A multifunctional rehabilitation physiotherapy instrument based on combined waveforms and a control method thereof

Abstract

The application provides a multifunctional rehabilitation physiotherapy instrument based on a combined waveform and a control method, the method comprising constructing a combined waveform database, the database storing a plurality of preset physiotherapy modes, any preset physiotherapy mode being a waveform sequence composed of at least two basic waveforms selected from square waves, trapezoidal waves and exponential waves according to preset time sequence parameters, for simulating corresponding Chinese medicine physiotherapy methods; obtaining patient data to determine a preliminary physiotherapy scheme according to the matching of the patient data and the database, and adjusting the parameters of the preliminary physiotherapy scheme according to the patient data through a preset dynamic parameter adjustment model to determine a target physiotherapy scheme; and realizing the waveform output of the target physiotherapy scheme based on a preset mechanism to perform physiotherapy on the patient, wherein the preset waveform adjustment algorithm is used to realize smooth transition of waveforms before and after switching. The application solves the problem of lacking a multifunctional rehabilitation physiotherapy instrument based on a combined waveform and a control method with self-adaptive dynamic adjustment and good treatment effect in the prior art.

Description

Technical Field

[0001] This invention relates to the field of physiotherapy equipment technology, and in particular to a multifunctional rehabilitation physiotherapy equipment and control method based on combined waveforms. Background Technology

[0002] In today's fast-paced society, prolonged desk work and sedentary lifestyles have become the norm for urban dwellers. This has led to a rapid spread of chronic illnesses and sub-health issues such as lower back and shoulder pain, muscle strain, and neurasthenia. These chronic health problems not only severely reduce people's quality of life but also create a huge medical burden. Traditional drug treatments are prone to drug resistance and are accompanied by numerous side effects; for example, nonsteroidal anti-inflammatory drugs (NSAIDs) can damage the gastrointestinal mucosa. Surgical treatments face the risk of perioperative complications, such as anesthetic accidents, and require a long postoperative functional recovery period. Given this challenging medical situation, non-invasive rehabilitation therapy has become a crucial medical intervention. Rehabilitation therapy devices, as core equipment, have become one of the recommended options due to their significant advantages, including the reversibility of neuromodulation mechanisms and low risk of biological tissue damage.

[0003] Currently, the technological basis of mainstream electrostimulation rehabilitation therapy equipment is the generation of specific electrical pulse waveforms through digital frequency synthesis technology. These waveforms are then applied to acupoints or muscle groups via electrodes, utilizing neuroelectrophysiological mechanisms to achieve pain relief and promote blood circulation. The system implementation typically relies on a core circuit built using a microcontroller and a dedicated waveform generation chip to complete basic waveform output and power drive.

[0004] However, current rehabilitation and physiotherapy equipment suffers from significant technical shortcomings: First, the problem of limited waveform output is prominent. Most devices can only generate square waves or sine waves of a fixed frequency. Under continuous electrical stimulation, the human body easily develops neural adaptation, causing the therapeutic effect to diminish with prolonged use. Clinical studies have confirmed that after single-waveform treatment for more than 15 minutes, the nerve excitation threshold can increase by more than 30%, directly affecting the therapeutic effect. Second, the functional integration is insufficient. Most devices only have basic electrical stimulation functions, resulting in limited therapeutic effects. Furthermore, they lack dynamic parameter adjustment mechanisms, making it difficult to provide differentiated treatment plans based on patient tolerance and disease stage. Summary of the Invention

[0005] Therefore, the purpose of this invention is to provide a multifunctional rehabilitation therapy device and control method based on combined waveforms, aiming to solve the problem that there is a lack of a multifunctional rehabilitation therapy device and control method based on combined waveforms with adaptive dynamic adjustment and good therapeutic effect in the prior art.

[0006] According to an embodiment of the present invention, a control method for a multifunctional rehabilitation therapy instrument based on combined waveforms is provided, the method comprising:

[0007] A combined waveform database is constructed, which stores multiple preset physiotherapy modes. Each preset physiotherapy mode is a waveform sequence composed of at least two basic waveforms among square wave, trapezoidal wave and exponential wave according to preset timing parameters, used to simulate corresponding traditional Chinese medicine physiotherapy techniques.

