Intelligent Taijiquan training and health preserving effect monitoring system and method

Abstract

The invention relates to the technical field of intelligent health, in particular to an intelligent shadowboxing training and health preserving effect monitoring system and method, and the system comprises an impedance collection module, a coordinate synchronization module, a rate positioning module, a time comparison module and a quantitative evaluation module. According to the method, weighted mapping analysis is carried out on trunk compression distribution parameters and thoracico-abdominal fluctuation signals in a time domain dimension, and a hidden time sequence corresponding relation between plantar force generation support change tracks and body surface impedance relaxation peak-valley nodes is excavated based on a dynamic normalized accumulation matrix; a closed-loop monitoring link penetrating through body movement and breath vomiting and reception difference is constructed, the real force generation coupling compactness is judged by means of transverse cross-domain comparison of gravity center conversion time phases, the overall coherence and coordination concepts undertaken in Tai Chi guiding movement are effectively restored, accurate adjustment intervention guidance with quantitative support is synchronously issued to a terminal, and the accuracy of the Tai Chi guiding movement is improved. And the discrimination validity of the multi-source fusion physiological data in the deep rehabilitation dimension is directly improved.

Description

Technical Field

[0001] This invention relates to the field of smart health technology, and in particular to an intelligent system and method for monitoring the training and health benefits of Tai Chi. Background Technology

[0002] The field of smart health technology mainly involves a comprehensive system that integrates the Internet of Things, micro wearable devices, mobile communication networks, and clinical case comparison for personal daily vital sign recording and medical auxiliary monitoring. Its core aspects include continuous reading of human physiological indicators, refined capture of limb movement spatial posture, home monitoring of chronic diseases, and personalized TCM rehabilitation movement tracking. This overall technology field involves directly attaching detection devices, including triaxial accelerometers, photoplethysmography sensors, and thin-film pressure-sensitive resistors, to the human epidermis or embedding them in wearable fabrics to continuously acquire human ventricular beat frequency, arterial pressure, capillary blood oxygen saturation, three-dimensional coordinate position changes of skeletal joints, and cortical muscle electrophysiological excitation potentials. Subsequently, relying on distributed cloud server nodes, the above-mentioned human physiological values ​​and motion dynamic parameters are stored by category and compared with thresholds. Among them, the traditional intelligent Tai Chi training and health preservation effect monitoring system refers to the quantitative verification of the standardization of movement trajectory during Tai Chi exercises and the continuous measurement of the intensity of the practitioner's cardiopulmonary physiological load. It typically uses a nine-axis microelectromechanical inertial sensor array, strapped to specific positions on both wrists, both ankles, and the lumbar spine, to collect the time series of three-dimensional spatial acceleration and angular velocity changes during specific Tai Chi movements such as "Wild Horse Parts Its Mane" and "White Crane Spreads Its Wings." It also uses medical silver chloride patch electrodes attached to the skin of the practitioner's left chest to continuously collect single-lead weak electrocardiogram potential waveforms. The inertial sensing sequence is then transmitted to the microprocessor via the built-in serial communication peripheral bus. The sequence is then compared with the three-dimensional coordinate reference point sequence of the movement trajectory of each joint of a standard Tai Chi instructor's body, which is pre-programmed into a read-only memory chip. Dynamic time warping matching algebraic operations are performed to calculate the absolute value of the Euclidean distance coordinate deviation of the corresponding human joint point. At the same time, the time sequence of the adjacent R-wave peak interval of the collected electrocardiogram potential waveform is extracted in the instruction execution cycle of the microprocessor, and the discrete Fourier transform mathematical equation is used to directly obtain the quantization parameter of the ratio of variability of the power spectral density in the high-frequency and low-frequency bands.

[0003] Existing technologies rely on sensor arrays and ECG patches attached to the human torso to read local acceleration and surface potential waveforms. The independently acquired coordinates and isolated heart rate sequences are then substituted into time warping and spectral transformation to perform calculations. This layered, fragmented data acquisition mode makes it difficult to establish the transient mechanical constraints between whole-body movement and chest and abdominal breathing. Isolated comparisons easily sever the interaction between the center of gravity of Tai Chi body movements and the internal rhythmic operation of the body. As a result, the effect evaluation is limited to the verification of the similarity of the apparent movements and the qualitative level of basic power consumption, which in turn restricts the accurate quantitative guidance for deep physiological synergistic mechanisms. Summary of the Invention

[0004] To address the shortcomings of existing technologies that rely on separately substituting independently acquired coordinates and isolated heart rate sequences into time warping and spectral transformation for calculations, this layered, fragmented data acquisition mode struggles to establish the transient mechanical constraints between whole-body movement and chest and abdominal breathing. Isolated comparisons easily sever the interaction between the center of gravity shift in Tai Chi movements and the body's internal rhythmic processes, resulting in effect evaluations remaining at the level of superficial movement similarity verification and basic power consumption qualitative assessment. This hinders the technical problem of accurately quantifying and guiding deep physiological synergistic mechanisms. Therefore, this invention provides an intelligent Tai Chi training and health preservation effect monitoring system and method. The technical solution is as follows: On the one hand, it provides an intelligent Tai Chi training and health preservation effect monitoring system, which includes: The impedance acquisition module acquires the output voltage signal of the Tai Chi chest and abdomen impedance measurement circuit, multiplies the output voltage signal with the set impedance conversion constant, extracts the timestamp when the product value is greater than the adjacent acquisition point, and generates a set of respiratory rhythm extreme value timestamps. The coordinate synchronization module obtains the array pressure value and the sensor two-dimensional coordinate value according to the set of extreme timestamps of the respiratory rhythm. It multiplies the array pressure value by the area of ​​the sensor detection unit to obtain the force value, then multiplies the force value by the sensor two-dimensional coordinate value and performs normalized cumulative division operation to obtain the center vector of plantar pressure. The rate positioning module extracts the coordinate difference between adjacent time sections based on the plantar pressure center vector, performs quadratic addition and square root operation, divides the calculation result by the time span to obtain the pressure center movement rate value, and generates a time stamp sequence of extreme values ​​of the center of gravity transformation. The time comparison module uses the extreme timestamp sequence of center of gravity transformation to extract the timestamp of center of gravity transformation and the timestamp of breathing rhythm. It performs a subtraction operation between the timestamp of center of gravity transformation and the timestamp of breathing rhythm to obtain the time difference value. The time difference value is compared with the benchmark value of Tai Chi body coordination to construct a body breathing coordination feature sequence. The quantitative assessment module calls the body movement and breathing coordination feature sequence, counts the frequency of occurrences where the time difference value is within the range of the Tai Chi body movement coordination benchmark value, divides the occurrence frequency by the total number of Tai Chi movements to calculate the physiological force coupling degree value, and compares it with the preset health and wellness threshold to generate a health and wellness effect monitoring and evaluation level.

[0005] As a further aspect of the present invention, the set of extreme timestamps for respiratory rhythm includes the moment of maximum inhalation filling, the moment of maximum exhalation emptying, and the transient point of breath-holding transition; the center vector of plantar pressure includes the lateral displacement vector, the longitudinal load-bearing vector, and the directional deflection angle; the sequence of extreme timestamps for center of gravity transition includes the critical moment of alternation between virtual and real states, the peak moment of dynamic force exertion, and the trough moment of the consolidation buffer; the sequence of body movement and breathing coordination characteristics includes the gas rhythm delay step length, the resonance index of form and qi frequency, and the deviation amplitude of breathing movements; and the health preservation effect monitoring and evaluation levels include the advanced level of deep guidance, the standard level of regular stretching, and the level of qi stagnation awaiting further advancement.

