Design method of adaptive signal conditioning system of flexible gait sensor

Abstract

The invention provides a design method of an adaptive signal conditioning system of a flexible gait sensor, and belongs to the technical field of flexible sensor signal conditioning. The method comprises the following steps: establishing a gait feature database by analyzing gait features in different states; obtaining design reference values of a gain G and a time constant according to stride frequencies and signal amplitudes of different gait states and requirements on dynamic response characteristics, and calculating corresponding accurate RC values under different gait states through mathematical relationships between the design reference values and capacitors and resistors in a charge amplifier and a low-pass filter circuit; carrying out simulation verification, and carrying out iterative optimization on the key parameters according to a result; designing a complete hardware system, and completing the design and layout of a PCB; and finally, circuit parameters are dynamically adjusted based on a pre-stored stride frequency RC mapping relation, and dynamic adaptive adjustment of the stride frequency is realized. According to the invention, high-precision conditioning output of mV-level weak signals is realized, the precision and stability of signal acquisition are remarkably improved, and the application scene of the system is expanded.

Description

Technical Field

[0001] This invention relates to a design method for an adaptive signal conditioning system for a flexible gait sensor, belonging to the field of flexible sensor signal conditioning technology. Background Technology

[0002] In recent years, flexible wearable gait sensors, as a cutting-edge technology in the field of intelligent sensing and health monitoring, have received widespread attention from academia and industry. Flexible sensors based on piezoelectric composite materials possess both high stability and rapid response characteristics, making them suitable for wearable devices and intelligent robots. Their excellent electromechanical conversion performance and flexible adaptability give them broad application prospects in health monitoring and human-computer interaction. These sensors, with their unique advantages, are driving revolutionary developments in personalized medicine, sports science, and human-computer interaction. Compared to traditional rigid sensors, the new generation of flexible sensors has achieved breakthroughs in material selection, structural design, and functional implementation. Furthermore, the development of IoT technology has brought great convenience to people's production and lives. People can collect large amounts of data through sensors and further wirelessly transmit and process this data in the cloud, realizing the interconnection of everything. Compared to traditional rigid sensors, the new generation of flexible sensors not only exhibits superior mechanical flexibility and biocompatibility but also provides users with a nearly imperceptible natural wearing experience through skin-fitting designs or embedded structures (such as smart insoles and integration into sportswear), greatly improving the practicality and comfort of long-term monitoring.

[0003] The signal conditioning circuit of a flexible wearable gait sensor typically consists of a low-noise charge amplifier and a low-pass filter, which can effectively amplify weak millivolt-level electrical signals while filtering out interference from high-frequency noise signals.

[0004] However, existing flexible wearable gait sensor systems still have significant technical limitations. While they can perform basic signal conditioning functions, they still employ a rudimentary open-loop control method. Control of the sensor is limited to physical buttons or pre-programmed fixed parameters. Due to the inherent fixed gain and cutoff frequency, signal distortion and other issues may occur. This static open-loop control mode cannot adapt to the dynamically changing working environment and usage requirements in gait monitoring, especially when facing users of different ages and different movement states (such as walking and running). It lacks the necessary adaptive capabilities, severely limiting monitoring accuracy and application scope. Summary of the Invention

[0005] The purpose of this invention is to solve the problem of multimodal signal distortion in charge amplifiers and low-pass filters caused by the static open-loop control mode of existing flexible wearable gait sensor systems, which have fixed gain and fixed cutoff frequency. Therefore, this invention provides an adaptive signal conditioning system design method for flexible gait sensors.

[0006] To address the aforementioned problems in existing technologies, this invention innovatively introduces intelligent adaptive adjustment technology, constructing a complete closed-loop feedback control system, which achieves the following breakthroughs:

[0007] 1. A microcontroller controls a digital potentiometer and a switched capacitor array, adjusting the RC value in the circuit to adaptively adjust the sensor's gain and time constant;

[0008] 2. By setting a calibration baseline, setting a baseline buffer, and combining it with a delay time window, the accuracy and reliability of sensor operating state switching are ensured;

[0009] 3. A third-order Sallen-Key topology Butterworth filter is used as the low-pass filter for the signal conditioning circuit, which has excellent out-of-band attenuation characteristics, optimized phase linearity, and stable gain.