[0008] Acquire patient data, match the patient data with the database to determine a preliminary physiotherapy plan, and adjust the parameters of the preliminary physiotherapy plan according to the patient data through a preset dynamic parameter adjustment model to determine a target physiotherapy plan;

[0009] The target physiotherapy plan is output based on a preset mechanism to provide physiotherapy to the patient. During waveform switching, a preset waveform adjustment algorithm is used to achieve a smooth transition between the previous and next waveforms.

[0010] In addition, the control method for a multifunctional rehabilitation therapy instrument based on combined waveforms according to the above embodiments of the present invention may also have the following additional technical features:

[0011] Furthermore, the steps for constructing the combined waveform database include:

[0012] Collect physical signals of the techniques used in TCM physiotherapy, patient body data, and patient subjective data to construct a multidimensional dataset;

[0013] Based on the multidimensional dataset, the physical signals are preprocessed and features are extracted, and the target-related features are determined through screening.

[0014] Based on the target association features, a gradient boosting tree model is used to construct a mapping model of combined waveform parameters and physiotherapy effects. The mapping model is then trained and optimized to determine waveform rules in order to construct a combined waveform database.

[0015] Furthermore, the patient data includes at least disease condition data and pain score data. The steps of acquiring patient data, matching the patient data with the database to determine a preliminary physiotherapy plan, and adjusting the parameters of the preliminary physiotherapy plan using a preset dynamic parameter adjustment model based on the patient data to determine a target physiotherapy plan include:

[0016] A preliminary physiotherapy plan is determined by matching the patient's condition data and pain score data against the database.

[0017] The correction frequency is determined based on the pain score data, and the correction frequency is combined with the base frequency of the waveform in the preliminary physiotherapy plan to obtain the target frequency;

[0018] Determine whether the damage time in the disease data is greater than a preset time threshold;

[0019] If not, then the basic amplitude of the waveform in the preliminary theoretical scheme is the target amplitude;

[0020] If so, the correction range is determined based on the damage time, and the correction range is combined with the base range to obtain the target range.

[0021] Furthermore, the correction frequency and correction magnitude are determined based on the pain score data and the injury time using a first preset formula and a second preset formula, respectively.

[0022] The first preset formula is:

[0023]

[0024] in, To correct the frequency, Based on the base frequency, To correct the frequency, Rate the pain. This is the frequency correction factor;

[0025] The second preset formula is:

[0026]

[0027] in, To adjust the range, , Basic amplitude, For the limit amplitude, For the time of damage, For the preset time threshold, This is the amplitude correction factor.

[0028] Furthermore, the waveform output of the target physiotherapy plan is realized based on a preset mechanism to perform physiotherapy on the patient. The step of achieving a smooth transition between waveforms during waveform switching using a preset waveform adjustment algorithm includes:

[0029] Based on the target physiotherapy plan, determine the preceding waveform data and the subsequent waveform data, so as to determine the preceding control parameters for physiotherapy to the patient based on the preceding waveform data and determine the subsequent control parameters based on the subsequent waveform data;

[0030] Based on the subsequent waveform data and the preceding waveform data, buffer waveform data within a preset buffer time is determined, and buffer control parameters are determined according to the buffer waveform data.

[0031] The physiotherapy device is controlled to perform physiotherapy on the patient according to the preceding control parameters, the buffer control parameters, the subsequent control parameters, and the preset timing parameters.

[0032] Furthermore, the buffered waveform data is determined by a third preset formula based on the subsequent waveform data and the preceding waveform data;

[0033] The third preset formula is:

[0034]

[0035] in, The amplitude of the preceding waveform. The amplitude of the subsequent waveform. The transition time constant, t For time.

[0036] Furthermore, after the step of implementing the waveform output of the target physiotherapy plan based on a preset mechanism to perform physiotherapy on the patient, the following steps are included:

[0037] Real-time acquisition of patient feedback data to determine discrepancies based on the patient feedback data and patient data;

[0038] Determine whether the ratio of the difference data to the patient data is greater than a preset difference threshold;

[0039] If so, the target theoretical scheme is adjusted based on the difference data by using a preset dynamic parameter adjustment model.

