Bio-current self-powering method for human body worn devices

Abstract

This invention provides a biocurrent self-powered method for wearable devices. The method involves collecting biocurrents generated on the human body surface through a flexible bioelectrode array disposed on the inner surface of the wearable device's strap; measuring the electrode-skin interface impedance at each frequency by injecting at least two test signals of different frequencies into the electrode pair to obtain a complex impedance spectrum; based on the complex impedance spectrum, fitting the electrical characteristics of the electrode-skin interface using an equivalent circuit model, and extracting model parameters, which include at least interface resistance, interface capacitance, and polarization resistance; calculating the optimal matching network parameters based on the extracted model parameters, and dynamically adjusting the input impedance of the acquisition circuit to achieve a conjugate match between the input impedance of the acquisition circuit and the output impedance of the electrode-skin interface; amplifying and rectifying the biocurrents collected after impedance matching to convert them into direct current (DC) energy; storing the DC energy in an energy storage system and powering the functional modules of the wearable device.

Description

Technical Field

[0001] This invention relates to the field of smart wearable device technology, and in particular to a bio-current self-powered method for wearable devices. Background Technology

[0002] With the rapid development of the Internet of Things (IoT) and mobile internet technologies, wearable devices such as smartwatches, health bracelets, and medical patches have become important tools for people's daily lives, health management, and exercise monitoring. These devices typically integrate multiple sensors, displays, wireless communication modules, and high-performance processors, offering increasingly rich functionality. However, this increased functionality has directly led to a sharp rise in device power consumption, and power supply issues have become a core bottleneck restricting their further popularization and development.

[0003] Currently, the vast majority of wearable devices use built-in lithium-ion batteries as their sole or primary power source. This power supply method has the following inherent drawbacks: First, lithium-ion batteries have limited energy density. Due to the compact physical space of wearable devices, users typically need to charge the device every few days, which significantly impacts the convenience of the user experience. Second, lithium batteries have a cycle life issue. As the number of charge-discharge cycles increases, the battery capacity gradually decreases. Typically, after several hundred cycles, the battery life will significantly decline, leading to a shortened overall lifespan of the device. Frequent battery replacements not only increase user costs but also create electronic waste, which is inconsistent with green and environmentally friendly principles.

[0004] To reduce reliance on external charging, the industry has proposed various self-powered technologies. Among them, the technology of generating electricity using the body's own bioelectric currents has attracted widespread attention because it is not limited by light conditions and does not rely on large-scale limb movements. The human body generates weak bioelectric signals during physiological activities: the electrocardiogram (ECG) signal on the skin surface has an amplitude of approximately 1-5mV and a frequency range of 0.05-100Hz; the electromyography (EMG) signal has an amplitude of approximately 0.01-0.1mV and a frequency range of 10-500Hz. These bioelectric signals contain considerable energy potential. It is estimated that the bioelectric energy generated by the human body during daily activities can reach the milliwatt level, theoretically sufficient to support the operation of low-power wearable devices.

[0005] However, efficiently harvesting bioelectric current and converting it into usable electrical energy faces numerous technical challenges. Among them, impedance mismatch at the electrode-skin interface is one of the core bottlenecks restricting energy harvesting efficiency.

[0006] Specifically, biocurrent harvesting typically employs flexible electrodes that adhere to the skin. The contact interface between the electrode and the skin exhibits complex electrical characteristics, including contact resistance, double-layer capacitance, and polarization resistance. These interfacial impedance parameters are not fixed but are dynamically influenced by various factors: when the body moves, the relative displacement between the electrode and the skin causes changes in contact pressure, resulting in drastic fluctuations in contact resistance (ranging from a few kΩ to hundreds of kΩ); when the body sweats, the electrolytes in the sweat alter the conductivity of the skin surface, leading to significant changes in interfacial capacitance and polarization resistance; furthermore, prolonged wear causes a gradual decrease in interfacial impedance due to hydration of the stratum corneum. Studies have shown that under dynamic usage scenarios, the variation in electrode-skin interfacial impedance can reach an order of magnitude or more.