[0010] 4. A low-bias-current JFET four-channel input operational amplifier AD8630ARZ-MS is used as the operational amplifier for both the charge amplifier and the low-pass filter to ensure the original characteristics of the signal.

[0011] The objective of this invention is achieved through the following technical solution:

[0012] A design method for an adaptive signal conditioning system for a flexible gait sensor includes the following steps:

[0013] Step 1: Determine the gait frequency under different states. The gait frequency under the i-th state is f. i The sensor output signal is V in i ;

[0014] Step 2: Select an operational amplifier with low bias current to design the charge amplifier and low-pass filter circuit, based on the frequency f determined in Step 1 for the i-th state. i and sensor output signal V in i First, the desired voltage output V out i Calculate the total gain of the circuit. Secondly, ensuring that the cutoff frequency in the i-th state is greater than f. i Under the premise of experience, the initial selection cutoff frequency f is determined. ciFinally, the feedback resistance R was further calculated. f Feedback capacitor C f and time constant The specific value;

[0015] Step 3: Use circuit simulation software to initially verify the feasibility of the circuit designed in Step 2, and adjust the key parameter in Step 2, namely the cutoff frequency f, based on the simulation results. ci Feedback resistor R f Feedback capacitor C f The total gain G of the circuit and the time constant Further iterative optimization will be carried out;

[0016] Step 4: Using an STM32 microcontroller as the main controller and the core of the AD conversion, based on the verification and optimization results of Step 3, design the schematic diagrams of the minimum system, wireless transmission module, signal conditioning circuit, sensor interface circuit, and power supply circuit, and complete the design and layout of the PCB of the adaptive signal conditioning circuit of the flexible gait sensor.

[0017] Step 5: By monitoring the AD conversion result of the adaptive signal conditioning circuit of the flexible gait sensor in Step 4, the current gait frequency state is determined, and based on the gait frequency f... i By mapping to the corresponding RC value, and using a digital potentiometer and a switched capacitor network, combined with an STM32 microcontroller to adjust the RC value, the cutoff frequency and gain are dynamically adjusted to adapt to the step frequency.

[0018] Preferably, the different states mentioned in step 1 include three states: running, walking, and standing still, and the frequency f of the gait signal in the three different synchro states of running, walking, or standing still is determined. i The range and sensor output signal V in i The reference values ​​for the amplitude range are as follows:

[0019] (1) Running state: Gait frequency f1 ranges from 2-3Hz, sensor output signal V in 1 The amplitude range is 0-500mV;

[0020] (2) Slow walking state: the gait frequency f2 is in the range of 1-2Hz, and the sensor output signal V in 2 The amplitude range is 0-50mV;

[0021] (3) Stationary state: Gait frequency f3 is in the range of 0Hz, and the sensor output signal V in 3 The amplitude range is 0-10mV.

[0022] Preferably, the operational amplifier selected in step 2 with low bias current is a precision JFET four-channel input operational amplifier AD8630ARZ-MS, powered by a single 5V power supply; the low-pass filter circuit structure is a third-order Sallen-Key topology Butterworth filter.

[0023] Preferably, the saturation voltage of the operational amplifier is 5V, and the value is selected as follows:

[0024] (1) Fast running state: gain G1=10, time constant =0.1;

[0025] (2) Slow walking state: gain G2=100, time constant =0.2;

[0026] (3) Static state: gain G3=500, time constant =2;

[0027] (4) Low-pass filter cutoff frequency f L =5.5Hz.

[0028] Preferably, based on the aforementioned reference value, the gain calculation formula is used. The feedback capacitor C is obtained. fi The magnitude, and then the time constant Relationship with RC value Give different frequency states and feedback resistor R fi and feedback capacitor C fi The mapping relationship of the values ​​is as follows:

[0029] (1) Running state: C f1 =10uF, R f1 =0.01MΩ, therefore f c1 =1.59Hz;

[0030] (2) Slow walking state: C f2 =1uF, R f2 =0.02MΩ, therefore f c2 =0.795Hz;

[0031] (3) At rest: C f3 =0.2uF, R f3 =10MΩ, therefore f c3 =0.08Hz.

