology, and more particularly to a calibration device and calibration method for a BIA. Background Technology

[0002] In the field of electronic measurement instrument technology, bioelectrical impedance analysis (BIA) instruments are important tools for medical diagnosis and body composition analysis. The accuracy of their measurement results is crucial, thus requiring regular calibration. Currently, the calibration of such instruments generally relies on operators manually switching between a series of discrete, high-precision resistor or capacitor standards. This traditional method is not only cumbersome and inefficient, heavily dependent on operator skill, but more importantly, the calibration references it provides are overly idealized (mostly pure resistors or pure capacitors), making it difficult to simulate the complex impedance behavior (i.e., impedance characteristics containing multiple components such as resistance, capacitance, and inductance) exhibited by real biological tissues at different frequencies. This limitation leads to incomplete calibration results, making it difficult to ensure the wideband measurement accuracy of the instrument in practical applications.

[0003] Existing calibration methods have several core problems:

[0004] First, the manual operation mode introduces unavoidable contact errors and operational inconsistencies, affecting the repeatability and reliability of calibration.

[0005] Secondly, the singularity and idealization of the calibration benchmark prevent the instrument from obtaining calibration data for complex impedance characteristics, thus limiting its accuracy when measuring real biological tissues.

[0006] Finally, the cumbersome process makes it extremely difficult to conduct large-scale, multi-frequency data acquisition, thus hindering the application of advanced calibration algorithms (such as dynamic error models based on massive amounts of data). Summary of the Invention

[0007] This invention proposes a calibration device and calibration method for a bioelectrical impedance analyzer, which solves the problems of low efficiency and poor consistency caused by manual operation of discrete standard parts for calibration in the prior art, as well as the inability to truly simulate the complex impedance characteristics of biological tissues due to the overly idealized calibration benchmark, thus restricting the final measurement accuracy of the bioelectrical impedance analyzer.

[0008] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0009] A calibration device for a bioelectrical impedance analyzer, the device being connected to the bioelectrical impedance analyzer host via a communication interface and a calibration port, the device comprising:

[0010] The main control unit is used to communicate with the host of the test instrument and parse instructions from the host.

[0011] A high-speed multiplexer, the control terminal of which is connected to the main control unit, and the output terminal of which is connected to the calibration port;

[0012] The standard impedance network consists of multiple high-precision passive components, including resistors, capacitors, inductors and their series and parallel combinations, wherein each impedance element is connected to a different input channel of the high-speed multiplexer.

[0013] A communication interface is connected to the main control unit and is used to realize data interaction with the test instrument host.

[0014] Furthermore, the main control unit is a microcontroller; the high-speed multiplexer is an analog switch with a bandwidth higher than 200MHz; the resistive elements in the standard impedance network have an accuracy of not less than ±0.1% and a temperature coefficient of less than ±25ppm / °C, and the capacitor elements are NPO / COG ceramic capacitors or mica capacitors with an accuracy of not less than ±0.1%; the communication interface is an SPI, I2C, or USB interface; the device is encapsulated in a shielded housing, and its internal printed circuit board adopts a design of at least four layers and has a complete ground plane layer; the calibration port is a coaxial interface; and the standard impedance network includes at least one RC parallel network and at least one CL series network.

[0015] A calibration method based on the above-mentioned calibration device, wherein the method is executed by the test instrument host and includes the following stages performed sequentially:

[0016] Calibration initialization and self-test phase: The test instrument host sends a start command through the communication interface, the calibration device powers on and performs a self-test, and reports status information; the start command includes the calibration mode, frequency point list and channel sequence.

[0017] Cyclic data acquisition and online verification stage: The main control unit controls the multiplexer to switch channels cyclically according to the channel sequence, and the test instrument host performs frequency sweep measurement on each channel, while judging the validity of the acquired data online;

[0018] Error modeling and model validation phase: An error correction model is established using all valid data, and the accuracy of the model is validated using a set of standard impedance data for validation.

[0019] Furthermore, the self-test in the calibration initialization and self-test phase includes: the main control unit sequentially switches the multiplexer to several known fixed impedance channels, the test instrument host performs rapid measurement, and if the deviation between the measurement result and the expected value is within the preset tolerance, the device is determined to be in normal condition; otherwise, the calibration is stopped and an error is reported. In addition, the calibration mode parameters further include the excitation current level setting. The test instrument host dynamically adapts the applied excitation current magnitude according to the theoretical value range of the selected standard impedance element to ensure that the measurement signal is in the optimal quantization range.

[0020] Furthermore, the cyclic data acquisition and online verification phase specifically includes:

[0021] a. Channel switching and stabilization: The main control unit controls the multiplexer to switch to the target channel and waits for a configurable stabilization delay td, which is dynamically set according to the type and capacitance value of the target impedance element;

[0022] b. Data acquisition: The main unit of the test instrument measures the amplitude and phase of the current impedance at each frequency specified in the frequency point list;

[0023] c. Online verification: Compare the measurement results of the current channel at key frequency points with the pre-stored upper and lower limits of the theoretical values ​​of the channel. If the measurement results exceed the limits multiple times in a row, the channel data is determined to be invalid and a channel fault flag is recorded.