[0007] Current biocurrent harvesting schemes generally employ a fixed input impedance design. The input impedance at the front end of the harvesting circuit is set to a preset fixed value (typically hundreds of kΩ to several MΩ) to accommodate the general skin contact of the electrodes. This fixed impedance design is acceptable under static, dry conditions, but it has serious drawbacks in dynamic scenarios: when the interface impedance changes due to movement or sweating, a mismatch occurs between the fixed input impedance and the changing interface output impedance, leading to signal attenuation and a sharp decline in energy transfer efficiency. According to the maximum power transfer theorem, when the source impedance and load impedance are not equal, the power obtained by the load is lower than the maximum value. Experimental data shows that in scenarios of intense movement, the energy harvesting efficiency drop due to impedance mismatch can reach more than 50%, and in severe cases, effective energy may not be harvested at all.

[0008] To address the aforementioned issues, some improvements have been attempted in existing technologies. For example, some solutions employ high input impedance designs (e.g., above 10MΩ) to try and reduce the impact of mismatch by increasing the load impedance. However, excessively high input impedance introduces greater thermal noise, reducing the signal-to-noise ratio, and the amplifier circuit is more susceptible to external electromagnetic interference, affecting signal quality. Another solution uses multi-electrode switching to improve acquisition performance by selecting the electrode pair with the optimal signal quality. However, this solution can only switch between a limited number of discrete impedance values, failing to achieve continuous and precise impedance matching, and signal interruption occurs during switching. Still other solutions attempt to stabilize electrode-skin contact through physical methods (e.g., increasing strap pressure), but this reduces wearing comfort and fails to address changes in electrochemical characteristics caused by sweating.

[0009] In summary, existing technologies cannot effectively address the decline in energy harvesting efficiency caused by changes in electrode-skin interface impedance in dynamic scenarios. Therefore, providing a biocurrent self-powered method that can track interface impedance changes in real time and dynamically adjust the input impedance of the harvesting circuit to achieve optimal matching is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0010] The purpose of this invention is to provide a biocurrent self-powered method for wearable devices, which solves the technical problem of low energy harvesting efficiency caused by changes in electrode-skin contact impedance in existing biocurrent self-powered technologies. This invention provides a bio-current self-powered method for wearable human devices, comprising the following steps: Data collection steps: Biocurrents generated on the human body surface are collected by a flexible bioelectrode array placed on the inner surface of the wearable device's strap. Impedance detection steps: When collecting biocurrents on the human body surface through a flexible bioelectrode array, inject at least two test signals of different frequencies into the electrode pair, measure the electrode-skin interface impedance at each frequency, and obtain the complex impedance spectrum. Impedance modeling steps: Based on the complex impedance spectrum, the electrical characteristics of the electrode-skin interface are fitted using an equivalent circuit model, and model parameters are extracted. The model parameters include at least interface resistance, interface capacitance, and polarization resistance. Matching adjustment steps: Based on the extracted model parameters, calculate the optimal matching network parameters and dynamically adjust the input impedance of the acquisition circuit to make the input impedance of the acquisition circuit and the output impedance of the electrode-skin interface form a conjugate match. Energy conversion steps: The biocurrent collected after impedance matching is amplified and rectified to convert it into direct current energy; Storage power supply step: The DC power is stored in the energy storage system and used to power the functional modules of the wearable device.

[0011] Furthermore, the test signal is at least two sinusoidal signals with frequencies in the range of 100Hz to 100kHz, and the measurement of the electrode-skin interface impedance at each frequency includes: measuring the voltage response and current response at both ends of the electrode after the test signal is injected, and calculating the impedance amplitude and phase angle at each frequency.

[0012] Furthermore, the equivalent circuit model is the Randles equivalent circuit or a variant thereof, and the equivalent circuit model includes: interface resistance. Interface capacitance Polarization resistance and Weber impedance The model parameters are extracted using nonlinear least squares curve fitting, and the objective function is:

[0013] in For frequency The complex impedance measured below, For the equivalent circuit model in the parameter set The theoretical complex impedance is as follows.