[0040] Another objective of this invention is a multifunctional rehabilitation therapy device based on combined waveforms, used to implement the aforementioned control method for a multifunctional rehabilitation therapy device based on combined waveforms, wherein the therapy device comprises:

[0041] Control unit, used to execute the control method;

[0042] The waveform generation unit is used to generate a corresponding therapeutic wave that matches the traditional Chinese medicine physiotherapy technique based on the target combined waveform data and target treatment parameters output by the control unit.

[0043] A signal amplification unit is used to amplify the signal output by the waveform generation unit to ensure the stimulation intensity.

[0044] The interactive unit is used to acquire patient data and display treatment parameters in real time;

[0045] The wireless transmission unit is used to upload treatment data to the cloud, providing data support for remote diagnosis and treatment and efficacy evaluation.

[0046] Another object of the present invention is a multifunctional rehabilitation therapy instrument control system based on combined waveforms, the system comprising:

[0047] The database construction module is used to construct a combined waveform database. The database stores multiple preset physiotherapy modes. Each preset physiotherapy mode is a waveform sequence composed of at least two basic waveforms among square wave, trapezoidal wave and exponential wave according to preset timing parameters, used to simulate corresponding traditional Chinese medicine physiotherapy techniques.

[0048] The treatment plan determination module is used to acquire patient data, match the patient data with the database to determine a preliminary physiotherapy plan, and adjust the parameters of the preliminary physiotherapy plan according to the patient data through a preset dynamic parameter adjustment model to determine the target physiotherapy plan.

[0049] The physiotherapy module is used to output the waveform of the target physiotherapy plan based on a preset mechanism to perform physiotherapy on the patient. When switching waveforms, a preset waveform adjustment algorithm is used to achieve a smooth transition between the previous and next waveforms.

[0050] Another objective of this invention is to provide a storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described control method for a multifunctional rehabilitation therapy device based on combined waveforms.

[0051] Another objective of this invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the above-described control method for a multifunctional rehabilitation therapy device based on combined waveforms.

[0052] This invention constructs a rich and professional combined waveform database. This database meticulously designs various waveform sequences based on the biomechanical characteristics of traditional Chinese medicine (TCM) physiotherapy techniques. This allows for the automatic adaptation of corresponding waveform sequences to patient data, providing precise, professional treatment services aligned with TCM physiotherapy principles and improving therapeutic efficacy. Furthermore, based on pain scores and condition data from the patient, the parameters of the adapted waveform sequences are dynamically adjusted in multiple dimensions to better match the patient's actual condition, generating personalized combined waveform treatment plans. In addition, transition buffers are set during waveform changes in the waveform sequence, and a preset waveform adjustment algorithm is used to achieve smooth amplitude transitions, effectively controlling waveform distortion during the transition phase. This ensures that the patient does not experience significant abrupt changes in stimulation due to waveform switching during treatment, greatly improving the comfort and stability of the treatment process. Therefore, this invention solves the problem in the prior art of lacking a multifunctional rehabilitation physiotherapy instrument and control method based on combined waveforms that offers adaptive dynamic adjustment and good therapeutic effects. Attached Figure Description

[0053] Figure 1 This is a flowchart of the control method for a multifunctional rehabilitation therapy instrument based on combined waveforms in the first embodiment of the present invention;

[0054] Figure 2 This is a schematic diagram of the result of the multifunctional rehabilitation therapy instrument control system based on combined waveforms in the third embodiment of the present invention;

[0055] Figure 3 This is a schematic diagram of the structure of the electronic device in the fourth embodiment of the present invention;

[0056] Figure 4 This is a hardware architecture diagram according to the second embodiment of the present invention;

[0057] Figure 5 This is a schematic diagram of the digital waveform output circuit in the second embodiment of the present invention;

[0058] Figure 6 This is a diagram of the rectifier and voltage regulator circuit in the second embodiment of the present invention;

[0059] Figure 7 This is a power amplifier circuit diagram according to the second embodiment of the present invention;

[0060] Figure 8 This is a design diagram of the communication protocol stack in the second embodiment of the present invention;

[0061] Figure 9 This is a system interface design diagram according to the second embodiment of the present invention;

[0062] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0063] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0064] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0065] Example 1

[0066] Please see Figure 1 The figure shows a control method for a multifunctional rehabilitation therapy instrument based on combined waveforms in the first embodiment of the present invention. The method specifically includes steps S01-S04.