[0024] Furthermore, the cyclic data acquisition and online verification phase also employs an alternating redundant acquisition strategy: after completing the first round of acquisition of all channels, the second round of acquisition is immediately performed with the opposite channel sequence; for each frequency point of each channel, the average value of the two rounds of acquisition data is taken as the final valid value; if the difference between the two rounds of data is too large, the third acquisition of that point is triggered, and the average value of the two closest data points among the three rounds is taken; in addition, in each frequency sweep measurement, each frequency point is sampled multiple times, and the sampled data is processed in real time using the moving average filtering algorithm in digital signal processing to suppress random noise.

[0025] Furthermore, the error modeling and model verification stage adopts a two-dimensional interpolation algorithm based on frequency and impedance amplitude as bivariates; the modeling process assigns weights to the measurement data of different channels, and the weight factor depends on the accuracy level of the impedance element of the channel itself and the measurement signal-to-noise ratio calculated during the acquisition process; the modeling algorithm further introduces a temperature compensation term, the coefficient of which is obtained by querying a pre-stored temperature-error characteristic curve table corresponding to the readings of the internal temperature sensor of the device.

[0026] Furthermore, the model verification specifically involves: selecting at least one RC parallel network channel in the standard impedance network that was not involved in the modeling as the verification benchmark, using the newly established error model to compensate for the measurement data of this channel, and calculating the residual error after compensation; if the residual error is less than the maximum error allowed by the system, the model verification is successful; otherwise, the calibration process is judged as a failure, and a prompt is made to check the device or tester; the verification process also includes phase error verification to ensure that the phase response after compensation is within the required range.

[0027] Furthermore, if model verification fails, the system automatically switches to a downgraded calibration mode: ignoring previously marked invalid channels and frequencies with excessive errors, and using only verified high-reliability channels and data, a simplified error model is reconstructed that is applicable to certain frequency bands and ranges, with a narrower range but guaranteed accuracy. In downgraded mode, the system evaluates the coverage of the remaining valid channels and generates a detailed downgraded calibration report, indicating the precise frequency boundaries and impedance ranges applicable to the current model.

[0028] Furthermore, the method also includes a calibration traceability and data archiving stage: After the complete calibration is completed, the system automatically generates a structured calibration report, the contents of which include, but are not limited to: calibration timestamp, channel sequence used, invalid channel list, final model parameters, model verification results, and logs of all original measurement data and compensated data; the report is encrypted and stored in the non-volatile memory of the test instrument host for subsequent quality traceability and analysis; furthermore, the system analyzes the historical calibration report data from multiple consecutive tests, uses a trend prediction algorithm to predict the performance degradation of key components, and proactively issues an early warning when the predicted value approaches the tolerance threshold.

[0029] The positive effects of this invention are:

[0030] Through a highly integrated standard impedance network and automatic switching mechanism, the calibration process is fully automated, completely eliminating human error and greatly improving calibration efficiency and consistency.

[0031] Secondly, thanks to the carefully selected high-precision passive components and professional electromagnetic shielding design, the accuracy and stability of the standard reference signal are guaranteed from the source, ensuring high calibration precision. Most importantly, the device's built-in composite impedance network provides a rich set of complex impedance standards, effectively simulating the frequency relaxation characteristics of biological tissues. This provides a physical basis for full-parameter calibration of the instrument, significantly improving its measurement accuracy in practical applications.

[0032] Ultimately, this solution provides a reliable hardware platform for the test instrument host to execute advanced intelligent calibration algorithms based on big data acquisition, thus promoting the intelligent development of calibration technology. Attached Figure Description

[0033] Figure 1 This is a block diagram of the internal structure of the calibration device in this invention and a schematic diagram of its connection with the main unit of the testing instrument;

[0034] Figure 2 This is a schematic diagram of the calibration process in this invention. Detailed Implementation

[0035] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0036] Example 1

[0037] A calibration device for a bioelectrical impedance analyzer, the device being connected to the bioelectrical impedance analyzer host via a communication interface and a calibration port, the device comprising:

[0038] The main control unit is used to communicate with the host of the test instrument and parse instructions from the host.

[0039] A high-speed multiplexer, the control terminal of which is connected to the main control unit, and the output terminal of which is connected to the calibration port;

[0040] The standard impedance network consists of multiple high-precision passive components, including resistors, capacitors, inductors and their series and parallel combinations, wherein each impedance element is connected to a different input channel of the high-speed multiplexer.

[0041] A communication interface is connected to the main control unit and is used to realize data interaction with the test instrument host.

[0042] In its implementation, the physical form is a separate metal box with a communication interface and calibration port. The core internal component is a precision-designed 4-layer printed circuit board (PCB). The main control unit uses a microcontroller based on the ARM Cortex-M core (such as STMicroelectronics' STM32F103 series), which is responsible for running the control logic. The high-speed multiplexer uses Analog Devices' ADG series 16-channel analog switches (such as the ADG1606), with a -3dB bandwidth exceeding 200MHz and an on-resistance as low as a few ohms, ensuring high-fidelity switching of high-frequency signals. The standard impedance network consists of high-precision components soldered onto the PCB. For example, the resistors are five low-temperature drift metal film resistors manufactured by Vishay with an accuracy of ±0.1%, covering resistance values ​​from 10Ω to 4.7kΩ; the capacitors are five NPO (COG) ceramic capacitors manufactured by Murata, with capacitance values ​​covering 100pF to 100nF, also with an accuracy of ±0.1%. The communication interface specifically adopts a Serial Peripheral Interface (SPI), with a standard 4-pin socket (including SCLK, MOSI, MISO, and CS signal lines) designed on the PCB for high-speed, full-duplex data exchange with the test instrument host. The calibration port uses an SMA coaxial interface, with its center conductor connected to the output of the multiplexer via a microstrip line with strictly controlled characteristic impedance.