[0032] Preferably, the specific steps in step 3 for preliminarily verifying the feasibility of the design in step 2 using circuit simulation software, and further iteratively optimizing the key parameters based on the simulation results, are as follows:

[0033] Step 31: Draw the charge amplifier and the Sallen-Key topology Butterworth filter in Multisim or Proteus circuit simulation software, and use the mapping relationship between different frequency states and RC values ​​as circuit parameters.

[0034] Step 32: Generate sinusoidal signals of different frequencies with noise using a signal generator to simulate the signal input circuit of the flexible gait sensor, and display the waveform output under different parameters and frequencies using an oscilloscope;

[0035] Step 33: Observe the distortion of the output waveform, DC bias, and the suppression effect on high-frequency noise in Step 32;

[0036] Step 34: If the simulation results are found to be defective based on the observation results in Step 33, adjust the value of RC and repeat Steps 31 to 33 until the defect is eliminated and the frequency response, attenuation slope and signal-to-noise ratio meet the design requirements.

[0037] Preferably, the STM32 microcontroller in step 4 uses an ARM Cortex-M3 core with a 72MHz clock frequency and supports the SPI communication protocol; the wireless transmission module uses a Bluetooth module.

[0038] Preferably, the specific process in step 5, which utilizes a digital potentiometer and a switched capacitor network, combined with an STM32 microcontroller to adjust the RC value, thereby achieving dynamic adaptive adjustment of the cutoff frequency and gain to the step frequency, is as follows:

[0039] A digital potentiometer and switched capacitor array capable of communicating with an STM32 microcontroller are used as the C. f and R f By changing the values ​​of resistors and capacitors through a program, the operating state of the adaptive signal conditioning circuit of the flexible gait sensor is changed according to the mapping relationship between different time frequency states and RC values, so as to achieve adaptation to different time states.

[0040] Preferably, in step 5, the adaptive signal conditioning circuit of the flexible gait sensor includes three gait frequency states, involving the determination of two baselines; the baseline noise V is determined through initial calibration. noise And record a series of slow walking amplitudes V walk And the amplitude V of fast running run The data is used as a reference to design the stationary-slow walking switching baseline T1 and the slow walking-fast running switching baseline T2, and the baseline combined with the delay time window is used as the basis for determining the state machine switching.

[0041] The delay time window is set by delaying the gait signal characteristics to meet the state machine switching conditions. If the gait signal characteristics still meet the switching conditions after the delay ends, the RC parameter values ​​of the circuit are updated.

[0042] Preferably, the baselines T1 and T2 may fluctuate due to high-frequency noise. To filter out the influence of high-frequency noise fluctuations at the baselines on the determination, a baseline buffer is set at the baselines T1 and T2, and it is stipulated that crossing the lower boundary of the buffer from bottom to top and crossing the upper boundary of the buffer from top to bottom is considered as crossing the baseline.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] In the design process of an adaptive signal conditioning system for a flexible gait sensor, this invention fully considers the mapping relationship between different gait states and the RC value of the circuit, and realizes adaptive parameter adjustment through a microcontroller. Compared with the conventional signal conditioning circuit of a flexible wearable gait sensor, it can achieve adjustable gain and gait adaptation, and has a wider range of application prospects.

[0045] This invention comprehensively considers the impact of gait frequency changes on signal distortion and loss under different gait states, and achieves high-precision conditioning output for weak signals at the mV level. It not only significantly improves the accuracy and stability of signal acquisition, but also expands the application scenarios of the system through intelligent adjustment function, which can meet diverse gait analysis needs. This adaptive conditioning scheme provides reliable technical support for the practical application of flexible sensors. Attached Figure Description

[0046] Figure 1 This is a flowchart illustrating the design method of an adaptive signal conditioning system for a flexible gait sensor according to the present invention.

[0047] Figure 2 This is a reference schematic diagram of a Butterworth filter circuit with a third-order Sallen-Key topology and its RC value.