[0067] S01, construct a combined waveform database, the database storing multiple preset physiotherapy modes, each of the preset physiotherapy modes being a waveform sequence composed of at least two basic waveforms among square wave, trapezoidal wave and exponential wave according to preset timing parameters, used to simulate corresponding traditional Chinese medicine physiotherapy techniques.

[0068] Specifically, physical signals of the techniques used by patients receiving traditional Chinese medicine physiotherapy, patient body data, and patient subjective data are collected to construct a multidimensional dataset. Based on the multidimensional dataset, the physical signals are preprocessed and feature extracted, and target-related features are determined through screening. Based on the target-related features, a gradient boosting tree model is used to construct a mapping model of combined waveform parameters and physiotherapy effects. The mapping model is then trained and optimized to determine waveform rules in order to construct a combined waveform database.

[0069] In practical implementation, to achieve a precise mapping between "electrical pulse combination waveforms and therapeutic effects," this database can be constructed using a systematic process of "quantitative acquisition of traditional Chinese medicine techniques → multi-dimensional signal analysis → machine learning modeling → clinical validation iteration," avoiding the subjectivity of traditional "empirical waveform definitions." The core data analysis and modeling methods are as follows:

[0070] First, quantitative data collection was conducted on traditional Chinese medicine physiotherapy techniques:

[0071] Specifically, the data collection subjects and scenario design were as follows: 20 TCM doctors with more than 8 years of clinical experience (including specialists in massage, scraping, and percussion) were selected to perform physiotherapy on 60 volunteers (aged 25-60, covering different body mass indexes BMI=18-28) in a standardized scenario. The three core techniques of "massage (pressing / kneading), scraping (light scraping / heavy scraping), and percussion (high frequency / low frequency)" were recorded. Each technique was repeated 30 times at three intensity levels of "light / medium / heavy" to form a basic sample database.

[0072] Specifically, multimodal sensor synchronous acquisition: A high-precision sensor array is used to convert the "biomechanical characteristics" of traditional Chinese medicine techniques into quantifiable physical signals. The specific acquisition scheme is as follows.

[0073]

[0074] Simultaneously collect subjective feedback from volunteers (instant pain NRS score, comfort VAS score) to form a four-dimensional dataset of "manual operation - physical signal - physiological response - subjective evaluation".

[0075] Secondly, signal preprocessing and feature extraction:

[0076] Specific wavelet noise reduction: The force signal and electromyography (EMG) signal are decomposed into 5 levels using the db wavelet basis. High-frequency noise is removed by soft thresholding (threshold λ=0.8σ, where σ is the noise standard deviation). After processing, the signal-to-noise ratio is improved from 28dB to 45dB. Baseline correction: The EMG signal is filtered by moving average (window size 200ms) to eliminate baseline drift and ensure the stability of muscle excitability indicators (baseline fluctuation ≤0.3%). Time synchronization: Multi-sensor data are aligned based on GPS timestamps, and the time deviation is controlled within ±0.5ms to avoid feature distortion caused by timing misalignment.

[0077] Specifically, signal processing algorithms are used to extract feature parameters strongly correlated with "therapeutic effects," establishing a mapping bridge between "TCM manipulation features and electrical pulse parameters."

[0078]

[0079] Furthermore, the correlation between features and therapeutic effects (MPF reduction value, VAS score) was analyzed using the maximum information coefficient (MIC), and redundant features with MIC < 0.65 were removed. Principal component analysis (PCA) was used to reduce the dimensionality of the remaining 12 features to 6 dimensions (cumulative variance contribution rate ≥ 92%), thereby reducing the complexity of modeling.