[0014] Furthermore, the matching adjustment step includes: The output impedance of the electrode-skin interface is calculated based on the extracted model parameters. ; Set the adjustable input impedance of the acquisition circuit The acquisition circuit includes an adjustable resistor array and an adjustable capacitor array; Calculate the optimal matching parameters so that ,in for The conjugate of complex numbers; By adjusting the resistance value of the adjustable resistor array and the capacitance value of the adjustable capacitor array using control signals, the input impedance of the acquisition circuit can be made to approach the optimal matching parameters.

[0015] Furthermore, the calculation of the optimal matching parameters includes: The output impedance of the electrode-skin interface is modeled as ,in For interface resistance, Polarization resistor, Interface capacitance, Angular frequency; Set the input impedance of the acquisition circuit to be ,in For adjustable input resistance, It is an adjustable input capacitor; Solve the conjugate matching condition , ; The control parameters for the adjustable resistor array and the adjustable capacitor array are set based on the solution results.

[0016] Furthermore, it also includes a dynamic tracking step: repeatedly executing the impedance detection step, impedance modeling step, and matching adjustment step at a preset cycle to track the impedance changes at the electrode-skin interface in real time and dynamically update the matching network parameters.

[0017] Furthermore, in the dynamic tracking step, when the rate of change of model parameters obtained from two adjacent measurements exceeds a preset threshold, the measurement cycle is shortened; when the rate of change of model parameters is lower than the preset threshold, the measurement cycle is extended to reduce power consumption.

[0018] Furthermore, the energy conversion step includes: An ultra-low noise amplifier circuit is used to amplify the biocurrent signal after impedance matching to a millivolt-level voltage signal. A rectifier circuit is used to convert the amplified AC or pulsating signal into DC power. The gain of the ultra-low noise amplifier circuit is dynamically adjusted according to the signal strength after impedance matching.

[0019] Furthermore, it also includes a monitoring multiplexing step: multiplexing the biocurrent signal collected in the impedance detection step, separating the electrocardiogram signal and electromyogram signal from it, and generating the user's physiological parameter information.

[0020] Furthermore, the physiological parameter information is used to adjust the frequency range of impedance matching. When electromyographic signals are detected to be dominant, the optimized frequency of impedance matching is shifted to the higher frequency band.

[0021] The technical solution of this invention effectively improves the biocurrent transmission efficiency compared to traditional fixed impedance schemes by real-time detection of electrode-skin interface impedance and performing conjugate matching. Furthermore, through dynamic tracking steps and adaptive measurement cycles, it can respond in real-time to interface impedance changes caused by exercise, sweating, etc., maintaining high energy acquisition efficiency even under intense exercise scenarios, thus solving the problem of acquisition failure due to unstable contact in existing technologies. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the overall process of the present invention; Figure 2 This is a detailed flowchart illustrating the impedance detection and matching adjustment process of the present invention. Detailed Implementation

[0024] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0026] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0027] Example 1 This invention provides a bio-current self-powered method for wearable human devices, comprising the following steps: S1 Acquisition Steps: Biocurrents generated on the human body surface are collected by a flexible bioelectrode array set on the inner surface of the wearable device's strap; the flexible bioelectrode array uses multilayer graphene / hydrogel composite material electrodes, and multiple independent electrode plates are set on the inner side of the strap.

[0028] S2 Impedance Detection Steps: When collecting biocurrents on the human body surface through a flexible bioelectrode array, inject at least two test signals of different frequencies into the electrode pair, measure the electrode-skin interface impedance at each frequency, and obtain the complex impedance spectrum.

[0029] In S2, the test signal consists of at least two sinusoidal signals with frequencies ranging from 100Hz to 100kHz. Measuring the electrode-skin interface impedance at each frequency includes measuring the voltage and current responses across the electrodes after the test signal is injected, and calculating the impedance amplitude and phase angle at each frequency. The test signal is generated by a digital-to-analog converter (DAC) of the microcontroller and injected into the electrode pair via a coupling capacitor. The test signal is a sinusoidal signal with frequencies of at least two points: 100Hz, 500Hz, 1kHz, 5kHz, 10kHz, 50kHz, and 100kHz. The amplitude is controlled below 10mV to avoid irritating the skin and interfering with bioelectrical signals. After signal injection, the voltage response across the electrodes and the current response flowing through the electrodes are acquired using an analog-to-digital converter (ADC). For each frequency point, at least 10 complete cycles of data are acquired, and the impedance amplitude and phase angle at that frequency are calculated using FFT analysis to form a complex impedance spectrum. .