[0048] Figure 3 This is a schematic diagram for the design of a charge amplifier.

[0049] Figure 4 This is the circuit diagram for simulation using Proteus.

[0050] Figure 5 This is a schematic diagram of the simulation results using a fast running state design as an example.

[0051] Figure 6 This diagram illustrates the connection between a digital potentiometer and a switched capacitor array and a microcontroller; where:

[0052] Figure 6(a) is a schematic diagram of the digital potentiometer connection;

[0053] Figure 6 (b) is a schematic diagram of the switched capacitor array connection.

[0054] Figure 7 This is a schematic diagram of a 3D model of the PCB board for the adaptive signal conditioning circuit of a flexible gait sensor.

[0055] Figure 8 The logic block diagram of the main program for gait adaptive switching.

[0056] Figure 9 A schematic diagram of the baseline buffer design and judgment logic.

[0057] Figure 10 This is the minimum system circuit diagram; where:

[0058] Figure 10 (a) Circuit diagram for power supply / download to Type-C;

[0059] Figure 10 (b) Select a circuit diagram for the BOOT;

[0060] Figure 10 (c) is the circuit diagram of the SWD download interface;

[0061] Figure 10 (d) is the circuit diagram of a 32MHz crystal oscillator;

[0062] Figure 10 (e) is the circuit diagram for an 8MHz crystal oscillator;

[0063] Figure 10 (f) is the core circuit diagram of the MCU;

[0064] Figure 10 (g) is the circuit diagram of the 5V to 3.3V module;

[0065] Figure 10 (h) is the circuit diagram of the power switch;

[0066] Figure 10 (i) is the main control filter circuit diagram;

[0067] Figure 10 (j) is the reset circuit diagram;

[0068] Figure 10 (k) is the circuit diagram for the 40-pin interface.

[0069] The Figure 7 In the attached diagram, 1 represents the STM32 microcontroller, 2 represents the four-channel operational amplifier, 3 represents the wireless communication module, and 4 represents the sensor interface. Detailed Implementation

[0070] The present invention will be further described in detail below with reference to the accompanying drawings: This embodiment is implemented under the premise of the technical solution of the present invention, and detailed implementation methods are given, but the protection scope of the present invention is not limited to the following embodiments.

[0071] like Figures 1 to 10 As shown in this embodiment, an adaptive signal conditioning system design method for a flexible gait sensor is proposed. First, gait characteristics under different states are analyzed, including step frequency and flexible sensor signal amplitude, to establish a gait feature database. Then, combining the gait feature database, and based on the step frequency and signal amplitude of different gait states and the requirements for dynamic response characteristics, design reference values ​​for gain G and time constant τ are obtained. The precise RC values ​​for different gait states are calculated through their mathematical relationship with the capacitors and resistors in the charge amplifier and low-pass filter circuits. Next, the RC design results are simulated and verified using simulation software, and key parameters are iteratively optimized based on the results. Then, using an STM32 microcontroller as the core, a complete hardware system including signal conditioning circuits and a wireless transmission module is designed, and the PCB design and layout are completed. Finally, by real-time monitoring of the step frequency state, the circuit parameters are dynamically adjusted based on the pre-stored step frequency RC mapping relationship to achieve high-precision conditioning of weak signals at the mV level, thereby realizing dynamic adaptive adjustment of the step frequency. The adaptive signal conditioning circuit for the flexible gait sensor designed in this invention innovatively achieves high-precision conditioning output for weak signals at the mV level. Specific implementation method one:

[0073] like Figure 1 As shown, an adaptive signal conditioning system design method for a flexible gait sensor is presented, which can achieve adaptive switching between different gait states. The specific steps are as follows:

[0074] Step 1: Determine the gait frequency of the gait signal in different states such as running, walking, or standing still. The gait frequency in the i-th state is f. i The sensor output signal is V in i ;