[0043] The core concept is "controlled switching and measurement." When the device receives a command from the host tester via the SPI interface, the main control unit (microcontroller) parses the command and identifies the standard impedance value required by the host. Subsequently, the main control unit sends the corresponding channel selection code to the high-speed multiplexer (ADG1606) via its general purpose input / output (GPIO) pins or a dedicated serial control bus. Based on this code, the multiplexer closes its corresponding internal analog switch, physically connecting the specified resistor, capacitor, or composite network element (such as a parallel combination of a 100Ω resistor and a 10nF capacitor) in the standard impedance network to the common calibration port (SMA interface). At this time, the host tester applies an AC excitation signal of known frequency and amplitude to the calibration port through its measurement electrodes and measures the current flowing through the standard element and the port voltage, thereby calculating the current impedance value. The entire process is programmatically controlled by the host, automatically switching multiple standard elements sequentially to quickly complete data acquisition.

[0044] Figure 1 The internal core components of the device of the present invention are clearly shown, as well as the connection relationship between the components, and the interaction method between it and the host tester is shown.

[0045] By integrating the main control unit, high-speed multiplexer, and standard impedance network, a unified calibration hardware platform was constructed. Its core benefit lies in achieving a high degree of integration and automation of the calibration process, completely changing the traditional cumbersome mode of relying on manual replacement of discrete standard components. This not only significantly improves calibration efficiency but also fundamentally eliminates contact resistance and consistency errors introduced by improper manual operation, laying a solid hardware foundation for rapid and reliable automated calibration.

[0046] The main control unit is a microcontroller; the high-speed multiplexer is an analog switch with a bandwidth higher than 200MHz; the resistive elements in the standard impedance network have an accuracy of not less than ±0.1% and a temperature coefficient of less than ±25ppm / °C, and the capacitor elements are NPO / COG ceramic capacitors or mica capacitors with an accuracy of not less than ±0.1%; the communication interface is an SPI, I2C, or USB interface; the device is encapsulated in a shielded housing, and its internal printed circuit board adopts a design of at least four layers and has a complete ground plane layer; the calibration port is a coaxial interface; and the standard impedance network includes at least one RC parallel network and at least one CL series network.

[0047] The specific microcontroller model can be STM32F103C8T6, with an operating frequency of 72MHz, providing sufficient processing power. The analog switch ADG1606 has a bandwidth of 200MHz, and its low on-resistance ensures that no significant errors are introduced when measuring small impedances. Resistors in the standard impedance network, such as the 1kΩ resistor, are selected from Vishay's PNM series, with an accuracy of ±0.1% and a temperature coefficient as low as ±15ppm / °C. Capacitors, such as the 1nF capacitor, are selected from Murata's GRM series COG capacitors, with an accuracy of ±0.1%. In addition to SPI, the communication interface can also be an I2C bus pad pre-installed on the PCB or a Micro-USB connector can be installed to achieve communication through the microcontroller's built-in USB device controller. The entire device is encapsulated in a shielded housing machined from aluminum alloy using CNC machining, and the housing is grounded. The internal PCB has a 4-layer structure, with the second layer being a complete ground plane and the fourth layer a power plane. This design provides a complete return path for high-frequency signals, effectively reducing electromagnetic interference and signal integrity issues. The calibration port is locked to the mounting hole on the housing. The RC parallel network consists of a 100Ω resistor and a 10nF capacitor connected in parallel, and then connected to an independent channel of the multiplexer; the CL series network consists of a 10nF capacitor and a high-Q wire-wound inductor with a rated inductance of 10μH connected in series, and then connected to another independent channel.

[0048] Carefully selected components and structural design ensure long-term stability and high-frequency accuracy of the reference. High-precision, low-temperature-drift resistors and capacitors guarantee minimal variation in the provided standard impedance value under different ambient temperatures, ensuring calibration accuracy from the outset. A complete grounded shielded enclosure and a four-layer PCB design work together to form a Faraday cage, effectively resisting external electromagnetic interference while minimizing internal high-frequency signal radiation. A complete ground plane reduces the signal loop area, thereby minimizing parasitic inductance and capacitance. RC parallel and CL series networks provide a frequency-dependent complex impedance standard, better simulating the impedance characteristics (i.e., impedance spectrum) of biological tissue at different frequencies. This allows calibration not only for purely resistive or purely capacitive loads but also to cover complex phase information, providing a physical basis for full-parameter (amplitude and phase) calibration of the test instrument.

[0049] The integrated design results in significant optimization of overall performance and improved reliability. Carefully selected high-precision, low-temperature-drift components ensure the long-term stability of the standard reference; professional shielding and PCB design guarantee signal integrity at high frequencies; and the introduction of a composite impedance network enables the device to simulate the complex impedance characteristics more closely resembling those of real biological tissue, thereby supporting comprehensive amplitude and phase calibration of the test instrument, greatly expanding the calibration coverage and practicality.