[0030] This step selects a frequency range of 100Hz to 100kHz, covering the region above the main frequency band of bioelectrical signals (0.05-500Hz), thus avoiding mutual interference between the test signal and the bioelectrical signal. Simultaneously, using multi-frequency measurements allows for the acquisition of more complete impedance spectrum information, improving the model fitting accuracy.

[0031] S3 impedance modeling steps: Based on the complex impedance spectrum, the electrical characteristics of the electrode-skin interface are fitted using an equivalent circuit model, and the model parameters are extracted. The model parameters include at least the interface resistance, interface capacitance, and polarization resistance.

[0032] In S3, the equivalent circuit model is the Randle equivalent circuit or its variants. The Randle equivalent circuit is a classic model describing the electrical characteristics of the electrode-electrolyte interface. Equivalent circuit models include: Interface resistance : Represents the resistance of the stratum corneum of the skin and the resistance of electrode contacts, with a typical value of 10kΩ-100kΩ; Interface capacitors : Represents the electric double-layer capacitance at the electrode-skin interface, typically 0.01μF-1μF; polarization resistor : Represents charge transfer resistance, typically 100kΩ-1MΩ; Weber impedance : Represents the impedance caused by ion diffusion, which can be ignored in the high-frequency range.

[0033] The model parameters are extracted using nonlinear least squares curve fitting, which can accurately extract these physiologically relevant parameters from the measurement data, providing a basis for subsequent matching.

[0034] The objective function of the model is:

[0035] in For frequency The complex impedance measured below, For the equivalent circuit model in the parameter set The theoretical complex impedance is given below, and:

[0036] These are the parameters to be fitted. The fitting algorithm uses the Levenberg-Marquardt algorithm, and the initial values ​​are set empirically (e.g., ...). =50kΩ, =200kΩ, (0.1μF), iterate until convergence.

[0037] After fitting, extract , , Three key parameters. These parameters are used not only for subsequent matching calculations but also for monitoring skin condition (e.g., during sweating). Enlarge (Decrease).

[0038] S4 Matching Adjustment Step: Based on the extracted model parameters, calculate the optimal matching network parameters and dynamically adjust the input impedance of the acquisition circuit to achieve a conjugate match between the input impedance of the acquisition circuit and the output impedance of the electrode-skin interface, thereby maximizing the transmission efficiency of the biocurrent. S4 specifically includes: S4.1, based on the extracted model parameters , , Calculate the output impedance of the electrode-skin interface at the dominant frequency of bioelectrical signals (e.g., 50Hz for ECG, 200Hz for EMG). ;

[0039] in For interface resistance, Polarization resistor, Interface capacitance, Angular frequency; S4.2, Set the adjustable input impedance of the acquisition circuit The acquisition circuit has an adjustable matching network at its front end, including an adjustable resistor array and an adjustable capacitor array. The adjustable resistor array consists of multiple resistors connected in series and analog switches, with a configurable resistance range of 1kΩ-1MΩ in 1kΩ steps. The adjustable capacitor array consists of multiple capacitors connected in parallel and analog switches, with a configurable capacitance range of 10pF-1μF in 10pF steps. The input impedance of the acquisition circuit is set as follows:

[0040] in For adjustable input resistance, It is an adjustable input capacitor; S4.3 Solve for the conjugate matching condition. Based on the solution, set the control parameters for the adjustable resistor array and the adjustable capacitor array. The formula derivation process is as follows: The output admittance of the electrode-skin interface is:

[0041] Calculate the optimal matching parameters so that ,in for The conjugate complex number of , according to the maximum power transfer theorem, when At this time, the power of the biocurrent transmitted from the electrodes to the acquisition circuit is at its maximum. To ensure conjugate matching between the input impedance of the acquisition circuit and the output impedance of the interface, the following must be satisfied:

[0042] Therefore, the solution is:

[0043]

[0044]

[0045] Based on the above formulas and the solution results, the control parameters for the adjustable resistor array and the adjustable capacitor array are set. This can be done based on the extracted... , , Parameters, calculate the optimal match and value.