[0075] Step 2: Select an operational amplifier with low bias current to design the charge amplifier and low-pass filter circuit, based on the frequency f determined in Step 1 for the i-th state. i and sensor output signal V in i First, the desired voltage output V out i Calculate the total gain of the circuit. Secondly, ensuring that the cutoff frequency in the i-th state is greater than f. i Under the premise of experience, the initial selection cutoff frequency f is determined.ci Finally, the feedback resistance R was further calculated. f Feedback capacitor C f and time constant The specific value;

[0076] Step 3: Use circuit simulation software to initially verify the feasibility of the circuit designed in Step 2, and adjust the cutoff frequency f, a key parameter in Step 2, based on the simulation results. ci Feedback resistor R f Feedback capacitor C f The total gain G of the circuit and the time constant Further iterative optimization will be carried out;

[0077] Step 4: Using an STM32 microcontroller as the main controller and the core of the AD conversion, based on the verification and optimization results of Step 3, such as... Figure 10 The schematic diagram shows the design of the minimum system, wireless transmission module, signal conditioning circuit, sensor interface circuit, and power supply circuit. Figure 2 , Figure 3 and Figure 6 As shown, the design and layout of the adaptive signal conditioning circuit PCB for the flexible gait sensor were completed, as follows. Figure 7 As shown;

[0078] Step 5: By monitoring the AD conversion result of the adaptive signal conditioning circuit of the flexible gait sensor in Step 4, the current gait frequency state is determined, and based on the gait frequency f... i By mapping to the corresponding RC value, and using a digital potentiometer and a switched capacitor network, combined with an STM32 microcontroller to adjust the RC value, the cutoff frequency and gain are dynamically adjusted to adapt to the step frequency. Specific Implementation Method Two:

[0080] This specific implementation method is a further explanation of Specific Implementation Method One.

[0081] In step 1, the signal is initially processed and acquired using a simple first-order RC filter. The results are then analyzed to obtain the estimated range of different time-state frequencies and signal amplitudes. The reference values ​​are as follows:

[0082] By initially acquiring and analyzing the signal, the estimated range of different time-state frequencies and signal amplitudes is obtained, and the reference values ​​are calculated as follows:

[0083] (1) Running state: gait frequency f1 ranges from 2-3Hz, sensor output signal amplitude V in 1 The amplitude range is approximately 0-500mV;

[0084] (2) Slow walking state: The gait frequency f2 is approximately in the range of 1-2Hz, and the sensor output signal V in 2 The amplitude range is approximately 0-50mV;

[0085] (3) Stationary state: Gait frequency f3 is in the range of 0Hz, and the amplitude of the sensor output signal V in 3 The amplitude range is approximately 0-10mV; Specific implementation method three:

[0087] This specific implementation method is a further explanation of Specific Implementation Method One.

[0088] Step 2, specifically, involves the following steps:

[0089] (1) Operational amplifier selection: The selected operational amplifier is a precision JFET four-channel input operational amplifier AD8630ARZ-MS;

[0090] (2) Determine the low-pass filter scheme: a third-order Sallen-Key topology Butterworth filter with a cutoff frequency of 5.5Hz and a total gain of 1. Its circuit and RC values ​​are referenced as follows: Figure 2 As shown;

[0091] (3) Determine the gain and time constant of the different states and the cutoff frequency of the low-pass filter:

[0092] 1) Fast running state gain G1=10, time constant =0.1;

[0093] 2) Slow-moving state gain G2=100, time constant =0.2;

[0094] 3) Static gain G3 = 500, time constant =2;

[0095] 4) Low-pass filter cutoff frequency f L =5.5Hz.

[0096] (4) Calculation of key parameters: The charge amplifier circuit reference is as follows Figure 3 As shown, C f and R f These are the feedback capacitor and the parallel resistor, respectively, calculated using the gain formula. The value of the feedback capacitor is obtained, and then the relationship between the time constant and RC is used. Give different frequency states and feedback resistor R fi and feedback capacitor C fi The mapping relationship of the values ​​is as follows, where, ;

[0097] 1) Fast running mode (2-3Hz): C f1 =10uF, R f1 =0.01MΩ, therefore f c1 =1.59Hz;

[0098] 2) Slow walking mode (1-2Hz): C f2 =1uF, R f2 =0.02MΩ, therefore f c2 =0.795Hz;

[0099] 3) Stationary state (0Hz): C f3 =0.2uF, R f3 =10MΩ, therefore f c3 =0.08Hz;

[0100] The cutoff frequency is related to the step frequency range. It is necessary to ensure that the cutoff frequency is greater than the step frequency. The specific value is verified in the simulation. Specific implementation method four:

[0102] This specific implementation method is a further explanation of Specific Implementation Method One.