[0006] As a further aspect of the present invention, the impedance acquisition module includes: The voltage signal conversion submodule acquires the output voltage signal of the Tai Chi chest and abdomen impedance measurement circuit, retrieves the set impedance conversion constant, performs a multiplication operation between the output voltage signal and the set impedance conversion constant, extracts the peak and trough conversion nodes in the output value of the multiplication operation, records the amplitude value corresponding to each conversion node, and constructs data points by combining the accumulated count value within the sampling clock cycle. The data points are arranged in sequence to generate a chest and abdomen impedance product sequence. The time-series extreme value determination submodule calls the chest and abdominal impedance product sequence, reads the values ​​of each acquisition point, compares the real-time acquisition point values ​​with the values ​​of adjacent acquisition points, extracts the timestamp parameters of the corresponding acquisition point when the real-time acquisition point value is greater than the values ​​of adjacent acquisition points, aggregates the extracted timestamp parameters within the differentiated time series and arranges them in ascending order to establish a respiratory rhythm extreme value timestamp set.

[0007] As a further aspect of the present invention, the coordinate synchronization module includes: The pressure coordinate alignment submodule calls the set of extreme timestamps of the respiratory rhythm, reads the time section parameters corresponding to the timestamps, extracts the array pressure values ​​output by the foot contact surface pressure detection unit under the time section, and simultaneously extracts the two-dimensional coordinate values ​​of the sensors built into the corresponding array nodes. It performs timestamp alignment and merging processing on the array pressure values ​​and the sensor two-dimensional coordinate values, combines the pressure and coordinate correlation items under the same time section, and establishes the foot space pressure mapping matrix. The surface source data conversion submodule extracts the array pressure value and sensor two-dimensional coordinate value based on the foot space pressure mapping matrix. It multiplies the array pressure value by the area of ​​the sensor detection unit to obtain the force value, and then performs an algebraic multiplication operation on the force value and the sensor two-dimensional coordinate value to obtain the nodal torque parameter. It performs cumulative summation on the nodal torque parameter to extract the total torque value, and performs normalized cumulative division operation by dividing the total torque value by the array pressure value to generate the foot pressure center vector.

[0008] As a further aspect of the present invention, the rate positioning module includes: The coordinate difference calculation submodule calls the plantar pressure center vector, extracts the two-dimensional coordinate parameters of adjacent time sections, subtracts the coordinate parameters of the next section from the coordinate parameters of the previous section to extract the coordinate difference, performs quadratic calculation on the horizontal and vertical components of the coordinate difference respectively, integrates the two square values ​​and performs an addition operation to extract the sum of squares parameter, performs a square root calculation on the sum of squares parameter to obtain the displacement scalar value, and arranges the displacement scalar values ​​in order to obtain the time-series displacement scalar set; The moving rate calculation submodule reads the displacement scalar values ​​corresponding to each time section based on the time-series displacement scalar set, retrieves the span values ​​between adjacent time sections, divides the displacement scalar values ​​by the time span values ​​to perform a division operation, calculates the pressure center moving rate parameters under different time nodes, and aggregates the pressure center moving rate parameters in chronological order to generate a pressure center moving rate sequence. The center of gravity extreme value filtering submodule extracts the real-time node pressure center movement rate parameters for the pressure center movement rate sequence, performs a numerical comparison operation between the real-time node movement rate parameters and the rate parameters of the adjacent nodes, filters the time stamp data when the real-time node rate parameters show extreme value states, integrates the filtered time stamp data in the differentiated time series and performs ascending sorting to obtain the center of gravity transformation extreme value time stamp sequence.

[0009] As a further aspect of the present invention, the real-time node movement rate parameter is compared with the rate parameters of the preceding and following adjacent nodes, including the rate parameters of the previous node and the rate parameters of the subsequent node. When the real-time node movement rate parameter is determined to be greater than the preceding node's rate parameter and greater than the following node's rate parameter, the real-time node movement rate parameter is defined as a local maximum extreme value state. When the real-time node movement rate parameter is determined to be less than the preceding node's rate parameter and less than the following node's rate parameter, the real-time node movement rate parameter is defined as a local minimum extreme value state. Separate the high-order extreme timestamp data corresponding to the real-time node movement rate parameter when it is in a local maximum extreme state, separate the low-order extreme timestamp data corresponding to the real-time node movement rate parameter when it is in a local minimum extreme state, and summarize the high-order extreme timestamp data and the low-order extreme timestamp data to construct the attached timestamp data.

[0010] As a further aspect of the present invention, the time comparison module includes: The phase extraction submodule calls the extreme timestamp sequence of the center of gravity transformation, reads the attached time node parameters, extracts the center of gravity transformation timestamp corresponding to the moment when the Tai Chi body movement transformation occurs, and synchronously retrieves the breathing rhythm timestamps in the same time period within the extreme timestamp set of breathing rhythm. It performs time sequence alignment processing on the center of gravity transformation timestamp and the breathing rhythm timestamp, combines the time sequence correlation items of the two, and establishes a body movement breathing time sequence mapping matrix. The time phase difference calculation submodule, based on the body breathing time sequence mapping matrix, extracts the center of gravity conversion timestamp and breathing rhythm timestamp under the same matching alignment relationship, performs a numerical subtraction calculation operation on the center of gravity conversion timestamp and breathing rhythm timestamp, obtains the time phase difference value corresponding to the body movement conversion and chest and abdominal undulation breathing, and aggregates the time phase difference values ​​in chronological order to obtain a time phase difference value set. The collaborative feature comparison submodule reads the time difference parameters corresponding to the time cross section for the set of time difference values, retrieves the Tai Chi body coordination benchmark values ​​in the pre-recorded storage area, performs an interval comparison operation between the time difference parameters under the time cross section and the Tai Chi body coordination benchmark values, marks the time nodes that are within or deviate from the benchmark value interval, aggregates all time nodes and comparison status calibration items, and obtains the body breathing coordination feature sequence.

[0011] As a further aspect of the present invention, the lower limit value and the upper limit value of the reference value for body coordination in Tai Chi Chuan are extracted, and the phase difference parameter under the time section is compared with the lower limit value and the upper limit value of the reference value respectively. When the phase difference parameter under the time section is greater than or equal to the lower limit of the reference value and less than or equal to the upper limit of the reference value, the phase difference parameter under the time section is defined as being within the reference value range. For time nodes within the reference value range, a collaborative matching calibration value is assigned. When the phase difference parameter under the time section is determined to be less than the lower limit of the reference value or greater than the upper limit of the reference value, the phase difference parameter under the time section is defined to deviate from the reference value range. For time nodes that deviate from the reference value range, a collaborative deviation calibration value is assigned. The collaborative matching calibration value and the collaborative deviation calibration value are aggregated to construct a comparison status calibration item.

[0012] As a further aspect of the present invention, the quantitative evaluation module includes: The frequency statistics submodule calls the body breathing coordination feature sequence, reads the corresponding comparison status calibration items at each time node, retrieves the corresponding calibration parameters within the Tai Chi body coordination benchmark value range, performs an equivalence matching check operation on the calibration items at each time node and the calibration parameters, filters the time segments that meet the equivalence matching conditions, performs an accumulation counting operation on the filtered time segments to extract the occurrence frequency parameters, and obtains the coordination standard achievement frequency value. The coupling degree calculation submodule, based on the frequency value of the collaborative achievement, reads the corresponding occurrence frequency parameter extracted by the cumulative counting operation, synchronously retrieves the corresponding total number of movements in the real-time Tai Chi training cycle, divides the occurrence frequency parameter by the total number of movements to perform a division algebra calculation operation, extracts the proportional quantitative parameter corresponding to the physiological force exertion between breathing and limb movement, and aggregates the proportional quantitative parameter in chronological order to establish the physiological force exertion coupling degree value; The health preservation level assessment submodule reads the proportional quantification parameter corresponding to the physiological exertion coupling degree value, retrieves the preset health preservation threshold, performs an interval boundary comparison operation between the proportional quantification parameter and the preset health preservation threshold, determines the segment of the preset health preservation threshold interval in which the proportional quantification parameter falls, assigns the corresponding quantitative assessment label according to the corresponding health preservation threshold interval segment, and sequentially aggregates the assigned quantitative assessment labels to generate a health preservation effect monitoring assessment level.