[0046] S4.4, based on the calculated and The control tables for the adjustable resistor array and adjustable capacitor array are consulted to determine the on / off state of the analog switch, making the actual resistance and capacitance values ​​as close as possible to the target values. After adjustment, the input impedance of the acquisition circuit and the output impedance of the interface achieve conjugate matching. By adjusting the resistance value of the adjustable resistor array and the capacitance value of the adjustable capacitor array through control signals, the input impedance of the acquisition circuit approaches the optimal matching parameters.

[0047] This step is based on the maximum power transfer theorem: the load achieves maximum power transfer when the source impedance and load impedance are conjugate complex numbers. Since bioelectrical signals have low frequencies (primarily energy below 500Hz), transmission line effects can be neglected, and a lumped-parameter circuit model is used for matching design. The adjustable resistor array and adjustable capacitor array are implemented using digital potentiometers and variable capacitor arrays, allowing for rapid adjustment under microcontroller control.

[0048] S5, Dynamic Tracking Step: Repeatedly execute the impedance detection step, impedance modeling step, and matching adjustment step at a preset cycle to track the impedance changes at the electrode-skin interface in real time and dynamically update the matching network parameters.

[0049] The impedance at the electrode-skin interface changes over time due to factors such as exercise, sweating, and prolonged wear. Dynamic tracking ensures the matching network adapts to the current interface state. The preset period can be set according to the application scenario; for example, measuring every 30 seconds during normal wear and every 5 seconds during strenuous exercise.

[0050] In the dynamic tracking step, when the rate of change of model parameters obtained from two consecutive measurements exceeds a preset threshold, the measurement cycle is shortened; when the rate of change of model parameters is lower than the preset threshold, the measurement cycle is extended to reduce power consumption.

[0051] Steps S2 to S4 are repeated at a preset cycle to track impedance changes at the electrode-skin interface in real time and dynamically update the matching network parameters. The preset cycle for this step during normal wear is 30 seconds. The system calculates the rate of change of model parameters between two consecutive measurements:

[0052] when When the percentage is >10%, the interface state is judged to be changing rapidly, and the measurement cycle is shortened to 5 seconds; when If the error is less than 2% and this is confirmed after three consecutive measurements, the interface is deemed stable, and the measurement cycle is extended to 60 seconds. By adaptively adjusting the measurement cycle, the power consumption of the test signal is reduced while maintaining tracking accuracy.

[0053] This step enables adaptive adjustment of the measurement frequency. When the interface state changes rapidly (such as during exercise and sweating), the measurement frequency is increased to track quickly; when the interface is stable, the measurement frequency is decreased to reduce the power consumption of the test signal. The test signal injection itself consumes a small amount of energy (approximately microwatts), and adaptive adjustment can further optimize system energy efficiency.

[0054] S6 energy conversion steps: The biocurrent collected after impedance matching is amplified and rectified to convert it into DC power. In S6, an ultra-low noise amplifier circuit is used to amplify the impedance-matched biocurrent signal to a millivolt-level voltage signal. This ultra-low noise amplifier circuit employs an instrumentation amplifier structure (such as the AD8421), with an input noise voltage density below 10 nV / √Hz. The gain of the amplifier circuit is dynamically adjusted based on the signal strength after matching in S4: when the signal strength is below 0.1 mV, the gain is set to 1000 times; when the signal strength is between 0.1 and 1 mV, the gain is set to 100 times; and when the signal strength is above 1 mV, the gain is set to 10 times. This dynamic gain adjustment ensures the signal remains within the full-scale range of the ADC while avoiding saturation.