[0103] Step 3 is specifically operated as follows:

[0104] (1) Draw the charge amplifier and Sallen-Key topology Butterworth filter in circuit simulation software such as Multisim and Proteus, and use the mapping relationship between different frequency states and RC values ​​as circuit parameters.

[0105] (2) Generate sinusoidal signals with different frequencies and noise through a signal generator to simulate the signal input of the flexible gait sensor and then simulate the subsequent circuit. Use an oscilloscope to display the waveform output under different parameters and frequencies.

[0106] (3) Observe the waveform distortion, DC bias, and the suppression effect on high-frequency noise;

[0107] (4) If the simulation results have defects and cutoff distortion occurs, increase C. f If the magnification factor is too high, decrease it; if the magnification effect is not ideal, decrease C. f Increase the magnification factor by adjusting the value of the time constant; if hysteresis distortion occurs, decrease the time constant. Calculate R f Repeat the above steps until the defect is eliminated and the frequency response, attenuation slope and signal-to-noise ratio meet the design requirements.

[0108] Step 3, taking fast running state design as an example, has the following simulation circuit: Figure 4 As shown, its gain G is 10, the input signal is a 2Hz low-frequency sine wave signal with a 50Hz noise signal, and the simulation results are as follows. Figure 5 As shown, the first waveform is the result after amplification and filtering, the second waveform is the result after amplification but without filtering, and the third waveform is the original signal. This achieves signal amplification, high-frequency noise filtering, and no obvious distortion. Specific implementation method five:

[0110] This specific implementation method is a further explanation of Specific Implementation Method One.

[0111] In step 4, due to C f The value corresponds to only three sets of values ​​under different time states. Therefore, the capacitance value is the result calculated above. The capacitors are connected in parallel, with each connected in series with an NMOS transistor to control the on / off state. The capacitor is controlled by GPIO. f The size; digital potentiometers use I 2 C communicates with the microcontroller to control R. f The adjustment. The connection between the digital potentiometer and the switched capacitor array and the STM32 microcontroller is as follows: Figure 6 As shown, the 3D model of the PCB board for the signal conditioning circuit of the flexible wearable gait sensor is as follows: Figure 7 As shown. Specific implementation method six:

[0113] This specific implementation method is a further explanation of Specific Implementation Method One.

[0114] Step 5 mainly includes three aspects of design methods;

[0115] (1) Design of the main program for gait adaptive switching: The logical block diagram of the program operation is as follows Figure 8 As shown, it mainly includes three aspects: gait state judgment, dynamic adjustment of RC value, AD conversion, and data transmission and storage.

[0116] (2) Determination of state switching baseline: This sensor has three gait frequency states, involving the determination of two baselines. This is achieved by detecting the number of times (M) the peak crosses baselines T1 and T2 within a specific time T, and using this as the basis for judgment. The values ​​of T1 and T2 can be determined based on the initial calibrated slow walking amplitude V. walk And the amplitude V of fast running run The dataset was used to determine the design process, which is as follows:

[0117] 1) Determining a confidence level C depends on the reliability requirements of the system;

[0118] 2) Based on the initial calibration data, select the values ​​of T1 and T2 to ensure the slow walking amplitude V walk And the amplitude V of fast running run Under the probability conditions required by confidence level C, it will not exceed the baselines T1 and T2.