[0013] On the other hand, the intelligent Tai Chi training and health preservation effect monitoring method, which is based on the aforementioned intelligent Tai Chi training and health preservation effect monitoring system, includes the following steps: S1: Collect the output voltage signal of the Tai Chi chest and abdomen impedance measurement circuit, multiply the output voltage signal with the set impedance conversion constant, extract the timestamp when the product value is greater than the adjacent collection point, and generate a set of respiratory rhythm extreme value timestamps. S2: Based on the set of extreme timestamps of the respiratory rhythm, obtain the array pressure value and the sensor two-dimensional coordinate value, multiply the array pressure value by the area of ​​the sensor detection unit to obtain the force value, multiply the force value by the sensor two-dimensional coordinate value, and perform normalized cumulative division operation to obtain the plantar pressure center vector. S3: Based on the plantar pressure center vector, extract the coordinate difference between adjacent time sections, perform quadratic addition and square root operation, divide the calculation result by the time span to obtain the pressure center movement rate value, and generate the center of gravity transformation extreme value timestamp sequence. S4: Using the aforementioned extreme timestamp sequence of center of gravity transformation, extract the timestamp of center of gravity transformation and the timestamp of breathing rhythm, perform a subtraction operation between the timestamp of center of gravity transformation and the timestamp of breathing rhythm to obtain the phase difference value, compare the phase difference value with the Tai Chi body coordination benchmark value, and construct a body breathing coordination feature sequence. S5: Call the body movement and breathing coordination feature sequence, count the frequency of occurrences where the time difference value is within the range of the Tai Chi body movement coordination benchmark value, divide the occurrence frequency by the total number of Tai Chi movements to calculate the physiological force coupling degree value, and compare it with the preset health and wellness threshold to generate a health and wellness effect monitoring and evaluation level.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By performing weighted mapping analysis on the trunk pressure distribution parameters and chest and abdominal fluctuation signals in the same domain, and based on the dynamic normalized cumulative matrix, the latent temporal correspondence between the foot force support trajectory and the diastolic peak and trough nodes of the body surface impedance is mined. A closed-loop monitoring link is constructed that runs through the difference between body movement and breathing, overcoming the problem of internal and external physical signs separation caused by the data barrier of a single sensor source. By using horizontal cross-domain comparison of the center of gravity transformation phase to determine the actual force coupling tightness, the overall coherent and coordinated meaning of the Tai Chi guiding movement is effectively restored. At the same time, precise adjustment and intervention guidance with quantitative support is sent to the terminal, directly improving the discriminative validity of multi-source fusion physiological data in the dimension of deep rehabilitation. Attached Figure Description

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

[0016] Figure 1 This is a schematic diagram of the system provided by the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the impedance acquisition module in this invention; Figure 4 This is a flowchart of the coordinate synchronization module in this invention; Figure 5 This is a flowchart of the rate positioning module in this invention; Figure 6 This is a flowchart of the time comparison module in this invention; Figure 7 This is a flowchart of the quantitative evaluation module in this invention; Figure 8This is a flowchart of the method provided by the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0019] This invention provides an intelligent system for monitoring the training and health benefits of Tai Chi, such as... Figure 1-2 The diagram shown illustrates an intelligent Tai Chi training and health preservation effect monitoring system. This system includes: The impedance acquisition module acquires the output voltage signal of the impedance measurement circuit for the chest and abdomen of Tai Chi Chuan, multiplies the output voltage signal by the impedance conversion constant to calculate the impedance change value, compares the impedance change values ​​of adjacent time nodes, extracts the absolute timestamp of the impedance change value reaching the extreme value, and generates a set of extreme timestamps of respiratory rhythm. The coordinate synchronization module obtains the array pressure value of the smart insole array and the two-dimensional coordinate value of the sensor based on the set of extreme timestamps of the breathing rhythm. It multiplies the array pressure value by the area of ​​the sensor detection unit to obtain the force value, then multiplies the force value by the sensor two-dimensional coordinate value and performs normalized cumulative division operation to obtain the center vector of the plantar pressure. The velocity positioning module extracts the coordinate difference between adjacent time sections based on the plantar pressure center vector, performs quadratic addition and square root operation on the coordinate difference to extract the absolute displacement value, divides the absolute displacement value by the time span value to calculate the pressure center movement rate value, filters the timestamps corresponding to the pressure center movement rate value reaching the extreme value, and generates a timetamp sequence of extreme values ​​of center of gravity transformation. The time comparison module uses the extreme timestamp sequence of center of gravity transformation to extract the timestamp of a single center of gravity transformation and the timestamp of a single breathing rhythm. It performs a subtraction operation between the timestamp of a single center of gravity transformation and the timestamp of a single breathing rhythm to extract the absolute value of the time difference. The absolute value of the time difference is compared with the benchmark value of Tai Chi body coordination to construct a body breathing coordination feature sequence. The quantitative assessment module calls the body movement and breathing coordination feature sequence, counts the frequency of occurrence of the absolute difference between the occurrence values ​​within the range of the Tai Chi body movement coordination benchmark value, divides the occurrence frequency value by the total number of Tai Chi movements to calculate the physiological force coupling degree value, compares the physiological force coupling degree value with the preset health and wellness threshold, and generates a health and wellness effect monitoring and evaluation level. The set of extreme timestamps for respiratory rhythm includes the moment of maximum inhalation, the moment of maximum exhalation, and the moment of breath-holding transition. The center vector of plantar pressure includes the lateral displacement vector, the longitudinal load-bearing vector, and the directional deflection angle. The sequence of extreme timestamps for center of gravity transition includes the critical moment of alternation between emptiness and fullness, the peak moment of dynamic force exertion, and the trough moment of the consolidation. The sequence of body movement and breathing coordination characteristics includes the gas rhythm delay step length, the resonance index of form and qi frequency, and the deviation amplitude of breathing movements. The health preservation effect monitoring and evaluation levels include the advanced level of deep guidance, the standard level of regular stretching, and the level of qi blockage that needs to be advanced.

[0020] Specifically, such as Figure 2 , 3 As shown, the impedance acquisition module includes: The voltage signal conversion submodule acquires the output voltage signal of the Tai Chi chest and abdomen impedance measurement circuit, retrieves the set impedance conversion constant, performs a multiplication operation between the output voltage signal and the set impedance conversion constant, extracts the peak and trough conversion nodes in the output value of the multiplication operation, records the amplitude value corresponding to each conversion node, and constructs data points by combining the accumulated count value within the sampling clock cycle. The data points are arranged in sequence to generate a chest and abdomen impedance product sequence. The output voltage signal of the Tai Chi chest and abdomen impedance measurement circuit is acquired through four flexible fabric dry electrode sensors configured on the chest and abdomen of the test subject. The signal conditioning component inside this module directly performs low-pass filtering and noise reduction on the raw input voltage signal, eliminating environmental power frequency interference and high-frequency electromyographic noise, and extracting the filtered, clean output voltage signal. The module retrieves the preset impedance conversion constant pre-stored in the flash memory chip via the internal I2C bus. The preset impedance conversion constant is obtained during the initial calibration phase by injecting a constant alternating current of 1 mA into a known standard 100-ohm precision resistor network, measuring the voltage across its terminals, and performing a division operation to calibrate it. The specific preset impedance conversion constant value is set to 45.5 ohms per volt. The output voltage signal is then multiplied by the preset impedance conversion constant. Specifically, the real-time voltage amplitude data is directly multiplied by this constant value to obtain the real-time physical impedance value of the chest and abdomen. For example, when the extracted real-time output voltage signal value is 2.2 volts, the module performs a multiplication algebraic operation on 2.2 and 45.5, obtaining the corresponding real-time physical impedance value of the chest and abdomen as 100.1 ohms. The advantage of this operation logic is that it directly converts the electrical signal into physical impedance through a linear scaling factor, avoiding the underlying hardware calculation delay caused by complex nonlinear curve fitting. The internal extreme value detection unit continuously extracts peak and trough transition nodes within the output value of the multiplication operation. The extreme value detection unit uses sliding time window differential logic, with the window length strictly set to 15 consecutive sampling points, comparing the impedance gradient change direction of the current sampling point with the five adjacent sampling points before and after it. When the impedance gradient value changes from positive to negative, the module identifies it as a peak transition node; when the impedance gradient value changes from negative to positive, the module identifies it as a trough transition node. The registers inside the module sequentially record the amplitude values ​​corresponding to each transition node. Specifically, the impedance amplitude corresponding to the first trough is recorded as 85.2 ohms, and the impedance amplitude corresponding to the first peak is recorded as 112.4 ohms. This module constructs data points by combining the accumulated count value within the sampling clock cycle. The sampling clock frequency inside the system is fixed at 50 Hz, and the accumulated count value is an integer sequence that starts from 0 and increments by 1 with each clock cycle. This module multiplies the accumulated count value by 20 milliseconds to obtain an absolute timestamp parameter, and binds this absolute timestamp parameter to the corresponding physical impedance value to form a data point containing both time and impedance attributes. All generated data points are sequentially arranged according to the timestamp parameter order to generate a sequence of impedance products.