[0080] In addition, machine learning modeling and mapping rule generation are performed:

[0081] Specifically, a gradient boosting tree (XGBoost) is used to construct a mapping model of "combined waveform parameters → therapeutic effect" (superior to traditional linear models, solving nonlinear correlation problems). The specific definitions are as follows: input variable X, adjustable parameters of the combined waveform (after encoding): basic waveform type (square wave / trapezoidal wave / exponential wave, independently encoded); frequency f (1-100Hz), amplitude A (0-5V), duty cycle D (30%-70%), waveform switching interval T (50-200ms); output variable Y, quantitative indicators of therapeutic effect: muscle relaxation effect, electromyographic MPF reduction value ΔMPF (Hz); subjective comfort, VAS score (1-10 points).

[0082] Specifically, the dataset is divided as follows: 70% of the samples are used for training, 15% for validation, and 15% for testing; hyperparameter optimization is then performed using Bayesian optimization to search for the optimal parameters.

[0083]

[0084] Model performance: On the test set, the ΔMPF prediction error is ≤1.5Hz (R²=0.91), and the VAS score prediction accuracy is ≥88% (MAE=0.4), proving the model's mapping accuracy.

[0085] Specifically, based on the model output, the core mapping rules for the database are constructed as follows: Single waveform basic rules: Square wave corresponds to "high-frequency stimulation" (f=50-80Hz, ΔMPF=25-35Hz), trapezoidal wave corresponds to "gentle stimulation" (f=30-50Hz, ΔMPF=15-25Hz), and exponential wave corresponds to "gradual stimulation" (f=20-40Hz, ΔMPF=10-20Hz); Combined waveform logic: When the target effect is "deep relaxation (ΔMPF≥30Hz) + high comfort (VAS≥8 points)," the model recommends the combination of "trapezoidal wave (f=45Hz, A=3.2V, D=60%) → square wave (f=60Hz, A=2.8V, D=40%)," with a switching interval T=80ms (simulating "kneading- (Alternating pressing rhythm); Dynamic threshold adjustment: if ΔMPF < 15Hz (insufficient effect), then A + 0.4V, f + 6Hz; if VAS < 6 points (low comfort), then A - 0.3V, T + 20ms.

[0086] Finally, clinical validation and iterative optimization are carried out:

[0087] Specifically, a multicenter validation trial, for example, selected 150 patients with low back pain (NRS score 5-8) and randomly assigned them to a "waveform library treatment group" (using the model-recommended protocol) and a "traditional control group" (fixed square wave f=50Hz, A=3V). Each group received 10 treatments (20 minutes each time). The results showed:

[0088]

[0089] Specifically, a database iteration mechanism is implemented. For example, for every 100 clinical data cases accumulated, the model is updated through incremental learning (50% of historical parameters are frozen, and only new samples are trained). The waveform rules are dynamically optimized as follows: for elderly patients (weak electromyographic signals): the recommended amplitude is +0.5V by default; for patients with sensitive constitutions (VAS score easily <6 points): the recommended proportion of exponential waves is increased to 60%.

[0090] S02, acquire patient data, match the patient data with the database to determine a preliminary physiotherapy plan, and adjust the parameters of the preliminary physiotherapy plan according to the patient data through a preset dynamic parameter adjustment model to determine the target physiotherapy plan.

[0091] Specifically, the patient data includes at least disease condition data and pain score data. A preliminary physiotherapy plan is determined by matching the disease condition data and pain score data against the database. A correction frequency is determined based on the pain score data, and this correction frequency is combined with the base frequency of the waveform in the preliminary physiotherapy plan to obtain a target frequency. It is determined whether the injury time in the disease condition data exceeds a preset time threshold. If not, the base amplitude of the waveform in the preliminary theoretical plan is the target amplitude; if so, a correction amplitude is determined based on the injury time, and this correction amplitude is combined with the base amplitude to obtain the target amplitude. In specific implementation, to achieve personalized treatment, this system constructs a parameter dynamic adjustment model with NRS pain score (0-10 points) and disease stage (acute / chronic phase) as inputs. Multi-dimensional parameter linkage ensures the accuracy of treatment. More specifically, a suitable basic physiotherapy plan is first determined based on patient data. Then, based on specific pain score data and disease condition data, the frequency and amplitude in the basic physiotherapy plan are adjusted to adapt to the patient's condition. This solves the problem that traditional medical equipment often uses fixed parameter treatment plans, which are difficult to adapt to the complex and changing conditions of patients. This approach achieves a positive correlation between treatment frequency and patient pain intensity. Simultaneously, it gradually increases treatment intensity according to the stage of disease progression. This innovative model of multi-dimensional parameter synergistic dynamic adjustment comprehensively improves the matching degree between the treatment plan and the patient's actual condition.