[0050] Example 2

[0051] A calibration method based on the calibration device described in Embodiment 1, wherein the method is executed by the test instrument host and includes the following stages performed sequentially:

[0052] Calibration initialization and self-test phase: The test instrument host sends a start command through the communication interface, the calibration device powers on and performs a self-test, and reports status information; the start command includes the calibration mode, frequency point list and channel sequence.

[0053] Cyclic data acquisition and online verification stage: The main control unit controls the multiplexer to switch channels cyclically according to the channel sequence, and the test instrument host performs frequency sweep measurement on each channel, while judging the validity of the acquired data online;

[0054] Error modeling and model validation phase: An error correction model is established using all valid data, and the accuracy of the model is validated using a set of standard impedance data for validation.

[0055] In implementation, this method is dominated by the calibration management software module running on the host test instrument. During the initialization phase, the host software sends a specific command frame via the SPI interface, such as 0xAA (start character), 0x01 (calibration mode code), and 0x0F (bit mask for the channel under test, indicating the use of the first 8 channels). Upon receiving this frame, the main control unit first controls the multiplexer to sequentially switch to channel 0 (e.g., a 10Ω resistor) and channel 5 (e.g., a 1nF capacitor). The host then quickly performs measurements at 1kHz and 100kHz frequencies, comparing the readings with standard values ​​stored in the host software database.

[0056] The process involves phased automation control and verification. Initial self-testing, like a "power-on self-diagnosis," quickly verifies the basic functionality of the device, preventing the use of a faulty reference for calibration. The core phase is cyclic data acquisition, which transforms the traditionally tedious task of manually plugging and unplugging standard parts dozens of times into a high-speed, unmanned automated process through a "command-driven, automatic switching, batch measurement" model, resulting in a significant improvement in efficiency and consistency. Online verification performs quality control at the source of data generation, preventing invalid data from entering subsequent modeling stages. The final error modeling and verification phase utilizes massive amounts of high-quality calibration data to construct an accurate "error map" through algorithms, enabling the tester to automatically correct system errors in subsequent real-world measurements.

[0057] Figure 2 The workflow of the entire calibration system when the device of the present invention is put into use is demonstrated.

[0058] A logically clear and self-verifying closed-loop calibration process was constructed. Through initial self-checking, cyclic data acquisition and online verification, and final model verification, this method elevates calibration from a single data acquisition action to an intelligent quality control process, ensuring the reliability of every link from the data source to the final model output, and significantly improving the robustness of the entire calibration system and the credibility of the results.

[0059] The self-test during the calibration initialization and self-test phase includes: the main control unit sequentially switches the multiplexer to several known fixed impedance channels, the test instrument host performs rapid measurements, and if the deviation between the measurement result and the expected value is within the preset tolerance, the device is determined to be in normal condition; otherwise, the calibration is stopped and an error is reported. Furthermore, the calibration mode parameters further include the excitation current level setting. The test instrument host dynamically adapts the applied excitation current magnitude according to the theoretical value range of the selected standard impedance element to ensure that the measurement signal is in the optimal quantization range.

[0060] During implementation, in the self-test phase, the "several known fixed impedance channels" switched by the main control unit are typically the pure resistance channels with the smallest (e.g., 10Ω), middle (e.g., 1kΩ), and largest (e.g., 4.7kΩ) impedance values ​​in the network, because these components have the most stable characteristics. The host measures at 1kHz and 50kHz; if the relative amplitude error is less than 0.5%, the self-test passes. Regarding the dynamic adaptation of the excitation current, the host software has a preset lookup table. For example, when the target standard impedance is less than 100Ω, the excitation current is set to 100μA (to avoid excessive current causing overheating or exceeding the measurement range); when the target impedance is greater than 1kΩ, the excitation current is set to 1mA (to ensure a sufficiently large measurement voltage signal and improve the signal-to-noise ratio).

[0061] The core lies in adaptively optimizing measurement conditions. Self-testing selects a typical channel, enabling the detection of significant device faults with minimal time cost and maximum probability. Dynamically adapting the excitation current is a key strategy to ensure measurements are always performed within the optimal linear range of the test instrument's analog-to-digital converter (ADC). For low impedance, a large current produces an easily measurable voltage drop; for high impedance, a small current avoids signal saturation. This ensures the measurement system obtains a high signal-to-noise ratio signal across the entire impedance range, thereby improving the final calibration accuracy from the source of data acquisition.

[0062] Introducing a deep self-test and dynamic excitation current adaptation mechanism during the initialization phase has the beneficial effect of enabling intelligent prelude and adaptive optimization of the calibration process. The self-test function acts as a competent "sentinel," eliminating the risk of device malfunction in advance; while the dynamic excitation current ensures that measurements of impedances with different ranges can operate within the optimal linear range of the tester, thereby maximizing signal quality in the initial stage of data acquisition and providing a prerequisite for subsequent high-precision modeling.

[0063] The cyclic data acquisition and online verification phase specifically includes:

[0064] a. Channel switching and stabilization: The main control unit controls the multiplexer to switch to the target channel and waits for a configurable stabilization delay td, which is dynamically set according to the type and capacitance value of the target impedance element;

[0065] b. Data acquisition: The main unit of the test instrument measures the amplitude and phase of the current impedance at each frequency specified in the frequency point list;

[0066] c. Online verification: Compare the measurement results of the current channel at key frequency points (such as 1kHz, 50kHz) with the pre-stored upper and lower limits of the theoretical values ​​of the channel. If the measurement results exceed the limits multiple times in a row, the channel data is determined to be invalid and the channel fault flag is recorded.