[0021] The time series extreme value determination submodule calls the chest and abdominal impedance product sequence, reads the values ​​of each acquisition point, compares the real-time acquisition point values ​​with the values ​​of the adjacent acquisition points, extracts the timestamp parameters of the corresponding acquisition point when the real-time acquisition point value is greater than the values ​​of the adjacent acquisition points, gathers the extracted timestamp parameters in the differentiated time series segments and arranges them in ascending order to establish a set of respiratory rhythm extreme value timestamps. The reading component sequentially reads the values ​​from each acquisition point, specifically the impedance values ​​in ohms obtained from the previous processing. The internal comparator unit compares the real-time acquisition point values ​​with those of the preceding and following acquisition points. The comparison logic involves subtracting the previous and following acquisition point values ​​from the real-time acquisition point value. If the difference between the two subtractions is greater than 0, the current comparison state is considered greater than. This module extracts the timestamp parameter of the corresponding acquisition point when the real-time acquisition point value is greater than the values ​​of the preceding and following acquisition points. For example, the impedance value of the 150th acquisition point is 120.3 ohms, the value of its preceding point (the 149th point) is 118.5 ohms, and the value of its following point (the 151st point) is 119.1 ohms. Performing the above comparison operation reveals that 120.3 is greater than both 118.5 and 119.1, so the timestamp parameter of the 150th acquisition point is extracted, such as a timestamp of 3000 milliseconds. The internal data aggregation unit gathers and sorts the extracted timestamp parameters within the differentiated time segments in ascending order. This unit divides the entire Tai Chi practice cycle into multiple differentiated time segments of 10,000 milliseconds each. Within each segment, it collects all timestamp parameters identified as extreme values ​​and uses a bubble sort algorithm to sort these timestamp parameters in ascending order of value. After sorting, these ordered timestamp parameters are stored in a separate memory block, creating a set of extreme timestamps for the breathing rhythm.

[0022] Specifically, such as Figure 2 , 4 As shown, the coordinate synchronization module includes: The pressure coordinate alignment submodule calls the set of extreme timestamps of respiratory rhythm, reads the time section parameters corresponding to the timestamps, extracts the array pressure values ​​output by the foot contact surface pressure detection unit under the time section, and simultaneously extracts the two-dimensional coordinate values ​​of the sensors built into the corresponding array nodes. It performs timestamp alignment and merging processing on the array pressure values ​​and the sensor two-dimensional coordinate values, combines the pressure and coordinate correlation items under the same time section, and establishes the foot space pressure mapping matrix. A data request command is sent to the smart pressure-sensitive insole located under the tester's feet to extract the array pressure values ​​output by the foot contact surface pressure detection unit at a given time point. The smart pressure-sensitive insole contains 256 pressure-sensitive thin-film sensors arranged in 16 rows by 16 columns. The array pressure values ​​are represented as a pressure matrix containing 256 elements in kilopascals. For example, at a time point of 3000 milliseconds, the module extracts a pressure value of 25.4 kilopascals from the sensor in the first row and first column. The positioning configuration unit within the module synchronously extracts the two-dimensional coordinate values ​​of the corresponding array node's built-in sensor. Each pressure-sensitive thin-film sensor is calibrated at the factory with physical coordinates relative to the insole's geometric center, in millimeters. For example, the two-dimensional coordinate values ​​of the sensor in the first row and first column are -45 millimeters on the x-axis and +120 millimeters on the y-axis. The synchronization fusion component performs timestamp alignment and merging processing on the array pressure values ​​and the sensor two-dimensional coordinate values. This component verifies the received timestamp of the pressure data packet against the selected time segment parameter. If the absolute value of the difference between the two is less than 5 milliseconds, the alignment is considered successful. It combines the pressure and coordinate correlation items at the same time segment. Specifically, it binds the 25.4 kPa pressure value of each sensor with its own x-coordinate of -45 mm and y-coordinate of +120 mm into a composite data structure containing three elements. This module stores 256 composite data structures in a multidimensional array to establish a foot space pressure mapping matrix.

[0023] The surface source data conversion submodule extracts array pressure values ​​and sensor two-dimensional coordinate values ​​based on the plantar space pressure mapping matrix. It multiplies the array pressure value by the area of ​​the sensor detection unit to obtain the force value. Then, it performs an algebraic multiplication operation on the force value and the sensor two-dimensional coordinate value to obtain the nodal torque parameters. It performs cumulative summation on the nodal torque parameters to extract the total torque value. It divides the total torque value by the array pressure value and performs a normalized cumulative division operation to generate the plantar pressure center vector. Data is read through the internal data interface based on the plantar space pressure mapping matrix. The parsing unit of this module extracts the array pressure value and the sensor's two-dimensional coordinate value one by one. The internal multiplier array multiplies the array pressure value by the area of ​​the sensor detection unit to obtain the force value, and then performs an algebraic multiplication operation on the force value and the sensor's two-dimensional coordinate value to obtain the nodal torque parameter. This algebraic multiplication operation includes independent operations in two dimensions: horizontal and vertical. Specifically, the pressure value of a node is multiplied by its horizontal coordinate value to obtain the horizontal torque parameter, and the pressure value is multiplied by its vertical coordinate value to obtain the vertical torque parameter. For example, if the array pressure value of a node is 30 kPa, its horizontal coordinate is 20 mm, and its vertical coordinate is 50 mm, the module multiplies 30 by 20 to obtain the horizontal torque parameter of 600 kPa / mm², and multiplies 30 by 50 to obtain the vertical torque parameter of 1500 kPa / mm². The internal accumulator component performs an accumulation and summation process on the nodal torque parameters to extract the total torque value. This component sums the lateral moment parameters corresponding to each of the 256 nodes to obtain the total lateral moment value, and simultaneously sums the longitudinal moment parameters corresponding to each of the 256 nodes to obtain the total longitudinal moment value. For example, the sum of the lateral moment parameters of the 256 nodes is 125,000 kPa / mm², and the sum of the longitudinal moment parameters is 280,000 kPa / mm². This module also sums the array pressure values ​​of the 256 nodes to obtain the total pressure value, for example, a total pressure of 4,000 kPa. The divider unit performs a normalized cumulative division operation by dividing the total moment value by the sum of the array pressure values. Specifically, the logic is to divide the total lateral moment value by the sum of the pressure values ​​to obtain the center horizontal coordinate parameter, and divide the total longitudinal moment value by the sum of the pressure values ​​to obtain the center vertical coordinate parameter. This module divides 125000 by 4000 to obtain a center x-coordinate of 31.25 mm, and divides 280000 by 4000 to obtain a center y-coordinate of 70 mm. Combining the calculated center x-coordinate of 31.25 mm and center y-coordinate of 70 mm generates the center vector of plantar pressure.