[0092] Based on the pain score data and the injury time, the correction frequency and correction magnitude are determined using a first preset formula and a second preset formula, respectively.

[0093] The first preset formula is:

[0094]

[0095] in, To correct the frequency, Based on the base frequency, To correct the frequency, Rate the pain. This is the frequency correction factor;

[0096] The second preset formula is:

[0097]

[0098] in, To adjust the range, , Basic amplitude, For the limit amplitude, For the time of damage, For the preset time threshold, This is the amplitude correction coefficient. Furthermore, in practical implementation, the patient's condition can be divided into stages based on their actual situation, such as the acute phase and the chronic phase. The chronic phase is further divided into different stages. When the patient is in the acute phase, a low-intensity protection strategy is adopted to ensure that the amplitude does not exceed the baseline amplitude. When in the chronic phase, different stage values ​​are determined according to different stages, which can then be substituted into the above formula to determine the corresponding appropriate amplitude based on different chronic phase stages.

[0099] S03, based on a preset mechanism, the waveform output of the target physiotherapy plan is realized to perform physiotherapy on the patient, wherein a preset waveform adjustment algorithm is used to achieve a smooth transition between the previous and subsequent waveforms when switching waveforms.

[0100] Specifically, based on the target physiotherapy plan, preceding waveform data and subsequent waveform data are determined. Preceding control parameters are then determined based on the preceding waveform data to perform physiotherapy on the patient, and subsequent control parameters are determined based on the subsequent waveform data. Buffer waveform data within a preset buffer time is determined based on the subsequent waveform data and the preceding waveform data, and buffer control parameters are then determined based on the buffer waveform data. Finally, the physiotherapy device is controlled to perform physiotherapy on the patient based on the preceding control parameters, the buffer control parameters, the subsequent control parameters, and the preset timing parameters.

[0101] The buffered waveform data is determined by a third preset formula based on the subsequent waveform data and the preceding waveform data.

[0102] The third preset formula is:

[0103]

[0104] in, The amplitude of the preceding waveform. The amplitude of the subsequent waveform. The transition time constant, t For time.

[0105] In practice, the waveform switching logic is implemented using an FPGA state machine, employing a three-step mechanism of "preloading-buffering-switching" to ensure a seamless transition. The switching time calculation formula is as follows:

[0106]

[0107] System clock (1GHz clock speed) The number of clock cycles required for the state transition. At that time, the switching time reached 1 This is far below the 1ms accuracy requirement. Furthermore, to avoid waveform distortion during the switching process, a 10ms interval is set before the two waveforms alternate. The transition buffer uses a third preset formula to adjust the waveform amplitude in the transition zone, achieving a smooth amplitude transition.

[0108] In summary, the control method for the multifunctional rehabilitation therapy instrument based on combined waveforms in the above embodiments of the present invention constructs a rich and professional database of combined waveforms. This database is meticulously designed with various waveform sequences based on the biomechanical characteristics of traditional Chinese medicine physiotherapy techniques. This allows for automatic adaptation of the corresponding waveform sequence to patient data, providing patients with precise, professional treatment services that align with the principles of traditional Chinese medicine physiotherapy and improving treatment effectiveness. Furthermore, based on the pain score and condition data in the patient's data, the parameters of the adapted waveform sequence are dynamically adjusted in multiple dimensions to better match the patient's actual condition, generating a personalized combined waveform treatment plan. In addition, a transition buffer is set during waveform changes in the waveform sequence, and a preset waveform adjustment algorithm is used to achieve smooth amplitude transitions, effectively controlling the waveform distortion during the transition phase. This ensures that the patient does not experience significant abrupt changes in stimulation due to waveform switching during treatment, greatly improving the comfort and stability of the treatment process. Therefore, the present invention solves the problem of the lack of an adaptive, dynamically adjustable, and effective multifunctional rehabilitation therapy instrument and control method based on combined waveforms in the prior art.