[0067] The stabilization delay (td) is configurable. For example, when switching to a purely resistive channel, td can be set to 1 millisecond; however, when switching to a channel with a larger capacitance (e.g., 100nF) or a composite network, due to charging and discharging effects, td may need to be extended to 5-10 milliseconds to ensure signal stability. During online verification, key frequency points are typically selected as low frequency (1kHz, representing resistivity) and mid-frequency (50kHz, representing capacitance). The host software stores the theoretical values ​​for each channel at each key frequency point and their allowable deviation range (e.g., ±3%). If the first measurement exceeds the limit, the main control unit will control the host to immediately remeasure at the original frequency point of the original channel (up to 3 consecutive times). If all measurements exceed the limit, the channel is marked as invalid.

[0068] Optimized waiting and real-time data validity gating are implemented for different load characteristics. The configurable stabilization delay (td) takes into account the transient response characteristics of different passive components, avoiding errors caused by measurements performed when the signal is unstable. Online verification acts as a real-time "quality checkpoint," eliminating accidental interference through multiple retests and quickly locating specific impedance paths with potential faults or performance degradation. This ensures that only reliable data flows into subsequent modeling processes, improving the robustness and reliability of the entire calibration process.

[0069] The system allows for configurable stable delays and real-time online verification for different load settings. Its advantages include refined management and immediate quality control of the measurement process. The configurable delays fully consider individual differences, ensuring the absolute stability of the measurement signal; while online verification acts as a real-time "quality checkpoint," instantly detecting and isolating abnormal data to prevent invalid or erroneous data from contaminating the entire dataset, thus ensuring the high purity and reliability of the data used for subsequent modeling.

[0070] The cyclic data acquisition and online verification phase also employs an alternating redundant acquisition strategy: after completing the first round of acquisition of all channels, the second round of acquisition is immediately performed with the opposite channel sequence; for each frequency point of each channel, the average value of the two rounds of acquisition data is taken as the final valid value; if the difference between the two rounds of data is too large, the third acquisition of that point is triggered, and the average value of the two closest data points among the three rounds is taken; in addition, in each frequency sweep measurement, each frequency point is sampled multiple times, and the sampled data is processed in real time using the moving average filtering algorithm in digital signal processing to suppress random noise.

[0071] When implementing the alternating redundant acquisition strategy, the first round proceeds in the order of channels 0, 1, 2, ... 15, and the second round immediately proceeds in the order of channels 15, 14, 13, ... 0. For each data point, if the absolute value of the difference between the two rounds of measurements is less than 1% of the full scale, the arithmetic mean is directly taken. If the difference is too large (e.g., exceeding 1%), a third acquisition is automatically triggered, and then the average of the two closest values ​​among the three data points is taken. Moving average filtering is implemented during the AD sampling phase of each measurement. For example, each frequency point is sampled 64 times consecutively, and then the arithmetic mean of these 64 sampling points is calculated as a valid measurement value.

[0072] Active noise and drift suppression are achieved through temporal redundancy and digital signal processing. Alternating sequence acquisition effectively counteracts the impact of system drift caused by slow increases in internal device temperature or slow changes in environmental factors, as the same channel is measured twice within a short period. Moving average filtering, a classic digital signal processing technique, reduces the amplitude of random white noise to 1 / √N (where N is the number of samples) through multiple sampling averaging, thereby significantly improving the signal-to-noise ratio of a single measurement. The combination of these two techniques enhances the quality and stability of the raw acquired data.

[0073] The adopted alternating redundant acquisition and digital filtering strategy effectively combats system drift and random noise. Alternating acquisition effectively compensates for system errors introduced by the passage of time and slow environmental changes, while digital filtering significantly suppresses random interference. The combination of these two techniques enhances the data acquisition process by providing a "sifting and filtering" capability, resulting in highly stable and consistent raw data, thus laying a solid foundation for constructing accurate error models.

[0074] The error modeling and model verification stage employs a two-dimensional interpolation algorithm based on frequency and impedance amplitude as dual variables. The modeling process assigns weights to the measurement data of different channels, with the weighting factor depending on the accuracy level of the impedance element in that channel and the measurement signal-to-noise ratio calculated during the current acquisition. The modeling algorithm further introduces a temperature compensation term, the coefficient of which is obtained by querying a pre-stored table of temperature-error characteristic curves corresponding to the readings of the internal temperature sensor of the device.

[0075] In the implementation of the two-dimensional interpolation algorithm, the host software organizes the collected data into a two-dimensional lookup table. One dimension is frequency (e.g., dozens of points from 1kHz to 1MHz), and the other dimension is the magnitude of the standard impedance. For any measurement point to be calibrated (frequency f_x, measured impedance magnitude |Z|_x), the error values ​​of the four nearest surrounding points (f1, |Z|1), (f1, |Z|2), (f2, |Z|1), and (f2, |Z|2) are found in the table. Then, bilinear interpolation is performed to calculate the estimated systematic error at point (f_x, |Z|_x). Weighting factor calculation: the initial weight of a high-precision (±0.1%) resistor channel is higher than that of a normal-precision (e.g., ±1%) inductor channel; simultaneously, the signal-to-noise ratio (SNR) is calculated based on the volatility (standard deviation / mean) of the measurement data, with data points having higher SNRs receiving greater weights.