[0024] Specifically, such as Figure 2 , 5 As shown, the rate positioning module includes: The coordinate difference calculation submodule calls the plantar pressure center vector, extracts the two-dimensional coordinate parameters of adjacent time sections, subtracts the coordinate parameters of the next section from the coordinate parameters of the previous section to extract the coordinate difference, performs quadratic calculation on the horizontal and vertical components of the coordinate difference respectively, integrates the two square values ​​and performs an addition operation to extract the sum of squares parameter, performs a square root calculation on the sum of squares parameter to obtain the displacement scalar value, arranges the displacement scalar values ​​in order to obtain the time-series displacement scalar set; The module extracts two-dimensional coordinate parameters from adjacent time sections. Specifically, it extracts the plantar pressure center vectors corresponding to the previous time section and the immediately following time section in chronological order of timestamps. A subtraction operator subtracts the coordinate parameters of the previous and subsequent time sections to extract the coordinate difference. This module subtracts the x-coordinate of the previous section from the x-coordinate of the subsequent section to obtain the lateral difference, and subtracts the y-coordinate of the previous section from the y-coordinate of the subsequent section to obtain the lateral difference. For example, if the x-coordinate of the previous section is 31.25 mm and the y-coordinate is 70 mm, and the x-coordinate of the subsequent section is 35.25 mm and the y-coordinate is 73 mm, this module subtracts 31.25 from 35.25 to obtain a lateral difference of 4 mm and subtracts 70 from 73 to obtain a lateral difference of 3 mm. The exponentiation unit performs quadratic calculations on the x- and y-components of the coordinate difference. This module multiplies the lateral difference by itself once and the lateral difference by itself once. Using a calculation example, this module multiplies 4 by 4 to obtain a horizontal square value of 16 square millimeters, and multiplies 3 by 3 to obtain a vertical square value of 9 square millimeters. The module's internal adder integrates these two square values ​​and performs an addition operation to extract the sum of squares. The module then sums the previously calculated 16 and 9, resulting in a sum of squares of 25 square millimeters. The module's floating-point unit performs a square root calculation on the sum of squares to obtain the displacement scalar value. The module then performs an arithmetic square root operation on 25, yielding a displacement scalar value of 5 millimeters. The module's arrangement component sequentially arranges the displacement scalar values, storing the calculated displacement scalar values ​​between adjacent time sections in a one-dimensional array according to the chronological order of their occurrence, thus obtaining a time-series displacement scalar set.

[0025] The moving rate calculation submodule, based on the time-series displacement scalar set, reads the displacement scalar values ​​corresponding to each time section, retrieves the span values ​​between adjacent time sections, divides the displacement scalar values ​​by the time span values ​​to perform a division operation, calculates the pressure center moving rate parameters under different time nodes, and aggregates the pressure center moving rate parameters in chronological order to generate a pressure center moving rate sequence. The module synchronously retrieves the span value between adjacent time segments. This span value is obtained by subtracting the timestamp parameter of the previous time segment from the timestamp parameter of the subsequent time segment. For example, if the subsequent timestamp is 3050 milliseconds and the previous timestamp is 3000 milliseconds, the module obtains a time span value of 50 milliseconds through subtraction. The division unit within this module performs a division operation by dividing the displacement scalar value by the time span value. Specifically, the logic is to divide the read displacement value in millimeters by the time value in milliseconds. Taking a specific example, the module divides 5 by 50, performing algebraic division to calculate the pressure center movement rate parameter at different time nodes. The calculated result is 0.1 mm per millisecond. To standardize the unit of measurement, the module converts it to 100 mm per second, which is the pressure center movement rate parameter at the current time node. The data assembly component within this module aggregates the pressure center movement rate parameters in chronological order. This component combines all the movement rate parameters generated during the entire Tai Chi movement with their corresponding timestamp parameters into a key-value pair structure, and stores them in consecutive memory address blocks in ascending order of timestamps starting from 0, generating a pressure center movement rate sequence.

[0026] The center of gravity extreme value filtering submodule extracts the real-time node pressure center movement rate parameter for the pressure center movement rate sequence, performs a numerical comparison operation between the real-time node movement rate parameter and the rate parameter of the adjacent node, filters the time stamp data when the real-time node rate parameter shows the extreme value state, integrates the filtered time stamp data in the differentiated time series, performs ascending sorting, and obtains the center of gravity transformation extreme value time stamp sequence. The module performs a numerical comparison operation between the real-time node's movement speed parameter and the speed parameters of its immediate and adjacent nodes. This comparison includes not only comparing the magnitude of the values ​​but also identifying the trend of speed changes. The module subtracts the speed value of the preceding node from the speed value of the real-time node to obtain the forward difference value, and subtracts the speed value of the real-time node from the speed value of the following node to obtain the backward difference value. Timestamp data is attached when the real-time node's speed parameter exhibits an extreme value. The criterion for an extreme value is that the forward difference value is greater than 0 and the backward difference value is less than 0. This indicates that the speed has changed from increasing to decreasing, i.e., exhibiting a local maximum value, corresponding to the momentum extreme value at the moment of transition between empty and full steps in Tai Chi. For example, if the real-time node's speed is 150 mm / s, the preceding node's is 120 mm / s, and the following node's is 110 mm / s, the forward difference is 30 (greater than 0), and the backward difference is -40 (less than 0), satisfying the extreme value criterion, the module immediately extracts the timestamp data, for example, 4500 milliseconds, attached to the real-time node. The recombination unit within this module integrates and sorts the timestamp data selected within the differentiated time series segments in ascending order. This module uses a quicksort algorithm to rearrange all extreme timestamp data selected from different differentiated time series segments such as Qi Shi, Lan Que Wei, and Dan Bian, in ascending order of values ​​to obtain the extreme timestamp sequence of center-of-gravity transition.

[0027] Specifically, such as Figure 2 , 6 As shown, the time comparison module includes: The phase extraction submodule calls the extreme timestamp sequence of center of gravity transformation, reads the attached time node parameters, extracts the center of gravity transformation timestamp corresponding to the moment when the Tai Chi body movement transformation occurs, and synchronously calls the breathing rhythm timestamps in the same time period within the extreme timestamp set of breathing rhythm. It performs time sequence alignment processing on the center of gravity transformation timestamp and the breathing rhythm timestamp, combines the time sequence correlation items of the two, and establishes a body movement breathing time sequence mapping matrix. The module reads the attached time node parameter, which is the specific millisecond scale at which the lower limb center of gravity shift reaches its peak state during Tai Chi practice, and extracts the corresponding center of gravity shift timestamp at the moment of the Tai Chi body movement transition. For example, the module extracts the first center of gravity shift timestamp representing the transition between the empty and full steps from the sequence, which is 4500 milliseconds. Simultaneously, it retrieves respiratory rhythm timestamps within the same time period from the aforementioned cached set of extreme respiratory rhythm timestamps. This module sets a search window centered on the center of gravity shift timestamp, extending 1000 milliseconds before and after it, and searches the set of extreme respiratory rhythm timestamps for respiratory peaks or troughs falling within this window. For example, within the window from 3500 milliseconds to 5500 milliseconds, it retrieves a respiratory rhythm timestamp representing the end of exhalation, which is 4800 milliseconds. The alignment unit within this module performs time-series alignment processing on the center of gravity shift timestamp and the respiratory rhythm timestamps. The specific alignment logic involves determining whether the two timestamps belong to the same standard Tai Chi movement cycle (e.g., within the cycle of the right Wild Horse Parts its Mane movement). Once confirmed to belong to the same cycle, the temporal correlation items of the two are combined. This module merges the center-of-gravity shift timestamp of 4500 milliseconds and the breathing rhythm timestamp of 4800 milliseconds into a one-dimensional tuple structure containing two independent time dimensions. All such correlation tuples in this exercise are stored in a dedicated data table according to the chronological order of the movements, establishing a body movement and breathing temporal mapping matrix.