[0055] A rectifier circuit composed of zero-threshold MOSFETs converts amplified AC or pulsating signals into DC power. The gate threshold voltage of the zero-threshold MOSFETs is close to 0V, allowing them to conduct under low voltage input, and the forward voltage drop can be controlled below 0.1V, significantly reducing rectification losses. The gain of the ultra-low noise amplifier circuit is dynamically adjusted based on the signal strength after impedance matching.

[0056] The rectified DC power is output to the subsequent energy storage circuit.

[0057] This step combines impedance matching with signal amplification. Impedance matching maximizes signal transmission, while dynamic gain adjustment adaptively adjusts the amplification factor based on the matched signal strength, preventing signal saturation or insufficient signal-to-noise ratio.

[0058] S7 Storage Power Supply Steps: Store DC power in the energy storage system and power the functional modules of the wearable device.

[0059] In the S7, the energy storage system includes a supercapacitor and a rechargeable lithium battery. Converted electrical energy is preferentially stored in the supercapacitor. When the supercapacitor voltage reaches 2.5V, it is transferred to the rechargeable lithium battery via a DC-DC converter. The system supplies power to each functional module based on the available energy, prioritizing the operation of the real-time clock and basic sensors.

[0060] S8 Monitoring Multiplexing Step: The biocurrent signal acquired in the impedance detection step is multiplexed to separate the electrocardiogram (ECG) and electromyography (EMG) signals, generating the user's physiological parameter information. This physiological parameter information is used to adjust the frequency range of impedance matching. When EMG signals are detected as dominant, the optimized frequency of impedance matching is shifted to a higher frequency band.

[0061] In S8, when electromyography (EMG) signals (main frequency 100-500Hz) are detected as dominant, it indicates that the user is exercising. At this time, the optimized frequency of impedance matching is shifted from the ECG frequency band (about 10-50Hz) to the higher frequency band, making the matching more suitable for the energy acquisition of EMG signals, while also taking into account the needs of health monitoring.

[0062] This invention acquires the complete electrical characteristics of the electrode-skin interface through multi-frequency impedance detection, extracts key parameters through equivalent circuit modeling, and dynamically adjusts the input impedance of the acquisition circuit using the conjugate matching principle. This solves the problem of decreased energy transmission efficiency caused by changes in electrode-skin contact impedance due to factors such as movement and sweating. Compared with traditional fixed impedance acquisition schemes, this method can effectively improve the efficiency of biocurrent transmission.

[0063] Taking a smartwatch as an example, this invention provides an application example: After a user wears a smartwatch using this method, the system automatically executes the following process: Initially, the system measures electrode-skin impedance at four frequency points: 100Hz, 1kHz, 10kHz, and 100kHz, and then fits and extracts the impedance. =50kΩ =200kΩ =0.1μF.

[0064] Calculate the matching parameters at 50Hz using the formula: =50kΩ+200kΩ / (1+(2π×50×200k×0.1μ)²)≈50kΩ+200kΩ=250kΩ, ≈(1+(2π×50×200k×0.1μ)²) / ((2π×50)²×200k²×0.1μ)≈0.2μF.

[0065] The system adjusts the matching network to =250kΩ =0.2μF.

[0066] The user starts running, and sweating causes a change in interface impedance. The system detects this after 5 seconds. Reduced to 10kΩ When the temperature is increased to 0.5μF, the matching parameters are automatically recalculated and the matching network is adjusted.

[0067] During the run, the system detected that electromyographic signals were dominant and shifted the matching frequency to 200Hz to improve the efficiency of electromyographic energy acquisition.

[0068] Throughout the process, the system consistently collects biocurrents under optimal matching conditions, achieving a significantly higher energy conversion efficiency than the fixed impedance scheme and realizing stable self-powered operation.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A bio-current self-powered method for wearable human devices, characterized in that, Includes the following steps: Data collection steps: Biocurrents generated on the human body surface are collected by a flexible bioelectrode array placed on the inner surface of the wearable device's strap. Impedance detection steps: When collecting biocurrents on the human body surface through a flexible bioelectrode array, inject at least two test signals of different frequencies into the electrode pair, measure the electrode-skin interface impedance at each frequency, and obtain the complex impedance spectrum. Impedance modeling steps: Based on the complex impedance spectrum, the electrical characteristics of the electrode-skin interface are fitted using an equivalent circuit model, and model parameters are extracted. The model parameters include at least interface resistance, interface capacitance, and polarization resistance. Matching adjustment steps: Based on the extracted model parameters, calculate the optimal matching network parameters and dynamically adjust the input impedance of the acquisition circuit to make the input impedance of the acquisition circuit and the output impedance of the electrode-skin interface form a conjugate match. Energy conversion steps: The biocurrent collected after impedance matching is amplified and rectified to convert it into direct current energy; Storage power supply step: The DC power is stored in the energy storage system and used to power the functional modules of the wearable device.