[0076] A high-resolution error map is established and a weighted optimization fit is performed. Two-dimensional interpolation can precisely describe the complex surface of system error variations with frequency and impedance, which is much more accurate than simple single-frequency or single-range calibration models. Introducing weighting factors makes the modeling process more intelligent, allowing the calibration algorithm to "trust" data that is inherently more accurate and better measured in this instance. This results in a final error model that more closely approximates the true error characteristics of the system, significantly improving the accuracy and reliability of the calibration model.

[0077] The weighted two-dimensional interpolation modeling method employed offers the advantage of constructing a high-fidelity, intelligently weighted system error mapping model. Two-dimensional interpolation can precisely characterize the complex relationship between error and frequency and impedance variations, far superior to simple single-point calibration. Furthermore, the introduction of weighting factors allows the model to "trust" more reliable data points, thus achieving an optimized, adaptive fit. The resulting error model more closely resembles reality, resulting in a qualitative leap in calibration accuracy and intelligence.

[0078] The model verification process specifically involves: selecting at least one RC parallel network channel in the standard impedance network that was not involved in the modeling as the verification benchmark; using the newly established error model to compensate for the measurement data of this channel; and calculating the residual error after compensation. If the residual error is less than the maximum error allowed by the system, the model verification is successful; otherwise, the calibration process is deemed a failure, and a prompt is made to check the device or tester. The verification process also includes phase error verification to ensure that the compensated phase response is within the required range.

[0079] The model verification process is as follows: During modeling, one channel of an RC parallel network (e.g., 100Ω / / 10nF) is intentionally excluded from the calculation. After the error model is established, the host control device switches to this verification channel and performs measurements across the entire frequency band. These measurements are then input into the newly established error model for compensation. After compensation, the residual error between the measured value and the theoretical value of the verification channel is calculated at each frequency point. The system presets a maximum permissible error threshold (e.g., 1% amplitude error, 1 degree phase error). If the residual error at all frequency points is below the threshold, the verification is successful. Phase error verification is performed simultaneously to ensure that both the real and imaginary parts of the impedance are effectively corrected.

[0080] The "hold-out method" is used to assess the model's generalization ability. This is a crucial step in ensuring calibration quality. Validating the model using a complex impedance standard with unique characteristics that was not involved in the modeling objectively assesses whether the error model truly possesses good generalization ability, rather than simply "memorizing" the data used for modeling. This effectively prevents overfitting. Only when the model can accurately predict and correct the error of an unknown standard can it be reliably used for measurements of unknown biological tissues. This is an important self-validation and quality control mechanism.

[0081] By introducing an independent model validation process, the core benefit is that it provides an objective evaluation of the generalization ability of the calibration results based on data not involved in the modeling. This approach effectively avoids the risk of model overfitting (i.e., only perfectly correcting training data but unable to handle new data), ensuring that the established error model has real practical value and can be reliably used to correct measurement results for unknown biological samples, greatly enhancing users' confidence in the calibrated instrument measurement results.

[0082] If model verification fails, the system automatically switches to degraded calibration mode: ignoring previously marked invalid channels and frequencies with excessive errors, and using only verified high-reliability channels and data, a simplified error model is reconstructed that is applicable to certain frequency bands and ranges, with a narrower range but guaranteed accuracy. In degraded mode, the system evaluates the coverage of the remaining valid channels and generates a detailed degraded calibration report, indicating the precise frequency boundaries and impedance ranges applicable to the current model.

[0083] When model validation fails, i.e., the residual error exceeds the threshold, the system automatically triggers a degradation calibration mode, which is implemented according to the following logic: The host software first reads the list of invalid channels and out-of-tolerance frequency points recorded during the "cyclic data acquisition and online validation phase." Then, the system constructs a "whitelist" containing only those channels that were marked as valid during the data acquisition phase and performed well in model validation. For example, suppose that of the original 15 channels, channel 3 (a capacitor) and channel 12 (an RL network) are excluded due to invalid data or excessively large errors detected during validation. The system then remodels the error using data from the remaining 13 channels. The modeling algorithm adaptively adjusts its effective frequency and impedance range. During implementation, the system analyzes the impedance distribution and frequency response covered by these 13 channels, calculating the impedance modulus range (e.g., reduced from 10Ω-10kΩ to 50Ω-5kΩ) and frequency boundaries (e.g., reduced from 1kHz-1MHz to 10kHz-800kHz) that still make the model reliable after degradation.

[0084] This system ensures limited reliability of core functions even when some hardware benchmarks fail or degrade. It's an intelligent fault-tolerance mechanism. It recognizes that instead of forcing an unreliable full-range model containing erroneous data, it's better to establish a simplified model that is absolutely reliable within a limited but rigorously validated range. The system automatically determines the boundaries of the simplified model by analyzing the remaining effective channels, ensuring accuracy is maintained when performing biometric measurements within these boundaries. This prevents the entire calibration system from being completely paralyzed due to the failure of a single standard component, greatly improving the availability and robustness of the equipment. The generated degraded calibration report clearly indicates the applicable domain of the current model, a responsible safety measure that alerts users that results may not be adequately calibrated when measuring particularly large or small impedances, or at extremely high / low frequencies.