[0028] The time phase difference calculation submodule, based on the body breathing time sequence mapping matrix, extracts the center of gravity conversion timestamp and breathing rhythm timestamp under the same matching position relationship, performs a numerical subtraction calculation operation on the center of gravity conversion timestamp and breathing rhythm timestamp to obtain the time phase difference value corresponding to the body movement conversion and chest and abdominal undulation breathing, and aggregates the time phase difference values ​​in chronological order to obtain the time phase difference value set. The module extracts the center-of-gravity transition timestamp and respiratory rhythm timestamp under the same matching alignment. The same matching alignment refers to two corresponding parameters encapsulated within the same one-dimensional tuple structure. For example, the module extracts a center-of-gravity transition timestamp of 4500 milliseconds and a respiratory rhythm timestamp of 4800 milliseconds from a matching alignment. The module's internal subtraction logic processor performs a numerical subtraction operation between the center-of-gravity transition timestamp and the respiratory rhythm timestamp. Specifically, the module forces the subtraction of the center-of-gravity transition timestamp using the respiratory rhythm timestamp to preserve the sign attribute of the time difference. This sign attribute characterizes whether the breathing action precedes or lags the body movement. Using a practical example, the module subtracts 4500 from 4800, performing a direct algebraic subtraction operation. This subtraction operation yields the corresponding time phase difference between the body movement transition and the chest and abdominal breathing pattern. Subtracting 4500 from 4800 yields 300 milliseconds. This 300 milliseconds represents the phase difference value at that specific action node. A positive value indicates that the breathing rhythm lags behind the center of gravity shift by 300 milliseconds. The queue generation unit within this module aggregates the phase difference values ​​in chronological order. This module collects dozens of phase difference values ​​involved in the entire Tai Chi movement, storing them in a linear data buffer to obtain a set of phase difference values.

[0029] The collaborative feature comparison submodule reads the time difference parameters corresponding to the time cross section for the set of time difference values, retrieves the Tai Chi body coordination benchmark values ​​in the pre-entered storage area, performs an interval comparison operation between the time difference parameters under the time cross section and the Tai Chi body coordination benchmark values, marks the time nodes that are within or deviate from the benchmark value interval, aggregates all time nodes and comparison status calibration items, and obtains the body breathing coordination feature sequence. The 300-millisecond time difference parameter obtained in the previous step is extracted, and the pre-entered Tai Chi body coordination benchmark value is retrieved from the database query interface. The process of setting this Tai Chi body coordination benchmark value is as follows: The system collected standard practice data from 50 national-level intangible cultural heritage Tai Chi inheritors in the early stages. The arithmetic mean of the time difference data for exhalation and force exertion movements was calculated, yielding a standard time difference mean of 150 milliseconds. Combined with the standard deviation of a normal distribution, a tolerance limit of 100 milliseconds was set, ultimately establishing the Tai Chi body coordination benchmark value within the range of 50 to 250 milliseconds. An internal interval comparator performs an interval comparison operation between the time difference parameter at the time section and the Tai Chi body coordination benchmark value. This module determines whether the read time difference parameter is greater than or equal to 50 and less than or equal to 250. It then calibrates the time nodes that are within or deviate from the benchmark value range. Taking the previously extracted 300 milliseconds as an example, this module, through comparison, finds that 300 is greater than 250, determining that it does not fall within the baseline range. Therefore, it assigns a deviation from the baseline calibration item with a value of 0 for this time node. If the phase difference parameter of another time node is 200 milliseconds, falling within the 50 to 250 range, then it assigns a calibration item within the baseline with a value of 1. The fusion generation unit within this module aggregates all time nodes and comparison status calibration items. This unit pairs and associates hundreds of time node identifiers with their corresponding 0 or 1 calibration items one by one, storing them in an attribute list to obtain the body movement and breathing coordination feature sequence. Table 1 lists some of the coordination comparison result data extracted in the embodiment.

[0030] Table 1: Data Table of Partial Feature Comparison Results ; As shown in Table 1, by comparing the phase difference values ​​at different time points, the system clearly defines the coordination status of each action node.

[0031] Specifically, such as Figure 2 , 7 As shown, the quantitative assessment module includes: The frequency statistics submodule calls the body breathing coordination feature sequence, reads the corresponding comparison status calibration items at each time node, retrieves the corresponding calibration parameters within the Tai Chi body coordination benchmark value range, performs an equivalence matching check operation on the calibration items and calibration parameters at each time node, filters the time segments that meet the equivalence matching conditions, performs an accumulation counting operation on the filtered time segments to extract the occurrence frequency parameters, and obtains the coordination standard achievement frequency value. The system reads the corresponding calibration items for each time node, that is, it reads the calibration status values ​​of 0 or 1 in the sequence one by one. Simultaneously, it retrieves the corresponding calibration parameters within the range of the Tai Chi body coordination benchmark values. These calibration parameters are the predefined standard judgment marks representing qualified movements and proper breathing coordination, specifically set to a value of 1. The internal logic matching unit performs an equivalence matching check operation on the calibration items and calibration parameters for each time node. This module checks whether each state calibration item read in the sequence is completely equal to the set value of 1. It then filters the time segments that meet the equivalence matching conditions. When a calibration item of 1 is detected for a time node, the module immediately extracts that time node from the sequence and stores it in an independent qualified node cache pool. The internal counter unit performs an accumulation counting operation on the selected time segments to extract the occurrence frequency parameter. The specific logic of the accumulation counting operation is as follows: the counter register is initially set to 0, and the counter register value automatically increases by 1 for each qualified time node data stored in the qualified node cache pool. For example, in a 5-minute Tai Chi performance, the system scanned and selected 85 time segments that met the calibration condition of 1. The counter incremented continuously from 0 85 times, resulting in a final count value of 85. The final value of the counter was output to obtain the frequency of achieving the collaborative standard.

[0032] The coupling degree calculation submodule, based on the frequency value of collaborative achievement, reads the corresponding occurrence frequency parameter extracted by the cumulative counting operation, synchronously retrieves the corresponding total number of movements in the real-time Tai Chi training cycle, divides the occurrence frequency parameter by the total number of movements to perform a division algebra calculation operation, extracts the proportional quantitative parameter corresponding to the physiological force exertion between breathing and limb movement, and aggregates the proportional quantitative parameter in chronological order to establish the physiological force exertion coupling degree value; The system synchronously retrieves the total number of movements within the real-time Tai Chi training cycle. This total number of movements is pre-calculated by the system's internal movement segmentation identifier based on the number of foot pressure transfer peaks. For example, if the system identifies that the 24-form simplified Tai Chi practice includes 100 independent empty-full footwork transition movements, then the total number of movements is set to 100. An internal floating-point divider performs algebraic division by dividing the frequency parameter by the total number of movements. Specifically, the calculation logic uses 85, representing the number of times the target was achieved, as the dividend, and 100, representing the total number of movements, as the divisor. This division calculation extracts the proportional quantification parameter corresponding to the physiological force exertion between breathing and limb movement. Dividing 85 by 100 yields 0.85, which is directly used as the proportional quantification parameter, representing the success rate of the practitioner's body movement and breathing coordination. The aggregation unit within this module aggregates the proportional quantification parameters sequentially according to time. The system combines the proportional quantization parameters of multiple training cycles with the date encoding of the training day in the same data stream to establish a physiological force coupling degree value.

[0033] The health preservation level assessment submodule reads the proportional quantification parameter corresponding to the physiological exertion coupling degree value, retrieves the preset health preservation threshold, performs an interval boundary comparison operation between the proportional quantification parameter and the preset health preservation threshold, determines the segment of the preset health preservation threshold interval in which the proportional quantification parameter falls, assigns the corresponding quantitative assessment label according to the corresponding health preservation threshold interval segment, and sequentially aggregates the assigned quantitative assessment labels to generate a health preservation effect monitoring assessment level. The system retrieves preset health and wellness thresholds. These thresholds are based on a two-year follow-up study of 1000 elderly people in a community who regularly practice Tai Chi. The improvement rates of their immune indicators were statistically analyzed using a scatter plot, and three key threshold demarcation points were established: 0.60, 0.75, and 0.90, thus dividing the system into four preset health and wellness threshold intervals. An internal logic comparator performs interval boundary comparison operations between the proportional quantification parameters and the preset health and wellness thresholds. This module compares 0.85 with 0.60, 0.75, and 0.90 respectively, determining which interval the proportional quantification parameter falls into. The comparison shows that 0.85 is greater than 0.75 and less than 0.90; therefore, the system determines that the proportional quantification parameter falls into the third interval, between 0.75 and 0.90. A corresponding quantitative assessment label is then assigned based on the health and wellness threshold interval. The system's built-in tag dictionary specifies that scores below 0.60 are assigned an inefficient exercise tag, 0.60 to 0.75 a basic strengthening tag, 0.75 to 0.90 a smooth blood and qi flow tag, and scores above 0.90 a full internal qi flow tag. For scores falling into the third range of 0.85, the module retrieves and assigns a smooth blood and qi flow tag. The assigned quantitative assessment tags are then sequentially aggregated, and the smooth blood and qi flow tags generated in this training session are sequentially concatenated with tag records in the historical archive to generate a health maintenance effect monitoring and assessment level. The advantage of this calculation logic is that by mapping abstract proportional data to intuitive text tags, it greatly reduces the barrier to understanding professional impedance breathing data for the tested users. Table 2 lists the reference data for the health maintenance level assessment set by the system.