2. The bio-current self-powered method for wearable devices according to claim 1, characterized in that, The test signal is at least two sinusoidal signals with frequencies in the range of 100Hz to 100kHz. The measurement of the electrode-skin interface impedance at each frequency includes: measuring the voltage and current responses at both ends of the electrode after the test signal is injected, and calculating the impedance amplitude and phase angle at each frequency.

3. The bio-current self-powered method for wearable devices according to claim 1, characterized in that, The equivalent circuit model is the Randles equivalent circuit or a variant thereof, and the equivalent circuit model includes: interface resistance. Interface capacitance Polarization resistance and Weber impedance The model parameters are extracted using nonlinear least squares curve fitting, and the objective function is: in For frequency The complex impedance measured below, For the equivalent circuit model in the parameter set The theoretical complex impedance is as follows.

4. The bio-current self-powered method for wearable devices according to claim 1, characterized in that, The matching adjustment step includes: The output impedance of the electrode-skin interface is calculated based on the extracted model parameters. ; Set the adjustable input impedance of the acquisition circuit The acquisition circuit includes an adjustable resistor array and an adjustable capacitor array; Calculate the optimal matching parameters so that ,in for The conjugate of complex numbers; By adjusting the resistance value of the adjustable resistor array and the capacitance value of the adjustable capacitor array using control signals, the input impedance of the acquisition circuit can be made to approach the optimal matching parameters.

5. The bio-current self-powered method for wearable devices according to claim 4, characterized in that, The calculation of the optimal matching parameters includes: The output impedance of the electrode-skin interface is modeled as ,in For interface resistance, Polarization resistor, Interface capacitance, Angular frequency; Set the input impedance of the acquisition circuit to be ,in For adjustable input resistance, It is an adjustable input capacitor; Solve the conjugate matching condition , ; The control parameters for the adjustable resistor array and the adjustable capacitor array are set based on the solution results.

6. The bio-current self-powered method for wearable devices according to claim 1, characterized in that, It also includes a dynamic tracking step: repeatedly executing the impedance detection step, impedance modeling step, and matching adjustment step at a preset cycle to track the impedance changes at the electrode-skin interface in real time and dynamically update the matching network parameters.

7. The bio-current self-powered method for wearable devices according to claim 6, characterized in that, In the dynamic tracking step, when the rate of change of model parameters obtained from two adjacent measurements exceeds a preset threshold, the measurement cycle is shortened; when the rate of change of model parameters is lower than the preset threshold, the measurement cycle is extended to reduce power consumption.

8. The bio-current self-powered method for wearable human devices according to claim 1, characterized in that, The energy conversion steps include: An ultra-low noise amplifier circuit is used to amplify the biocurrent signal after impedance matching to a millivolt-level voltage signal. A rectifier circuit is used to convert the amplified AC or pulsating signal into DC power. The gain of the ultra-low noise amplifier circuit is dynamically adjusted according to the signal strength after impedance matching.

9. The bio-current self-powered method for wearable devices according to claim 1, characterized in that, It also includes a monitoring multiplexing step: reusing the biocurrent signal collected in the impedance detection step to separate the electrocardiogram signal and electromyogram signal to generate the user's physiological parameter information.

10. The bio-current self-powered method for wearable devices according to claim 9, characterized in that, The physiological parameter information is used to adjust the frequency range of impedance matching. When electromyographic signals are detected to be dominant, the optimized frequency of impedance matching is shifted to the higher frequency band.

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