[0085] This endows the calibration system with strong fault tolerance and graceful degradation characteristics. When some references fail, the system is not completely paralyzed, but can intelligently utilize the remaining healthy resources to build a simplified model that is still reliable within a limited range. This ensures the basic availability and measurement accuracy of the equipment under non-ideal conditions, greatly improving the system's resilience and practicality in the face of component aging or accidental failures.

[0086] The method also includes a calibration traceability and data archiving stage: After a complete calibration is completed, the system automatically generates a structured calibration report, which includes, but is not limited to: calibration timestamp, channel sequence used, invalid channel list, final model parameters, model validation results, and logs of all original measurement data and compensated data; the report is encrypted and stored in the non-volatile memory of the test instrument host for subsequent quality traceability and analysis; furthermore, the system analyzes historical calibration report data from multiple consecutive times, uses trend prediction algorithms (such as linear regression) to predict the performance degradation of key components, and proactively issues an early warning when the predicted value approaches the tolerance threshold.

[0087] The calibration traceability and data archiving phases are executed automatically at the end of each calibration process. Specifically, the software on the test instrument host generates a structured XML or JSON file as a calibration report. This report includes: precise calibration start and end timestamps (UTC time), the sequence of all channel IDs actually used in this calibration, a list of channels marked as invalid and their reasons for failure, key parameters of the finally adopted error model (such as interpolation tables or polynomial coefficients), and the final result of model validation (pass / fail, and the residual error value at the time of failure). All raw measurement data (amplitude, phase, frequency points) and model-compensated data are packaged together as attachments in binary or CSV format. This data package is encrypted using the AES-128 algorithm and then stored as a complete, tamper-proof record block in a specific partition of the host's embedded eMMC storage chip. In addition, the system maintains an independent calibration history database, recording the core indicators of each calibration (such as the average deviation between the measured and theoretical values ​​of key channels).

[0088] This system enables full lifecycle quality tracking and predictive maintenance. Detailed calibration reports and encrypted data archiving provide a complete "electronic record" for every calibration of each device, meeting the stringent regulatory requirements for quality traceability in fields such as medical devices. If subsequent measurement results are questioned, the calibration status at the time can be traced back for review. A deeper working principle lies in using big data for trend prediction. The system periodically (e.g., monthly) runs a background analysis task that performs linear regression analysis on long-term measurements of the same channel and frequency point in the historical database. For example, it might find that the measurement value of a 1kΩ resistor channel at 100kHz has been slowly increasing at a rate of 0.01% per month over the past year. By extrapolating this trend, the system can predict that within the next three months, the drift of the component may cause the error to exceed the tolerance, thus proactively issuing a warning to the user, indicating that preventative maintenance or component replacement may be necessary. This represents a leap from passive calibration to proactive predictive maintenance, significantly improving the reliability and lifespan of the equipment.

[0089] Figure 2 The complete calibration system workflow, which includes all these stages, is shown, with the final stage corresponding to the stages described above.

[0090] The traceability and archiving mechanism has profound benefits in enabling full lifecycle traceability and predictive maintenance capabilities for calibration data. Complete electronic archives meet stringent quality control and regulatory requirements; furthermore, trend analysis based on historical data can proactively warn of component performance degradation, shifting maintenance strategies from "remedial measures" to "predictive measures," achieving intelligent health management of equipment, effectively extending equipment lifespan, and preventing measurement deviations.

[0091] The above-described embodiments are detailed and specific, illustrating preferred embodiments of the present invention. They are only used to illustrate the technical ideas and features of the present invention, with the aim of enabling those skilled in the art to understand the content of the present invention and implement it accordingly. However, they are not limited to the present invention, and the patent scope of the present invention cannot be limited by this embodiment alone. That is, any equivalent changes or modifications made to the spirit disclosed in the present invention, without departing from the structure of the present invention, such as local improvements within the system and modifications or transformations between subsystems, are still within the patent scope of the present invention.