[0034] Table 2: Reference Data Table for Health Preservation Level Assessment ; As shown in Table 2, the system directly converts the calculated proportional quantification parameters into specific label levels to guide the health benefits of Tai Chi practitioners by setting a fixed threshold range.

[0035] Please see Figure 8 The intelligent Tai Chi training and health preservation effect monitoring method is based on the aforementioned intelligent Tai Chi training and health preservation effect monitoring system and includes the following steps: S1: Collect the output voltage signal of the Tai Chi chest and abdomen impedance measurement circuit, multiply the output voltage signal with the set impedance conversion constant, extract the timestamp when the product value is greater than the adjacent collection point, and generate a set of respiratory rhythm extreme value timestamps. S2: Based on the set of extreme timestamps of respiratory rhythm, obtain the array pressure value and the sensor two-dimensional coordinate value. Multiply the array pressure value by the area of ​​the sensor detection unit to obtain the force value. Then multiply the force value by the sensor two-dimensional coordinate value and perform normalized cumulative division operation to obtain the center vector of plantar pressure. S3: Based on the plantar pressure center vector, extract the coordinate difference between adjacent time sections, perform quadratic addition and square root operation, divide the calculation result by the time span to obtain the pressure center movement rate value, and generate the center of gravity transformation extreme value timestamp sequence. S4: Using the extreme timestamp sequence of center of gravity shift, extract the timestamp of center of gravity shift and the timestamp of breathing rhythm. Subtract the timestamp of center of gravity shift from the timestamp of breathing rhythm to obtain the phase difference value. Compare the phase difference value with the benchmark value of Tai Chi body coordination to construct a body breathing coordination feature sequence. S5: Call the body movement and breathing coordination feature sequence, count the frequency of occurrences where the time difference value is within the range of the Tai Chi body movement coordination benchmark value, divide the occurrence frequency by the total number of Tai Chi movements to calculate the physiological force coupling degree value, and compare it with the preset health and wellness threshold to generate a health and wellness effect monitoring and evaluation level.

[0036] The above description is merely a specific embodiment of the present invention, but 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 technical scope 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 protection of the described technical solutions.

Claims

1. An intelligent Tai Chi training and health preservation effect monitoring system, characterized in that, The system includes: The impedance acquisition module acquires the output voltage signal of the Tai Chi chest and abdomen impedance measurement circuit, multiplies the output voltage signal with the set impedance conversion constant, extracts the timestamp when the product value is greater than the adjacent acquisition point, and generates a set of respiratory rhythm extreme value timestamps. The coordinate synchronization module obtains the array pressure value and the sensor two-dimensional coordinate value according to the set of extreme timestamps of the respiratory rhythm. It multiplies the array pressure value by the area of ​​the sensor detection unit to obtain the force value, then multiplies the force value by the sensor two-dimensional coordinate value and performs normalized cumulative division operation to obtain the center vector of plantar pressure. The rate positioning module extracts the coordinate difference between adjacent time sections based on the plantar pressure center vector, performs quadratic addition and square root operation, divides the calculation result by the time span to obtain the pressure center movement rate value, and generates a time stamp sequence of extreme values ​​of the center of gravity transformation. The time comparison module uses the extreme timestamp sequence of center of gravity transformation to extract the timestamp of center of gravity transformation and the timestamp of breathing rhythm. It performs a subtraction operation between the timestamp of center of gravity transformation and the timestamp of breathing rhythm to obtain the time difference value. The time difference value is compared with the benchmark value of Tai Chi body coordination to construct a body breathing coordination feature sequence. The quantitative assessment module calls the body movement and breathing coordination feature sequence, counts the frequency of occurrences where the time difference value is within the range of the Tai Chi body movement coordination benchmark value, divides the occurrence frequency by the total number of Tai Chi movements to calculate the physiological force coupling degree value, and compares it with the preset health and wellness threshold to generate a health and wellness effect monitoring and evaluation level.

2. The intelligent Tai Chi training and health preservation effect monitoring system according to claim 1, characterized in that, The set of extreme timestamps for respiratory rhythm includes the moment of maximum inhalation, the moment of maximum exhalation, and the transient point of breath-holding transition. The center vector of plantar pressure includes the lateral displacement vector, the longitudinal load-bearing vector, and the directional deflection angle. The sequence of extreme timestamps for center of gravity transition includes the critical moment of alternation between emptiness and fullness, the peak moment of dynamic force exertion, and the trough moment of recovery. The sequence of body movement and breathing coordination characteristics includes the gas rhythm delay step length, the resonance index of form and qi frequency, and the deviation amplitude of breathing movements. The health preservation effect monitoring and evaluation levels include the advanced level of deep guidance, the standard level of regular stretching, and the level of qi stagnation awaiting further advancement.

3. The intelligent Tai Chi training and health preservation effect monitoring system according to claim 1, characterized in that, The impedance acquisition module includes: The voltage signal conversion submodule acquires the output voltage signal of the Tai Chi chest and abdomen impedance measurement circuit, retrieves the set impedance conversion constant, performs a multiplication operation between the output voltage signal and the set impedance conversion constant, extracts the peak and trough conversion nodes in the output value of the multiplication operation, records the amplitude value corresponding to each conversion node, and constructs data points by combining the accumulated count value within the sampling clock cycle. The data points are arranged in sequence to generate a chest and abdomen impedance product sequence. The time-series extreme value determination submodule calls the chest and abdominal impedance product sequence, reads the values ​​of each acquisition point, compares the real-time acquisition point values ​​with the values ​​of adjacent acquisition points, extracts the timestamp parameters of the corresponding acquisition point when the real-time acquisition point value is greater than the values ​​of adjacent acquisition points, aggregates the extracted timestamp parameters within the differentiated time series and arranges them in ascending order to establish a respiratory rhythm extreme value timestamp set.

4. The intelligent Tai Chi training and health preservation effect monitoring system according to claim 3, characterized in that, The coordinate synchronization module includes: The pressure coordinate alignment submodule calls the set of extreme timestamps of the respiratory rhythm, reads the time section parameters corresponding to the timestamps, extracts the array pressure values ​​output by the foot contact surface pressure detection unit under the time section, and simultaneously extracts the two-dimensional coordinate values ​​of the sensors built into the corresponding array nodes. It performs timestamp alignment and merging processing on the array pressure values ​​and the sensor two-dimensional coordinate values, combines the pressure and coordinate correlation items under the same time section, and establishes the foot space pressure mapping matrix. The surface source data conversion submodule extracts the array pressure value and sensor two-dimensional coordinate value based on the foot space pressure mapping matrix. It multiplies the array pressure value by the area of ​​the sensor detection unit to obtain the force value, and then performs an algebraic multiplication operation on the force value and the sensor two-dimensional coordinate value to obtain the nodal torque parameter. It performs cumulative summation on the nodal torque parameter to extract the total torque value, and performs normalized cumulative division operation by dividing the total torque value by the array pressure value to generate the foot pressure center vector.