[0109] Example 2

[0110] In another aspect, this invention also proposes a multifunctional rehabilitation therapy device based on combined waveforms, which performs physiotherapy on patients according to the aforementioned control method for the multifunctional rehabilitation therapy device based on combined waveforms, including:

[0111] Control unit, used to execute the control method;

[0112] The waveform generation unit is used to generate a corresponding therapeutic wave that matches the traditional Chinese medicine physiotherapy technique based on the target combined waveform data and target treatment parameters output by the control unit.

[0113] A signal amplification unit is used to amplify the signal output by the waveform generation unit to ensure the stimulation intensity.

[0114] The interactive unit is used to acquire patient data and display treatment parameters in real time;

[0115] The wireless transmission unit is used to upload treatment data to the cloud, providing data support for remote diagnosis and treatment and efficacy evaluation.

[0116] As an example, and not a limitation, in some alternative embodiments, a minimum system circuit can be built using a Samsung Exynos 4412 quad-core processor as the control core, based on the ARM Cortex-A9 architecture, including a power management unit, a 20MHz crystal clock circuit, and an RC reset circuit, such as... Figure 4As shown. By configuring the Linux 4.9 kernel parameters, the processor's operating frequency is dynamically adjusted to 1.4GHz. Utilizing the quad-core parallel processing capability, multiple tasks such as waveform generation (35% CPU load), WiFi data transmission (20%), and human-computer interaction (15%) are performed collaboratively, with task switching latency ≤50ms. Specific hardware architecture Figure 4 As shown.

[0117] Furthermore, the digital waveform output circuit of the waveform generation unit can be used to construct a DDS driver circuit based on the AD8951 chip (such as...). Figure 5 As shown), its internal 32-bit phase accumulator achieves frequency synthesis through the following recursive relationship:

[0118]

[0119] When a 125MHz reference clock is input, the output frequency formula is:

[0120]

[0121] A frequency resolution of 0.029 Hz can be achieved by writing the frequency control word K (range 0~2^32-1) through the 16-bit SPI interface. For example, when K=8388608, the calculation is as follows: It meets the 1-1000Hz adjustment range of the physiotherapy device. The chip's built-in high-speed comparator converts the analog signal output from the sine lookup table into a square wave. With the help of an external RC filter circuit, it can generate basic waveforms such as trapezoidal waves and exponential waves, with a rise time ≤50ns.

[0122] Furthermore, two 470s can be used. An electrolytic capacitor and four 1N4007 rectifier diodes form a bridge rectifier module, which converts the 13V AC signal output from the step-down transformer into pulsating DC, which is then passed through a 100V rectifier circuit. A voltage regulator circuit is connected after the filter capacitor. The voltage regulator section consists of two 3.3V Zener diodes (IN4733A), two high-power transistors (NPN type MJE3055T and PNP type MJE2955T), and a voltage divider resistor network. It achieves a 5V regulated output through a negative feedback mechanism, with a ripple voltage ≤50mV (actual peak-to-peak value measured by oscilloscope), meeting the 3.3V power supply requirements of the AD9851 chip and the 5V power supply requirements of the processor. The specific circuit is shown below. Figure 6 As shown.