[0119] (3) Gait Adaptive Switching State Machine Design: This state machine mainly includes three states: stationary, jogging, and running, designated S1, S2, and S3 respectively. It involves four switching conditions: stationary-walking, walking-running, running-walking, and walking-stationary, with switching conditions A... s-w A w-r And B r-w B w-s A schematic diagram of the state machine for intelligent gait switching is shown below. Figure 8 As shown;

[0120] (4) Design of the threshold baseline for state machine transitions and its buffer and delay window:

[0121] 1) Baseline Buffer Design: Baseline buffers are defined for baselines T1 and T2. A baseline crossing is defined as crossing the lower bound of the buffer from bottom to top and the upper bound from top to bottom. The width of the buffer is equal to W. T The amplitude of the noise signal is related and can be determined by the baseline noise V. noise To determine, it should be ensured that W T ≥2ⅹV noise , Figure 9 This is a schematic diagram of the baseline buffer design and judgment logic;

[0122] 2) Delay window design: When the characteristics of the gait signal meet the conditions for state machine switching, a delay D is set. If the gait characteristics still meet the switching conditions after the delay ends, the RC parameter values ​​of the circuit are updated.

[0123] The above description is merely a preferred embodiment of the present invention. These specific embodiments are different implementations based on the overall concept of the present invention, and the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A design method for an adaptive signal conditioning system for a flexible gait sensor, characterized in that, Includes the following steps: Step 1: Determine the gait frequency under different states. The gait frequency under the i-th state is f. i The sensor output signal is V in i ; Step 2: Select an operational amplifier with low bias current to design the charge amplifier and low-pass filter circuit, based on the frequency f determined in Step 1 for the i-th state. i and sensor output signal V in i First, the desired voltage output V out i Calculate the total gain of the circuit. ; Secondly, ensuring that the cutoff frequency in the i-th state is greater than f i Under the premise of experience, the initial selection cutoff frequency f is determined. ci Finally, the feedback resistance R was further calculated. f Feedback capacitor C f and time constant The specific value; Step 3: Use circuit simulation software to initially verify the feasibility of the circuit designed in Step 2, and adjust the key parameter in Step 2, namely the cutoff frequency f, based on the simulation results. ci Feedback resistor R f Feedback capacitor C f The total gain G of the circuit and the time constant Further iterative optimization will be carried out; Step 4: Using an STM32 microcontroller as the main controller and the core of the AD conversion, based on the verification and optimization results of Step 3, design the schematic diagrams of the minimum system, wireless transmission module, signal conditioning circuit, sensor interface circuit, and power supply circuit, and complete the design and layout of the PCB of the adaptive signal conditioning circuit of the flexible gait sensor. Step 5: By monitoring the AD conversion result of the adaptive signal conditioning circuit of the flexible gait sensor in Step 4, the current gait frequency state is determined, and based on the gait frequency f... i By mapping to the corresponding RC value, and using a digital potentiometer and a switched capacitor network, combined with an STM32 microcontroller to adjust the RC value, the cutoff frequency and gain are dynamically adjusted to adapt to the step frequency.

2. The design method for an adaptive signal conditioning system of a flexible gait sensor according to claim 1, characterized in that, The different states mentioned in step 1 include three states: running, walking, and standing still. The frequency f of the gait signal is determined in each of these three different gait states. i The range and sensor output signal V in i The reference values ​​for the amplitude range are as follows: (1) Running state: Gait frequency f1 ranges from 2-3Hz, sensor output signal V in 1 The amplitude range is 0-500mV; (2) Slow walking state: the gait frequency f2 is in the range of 1-2Hz, and the sensor output signal V in 2 The amplitude range is 0-50mV; (3) Stationary state: Gait frequency f3 is in the range of 0Hz, and the sensor output signal V in 3 The amplitude range is 0-10mV.

3. The design method for an adaptive signal conditioning system of a flexible gait sensor according to claim 2, characterized in that, In step 2, the operational amplifier selected for low bias current is the precision JFET four-channel input operational amplifier AD8630ARZ-MS, powered by a single 5V power supply; the low-pass filter circuit structure is a third-order Sallen-Key topology Butterworth filter.

4. The design method for an adaptive signal conditioning system of a flexible gait sensor according to claim 3, characterized in that, The saturation voltage of the operational amplifier is 5V, so the following values ​​are recommended: (1) Fast running state: gain G1=10, time constant =0.1; (2) Slow walking state: gain G2=100, time constant =0.2; (3) Static state: gain G3=500, time constant =2; (4) Low-pass filter cutoff frequency f L =5.5Hz.