Claims

1. A calibration device for a bioelectrical impedance measurement instrument, characterized in that The device is connected to the main unit of the bioelectrical impedance analyzer via a communication interface and a calibration port. The device includes: The main control unit is used to communicate with the host of the test instrument and parse instructions from the host. A multiplexer, the control terminal of which is connected to the main control unit, and the output terminal of which is connected to the calibration port; The standard impedance network consists of multiple high-precision passive components, including resistors, capacitors, inductors and their series and parallel combinations, with each impedance component connected to a different input channel of a high-speed multiplexer. A communication interface, connected to the main control unit, is used to realize data interaction with the test instrument host; The calibration method is performed by the test instrument host and includes the following stages performed sequentially: Calibration initialization and self-test phase: The test instrument host sends a start command through the communication interface, the calibration device powers on and performs a self-test, and reports status information; the start command includes the calibration mode, frequency point list and channel sequence. Cyclic data acquisition and online verification stage: The main control unit controls the multiplexer to switch channels cyclically according to the channel sequence, and the test instrument host performs frequency sweep measurement on each channel, while judging the validity of the acquired data online; Error modeling and model validation phase: An error correction model is established using all valid data, and the accuracy of the model is validated using a set of standard impedance data for validation. The cyclic data acquisition and online verification phase specifically includes: a. Channel switching and stabilization: The main control unit controls the multiplexer to switch to the target channel and waits for a configurable stabilization delay td, which is dynamically set according to the type and capacitance value of the target impedance element; b. Data acquisition: The main unit of the test instrument measures the amplitude and phase of the current impedance at each frequency specified in the frequency point list; c. Online verification: Compare the measurement results of the current channel at key frequency points with the pre-stored upper and lower limits of the theoretical values ​​for the channel. If the measurement results exceed the limits multiple times in a row, the channel data is deemed invalid and a channel fault flag is recorded. The cyclic data acquisition also employs an alternating redundant acquisition strategy: after completing the first round of acquisition of all channels, the second round of acquisition is immediately performed with the opposite channel sequence; in each frequency sweep measurement, each frequency point is sampled multiple times, and the sampled data is processed in real time using the moving average filtering algorithm in digital signal processing to suppress random noise; Error modeling employs a two-dimensional interpolation algorithm based on two variables: frequency and impedance amplitude. The modeling process assigns weights to the measurement data of different channels, with the weighting factor depending on the accuracy level of the impedance element in that channel and the measurement signal-to-noise ratio calculated during the current acquisition. The modeling algorithm further introduces a temperature compensation term, the coefficient of which is obtained by querying a pre-stored table of temperature-error characteristic curves corresponding to the readings of the internal temperature sensor of the device. In the implementation of the two-dimensional interpolation algorithm, the host software organizes the collected data into a two-dimensional lookup table. One dimension is frequency, with dozens of points ranging from 1kHz to 1MHz. The other dimension is the magnitude of the standard impedance. For any measurement point to be calibrated, the frequency of the measurement point is f_x, and the measured impedance magnitude is |Z|_x. The error values ​​of the four nearest points around it are found in the table, with the coordinates of the four points being (f1, |Z|1), (f1, |Z|2), (f2, |Z|1), and (f2, |Z|2). Then, bilinear interpolation is performed to calculate the estimated system error at point (f_x, |Z|_x). The weighting factor is calculated as follows: the initial weight of a high-precision ±0.1% resistance channel is higher than that of a normal-precision ±1% inductor channel. At the same time, the signal-to-noise ratio is calculated based on the fluctuation of the measurement data, and data points with higher signal-to-noise ratios have greater weights.

2. The calibration device for bioelectrical impedance measurement apparatus according to claim 1, wherein The main control unit is a microcontroller; the high-speed multiplexer is an analog switch with a bandwidth higher than 200MHz; the resistive elements in the standard impedance network have an accuracy of not less than ±0.1% and a temperature coefficient of less than ±25ppm / °C, and the capacitor elements are NPO / COG ceramic capacitors or mica capacitors with an accuracy of not less than ±0.1%; the communication interface is an SPI, I2C, or USB interface; the device is encapsulated in a shielded housing, and its internal printed circuit board adopts a design of at least four layers and has a complete ground plane layer; the calibration port is a coaxial interface; and the standard impedance network includes at least one RC parallel network and at least one CL series network.

3. The calibration device according to claim 1, characterized in that, The self-test in the calibration initialization and self-test phase includes: the main control unit sequentially switches the multiplexer to a known fixed impedance channel, the test instrument host performs rapid measurement, and if the deviation between the measurement result and the expected value is within the preset tolerance, the device is determined to be in normal condition; otherwise, the calibration is stopped and an error is reported. Furthermore, the calibration mode parameters further include the excitation current level setting. The test instrument host dynamically adapts the applied excitation current magnitude according to the theoretical value range of the selected standard impedance element to ensure that the measurement signal is in the optimal quantization range.

4. The calibration device according to claim 1, characterized in that, The model verification process specifically involves: selecting at least one RC parallel network channel in the standard impedance network that was not involved in the modeling as the verification benchmark; using the newly established error model to compensate for the measurement data of this channel; and calculating the residual error after compensation. If the residual error is less than the maximum error allowed by the system, the model verification is successful; otherwise, the calibration process is deemed a failure, and a prompt is made to check the device or tester. The verification process also includes phase error verification to ensure that the compensated phase response is within the required range.

5. The calibration device according to claim 4, characterized in that, If model verification fails, the system automatically switches to degraded calibration mode: ignoring previously marked invalid channels and frequencies with excessive errors, and using only verified high-reliability channels and data, a simplified error model is reconstructed that is applicable to certain frequency bands and ranges, with a narrower range but guaranteed accuracy. In degraded mode, the system evaluates the coverage of the remaining valid channels and generates a detailed degraded calibration report, indicating the precise frequency boundaries and impedance ranges applicable to the current model.

6. The calibration apparatus according to claim 1, characterized in that, The calibration traceability and data archiving phases are included: After a complete calibration is completed, the system automatically generates a structured calibration report, which includes: calibration timestamp, channel sequence used, invalid channel list, final model parameters, model validation results, and logs of all original measurement data and compensated data; this report is encrypted and stored in the non-volatile memory of the test instrument host for subsequent quality traceability and analysis; by analyzing historical calibration report data from multiple consecutive tests, the system uses a trend prediction algorithm to predict the performance degradation of key components and proactively issues an early warning when the predicted value reaches the tolerance threshold.

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