5. The intelligent Tai Chi training and health preservation effect monitoring system according to claim 4, characterized in that, The rate positioning module includes: The coordinate difference calculation submodule calls the plantar pressure center vector, extracts the two-dimensional coordinate parameters of adjacent time sections, subtracts the coordinate parameters of the next section from the coordinate parameters of the previous section to extract the coordinate difference, performs quadratic calculation on the horizontal and vertical components of the coordinate difference respectively, integrates the two square values ​​and performs an addition operation to extract the sum of squares parameter, performs a square root calculation on the sum of squares parameter to obtain the displacement scalar value, and arranges the displacement scalar values ​​in order to obtain the time-series displacement scalar set; The moving rate calculation submodule reads the displacement scalar values ​​corresponding to each time section based on the time-series displacement scalar set, retrieves the span values ​​between adjacent time sections, divides the displacement scalar values ​​by the time span values ​​to perform a division operation, calculates the pressure center moving rate parameters under different time nodes, and aggregates the pressure center moving rate parameters in chronological order to generate a pressure center moving rate sequence. The center of gravity extreme value filtering submodule extracts the real-time node pressure center movement rate parameters for the pressure center movement rate sequence, performs a numerical comparison operation between the real-time node movement rate parameters and the rate parameters of the adjacent nodes, filters the time stamp data when the real-time node rate parameters show extreme value states, integrates the filtered time stamp data in the differentiated time series and performs ascending sorting to obtain the center of gravity transformation extreme value time stamp sequence.

6. The intelligent Tai Chi training and health preservation effect monitoring system according to claim 5, characterized in that, Compare the real-time node movement rate parameters with the rate parameters of adjacent nodes, including the rate parameters of the preceding node and the rate parameters of the following node. When the real-time node movement rate parameter is determined to be greater than the preceding node's rate parameter and greater than the following node's rate parameter, the real-time node movement rate parameter is defined as a local maximum extreme value state. When the real-time node movement rate parameter is determined to be less than the preceding node's rate parameter and less than the following node's rate parameter, the real-time node movement rate parameter is defined as a local minimum extreme value state. Separate the high-order extreme timestamp data corresponding to the real-time node movement rate parameter when it is in a local maximum extreme state, separate the low-order extreme timestamp data corresponding to the real-time node movement rate parameter when it is in a local minimum extreme state, and summarize the high-order extreme timestamp data and the low-order extreme timestamp data to construct the attached timestamp data.

7. The intelligent Tai Chi training and health preservation effect monitoring system according to claim 5, characterized in that, The time comparison module includes: The phase extraction submodule calls the extreme timestamp sequence of the center of gravity transformation, reads the attached time node parameters, extracts the center of gravity transformation timestamp corresponding to the moment when the Tai Chi body movement transformation occurs, and synchronously retrieves the breathing rhythm timestamps in the same time period within the extreme timestamp set of breathing rhythm. It performs time sequence alignment processing on the center of gravity transformation timestamp and the breathing rhythm timestamp, combines the time sequence correlation items of the two, and establishes a body movement breathing time sequence mapping matrix. The time phase difference calculation submodule, based on the body breathing time sequence mapping matrix, extracts the center of gravity conversion timestamp and breathing rhythm timestamp under the same matching alignment relationship, performs a numerical subtraction calculation operation on the center of gravity conversion timestamp and breathing rhythm timestamp, obtains the time phase difference value corresponding to the body movement conversion and chest and abdominal undulation breathing, and aggregates the time phase difference values ​​in chronological order to obtain a time phase difference value set. The collaborative feature comparison submodule reads the time difference parameters corresponding to the time cross section for the set of time difference values, retrieves the Tai Chi body coordination benchmark values ​​in the pre-recorded storage area, performs an interval comparison operation between the time difference parameters under the time cross section and the Tai Chi body coordination benchmark values, marks the time nodes that are within or deviate from the benchmark value interval, aggregates all time nodes and comparison status calibration items, and obtains the body breathing coordination feature sequence.

8. The intelligent Tai Chi training and health preservation effect monitoring system according to claim 7, characterized in that, Extract the lower and upper limits of the Tai Chi body coordination reference values, and compare the time phase difference parameters under the time section with the lower and upper limits of the reference values ​​respectively. When the phase difference parameter under the time section is greater than or equal to the lower limit of the reference value and less than or equal to the upper limit of the reference value, the phase difference parameter under the time section is defined as being within the reference value range. For time nodes within the reference value range, a collaborative matching calibration value is assigned. When the phase difference parameter under the time section is determined to be less than the lower limit of the reference value or greater than the upper limit of the reference value, the phase difference parameter under the time section is defined to deviate from the reference value range. For time nodes that deviate from the reference value range, a collaborative deviation calibration value is assigned. The collaborative matching calibration value and the collaborative deviation calibration value are aggregated to construct a comparison status calibration item.

9. The intelligent Tai Chi training and health preservation effect monitoring system according to claim 7, characterized in that, The quantitative evaluation module includes: The frequency statistics submodule calls the body breathing coordination feature sequence, reads the corresponding comparison status calibration items at each time node, retrieves the corresponding calibration parameters within the Tai Chi body coordination benchmark value range, performs an equivalence matching check operation on the calibration items at each time node and the calibration parameters, filters the time segments that meet the equivalence matching conditions, performs an accumulation counting operation on the filtered time segments to extract the occurrence frequency parameters, and obtains the coordination standard achievement frequency value. The coupling degree calculation submodule, based on the frequency value of the collaborative achievement, reads the corresponding occurrence frequency parameter extracted by the cumulative counting operation, synchronously retrieves the corresponding total number of movements in the real-time Tai Chi training cycle, divides the occurrence frequency parameter by the total number of movements to perform a division algebra calculation operation, extracts the proportional quantitative parameter corresponding to the physiological force exertion between breathing and limb movement, and aggregates the proportional quantitative parameter in chronological order to establish the physiological force exertion coupling degree value; The health preservation level assessment submodule reads the proportional quantification parameter corresponding to the physiological exertion coupling degree value, retrieves the preset health preservation threshold, performs an interval boundary comparison operation between the proportional quantification parameter and the preset health preservation threshold, determines the segment of the preset health preservation threshold interval in which the proportional quantification parameter falls, assigns the corresponding quantitative assessment label according to the corresponding health preservation threshold interval segment, and sequentially aggregates the assigned quantitative assessment labels to generate a health preservation effect monitoring assessment level.

10. An intelligent method for monitoring the training and health benefits of Tai Chi, characterized in that, The intelligent Tai Chi training and health preservation effect monitoring system according to any one of claims 1-9 includes the following steps: S1: Collect the output voltage signal of the Tai Chi chest and abdomen impedance measurement circuit, multiply the output voltage signal with the set impedance conversion constant, extract the timestamp when the product value is greater than the adjacent collection point, and generate a set of respiratory rhythm extreme value timestamps. S2: Based on the set of extreme timestamps of the respiratory rhythm, obtain the array pressure value and the sensor two-dimensional coordinate value, multiply the array pressure value by the area of ​​the sensor detection unit to obtain the force value, multiply the force value by the sensor two-dimensional coordinate value, and perform normalized cumulative division operation to obtain the plantar pressure center vector. S3: Based on the plantar pressure center vector, extract the coordinate difference between adjacent time sections, perform quadratic addition and square root operation, divide the calculation result by the time span to obtain the pressure center movement rate value, and generate the center of gravity transformation extreme value timestamp sequence. S4: Using the aforementioned extreme timestamp sequence of center of gravity transformation, extract the timestamp of center of gravity transformation and the timestamp of breathing rhythm, perform a subtraction operation between the timestamp of center of gravity transformation and the timestamp of breathing rhythm to obtain the phase difference value, compare the phase difference value with the Tai Chi body coordination benchmark value, and construct a body breathing coordination feature sequence. S5: Call the body movement and breathing coordination feature sequence, count the frequency of occurrences where the time difference value is within the range of the Tai Chi body movement coordination benchmark value, divide the occurrence frequency by the total number of Tai Chi movements to calculate the physiological force coupling degree value, and compare it with the preset health and wellness threshold to generate a health and wellness effect monitoring and evaluation level.

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