5. The design method for an adaptive signal conditioning system of a flexible gait sensor according to claim 4, characterized in that, Based on the aforementioned reference values, the gain calculation formula is used. The feedback capacitor C is obtained. fi The magnitude, and then the time constant Relationship with RC value Give different frequency states and feedback resistor R fi and feedback capacitor C fi The mapping relationship of the values ​​is as follows: (1) Running state: C f1 =10uF, R f1 =0.01MΩ, therefore f c1 =1.59Hz; (2) Slow walking state: C f2 =1uF, R f2 =0.02MΩ, therefore f c2 =0.795Hz; (3) At rest: C f3 =0.2uF, R f3 =10MΩ, therefore f c3 =0.08Hz.

6. The design method of an adaptive signal conditioning system for a flexible gait sensor according to claim 1, characterized in that, Step 3 involves using circuit simulation software to initially verify the feasibility of the design in Step 2, and then iteratively optimizing the key parameters based on the simulation results. The specific steps are as follows: Step 31: Draw the charge amplifier and the Sallen-Key topology Butterworth filter in Multisim or Proteus circuit simulation software, and use the mapping relationship between different frequency states and RC values ​​as circuit parameters. Step 32: Generate sinusoidal signals of different frequencies with noise using a signal generator to simulate the signal input circuit of the flexible gait sensor, and display the waveform output under different parameters and frequencies using an oscilloscope; Step 33: Observe the distortion of the output waveform, DC bias, and the suppression effect on high-frequency noise in Step 32; Step 34: If the simulation results are found to be defective based on the observation results in Step 33, adjust the value of RC and repeat Steps 31 to 33 until the defect is eliminated and the frequency response, attenuation slope and signal-to-noise ratio meet the design requirements.

7. The design method for an adaptive signal conditioning system of a flexible gait sensor according to claim 1, characterized in that, The STM32 microcontroller mentioned in step 4 uses an ARM Cortex-M3 core with a main frequency of 72MHz and supports the SPI communication protocol; the wireless transmission module uses a Bluetooth module.

8. The design method of an adaptive signal conditioning system for a flexible gait sensor according to claim 1, characterized in that, The specific process described in step 5, which utilizes a digital potentiometer and a switched capacitor network in conjunction with an STM32 microcontroller to adjust the RC value, thereby achieving dynamic adaptive adjustment of the cutoff frequency and gain to the step frequency, is as follows: A digital potentiometer and switched capacitor array capable of communicating with an STM32 microcontroller are used as the C. f and R f By changing the values ​​of resistors and capacitors through a program, the operating state of the adaptive signal conditioning circuit of the flexible gait sensor is changed according to the mapping relationship between different time frequency states and RC values, so as to achieve adaptation to different time states.

9. The design method of an adaptive signal conditioning system for a flexible gait sensor according to claim 1, characterized in that, Step 5 involves an adaptive signal conditioning circuit for the flexible gait sensor, which includes three gait frequency states and involves the determination of two baselines; the baseline noise V is determined through initial calibration. noise And record a series of slow walking amplitudes V walk And the amplitude V of fast running run The data is used as a reference to design the stationary-slow walking switching baseline T1 and the slow walking-fast running switching baseline T2, and the baseline combined with the delay time window is used as the basis for determining the state machine switching. The delay time window is set by delaying the gait signal characteristics to meet the state machine switching conditions. If the gait signal characteristics still meet the switching conditions after the delay ends, the RC parameter values ​​of the circuit are updated.

10. The design method of an adaptive signal conditioning system for a flexible gait sensor according to claim 9, characterized in that, The baselines T1 and T2 may fluctuate due to high-frequency noise. To filter out the influence of high-frequency noise fluctuations on the determination, a baseline buffer is set at baselines T1 and T2. It is stipulated that crossing the lower boundary of the buffer from bottom to top and crossing the upper boundary of the buffer from top to bottom is considered a baseline crossing.

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