[0123] Alternatively, the power amplifier circuit of the signal amplification unit can use BD438 (NPN) / S8050 (NPN) and BD437 (PNP) / S8550 (PNP) to form a quasi-complementary symmetrical amplification structure, and the voltage gain formula is:

[0124]

[0125] Among them, the emitter feedback resistor load resistance The theoretical gain is 100 times. In the actual circuit, 100... The compensation capacitor eliminates high-frequency self-oscillation. Powered by a dual ±15V power supply, it amplifies the 0-0.3V signal output from the digital-to-analog converter to 0-30V, with a maximum output current of 500mA, meeting the stimulation intensity requirements of medical electrode plates (equivalent impedance 50-200Ω). Distortion is measured to be <0.05% using a spectrum analyzer (1kHz sine wave test). The specific circuit is shown below. Figure 7 As shown.

[0126] Specifically, this solution uses the ESP8266 chip to construct the wireless communication module, i.e., the wireless transmission unit, operating in STA mode, supporting the IEEE 802.11b / g / n protocol, with a maximum transmission rate of 72.2Mbps. The module communicates with the Exynos4412 master controller via a UART interface, with a baud rate set to 115200bps and a data frame format of 8N1. The communication protocol stack design is as follows: Figure 8 As shown.

[0127] Alternatively, a resistive touchscreen + TFT LCD solution can be used, with a resolution of 800×480 and a display of 260,000 colors. Its interface design is as follows: Figure 9 As shown. The drive circuit design includes:

[0128] Touch control chip: ADS7843, 12-bit ADC resolution, communicates with the host controller via SPI interface;

[0129] Backlight control: Employs PWM dimming (frequency 2kHz), with a duty cycle adjustment range of 5%-100%;

[0130] Interface design: 8-bit 8080 parallel interface, read and write timing meets the requirements of TFT controller.

[0131] The touch response algorithm uses a Kalman filter to process touch coordinate data, and the state prediction equation is as follows: , where the state vector It contains position and velocity information. Sliding operation detection: when the distance d between two adjacent sampling points is greater than 10px and the angle change θ is less than 30°, it is determined to be sliding.

[0132] In addition, a temperature control circuit can be set up to cooperate with waveform physiotherapy. The temperature control circuit can use an NTC thermistor (B value 3950K) as the temperature sensor, with a measurement range of 25-60℃ and an accuracy of ±0.1℃. The conditioning circuit includes: a Wheatstone bridge to convert resistance changes into a voltage signal; an instrumentation amplifier, AD620, with a gain set to 100; and a second-order low-pass filter with a cutoff frequency of 10Hz to reduce noise interference. PID control algorithm implementation:

[0133]

[0134] Discretization (sampling period T=100ms): ,

[0135] Parameter tuning (Ziegler-Nichols): Critical gain Critical period Final parameters: , , .

[0136] Example 3

[0137] Please see Figure 2 The diagram shown is a structural block diagram of the multifunctional rehabilitation therapy instrument control system based on combined waveforms proposed in the third embodiment of the present invention. This multifunctional rehabilitation therapy instrument control system 200 based on combined waveforms includes: a database construction module 21, a scheme determination module 22, and a therapy module 23, wherein:

[0138] The database construction module 21 is used to construct a combined waveform database. The database stores multiple preset physiotherapy modes. Each preset physiotherapy mode is a waveform sequence composed of at least two basic waveforms among square wave, trapezoidal wave and exponential wave according to preset timing parameters, used to simulate corresponding traditional Chinese medicine physiotherapy techniques.

[0139] The solution determination module 22 is used to acquire patient data, match the patient data with the database to determine a preliminary physiotherapy plan, and adjust the parameters of the preliminary physiotherapy plan according to the patient data through a preset dynamic parameter adjustment model to determine a target physiotherapy plan.

[0140] Physiotherapy module 23 is used to output the waveform of the target physiotherapy plan based on a preset mechanism to perform physiotherapy on the patient. When switching waveforms, a preset waveform adjustment algorithm is used to achieve a smooth transition between the previous and next waveforms.

[0141] Example 4

[0142] In another aspect, the present invention also proposes an electronic device, please refer to [link to relevant documentation]. Figure 3The diagram shows an electronic device according to the fourth embodiment of the present invention, including a memory 20, a processor 10, and a computer program 30 stored in the memory and executable on the processor. When the processor 10 executes the computer program 30, it implements the control method of the multifunctional rehabilitation therapy instrument based on combined waveforms as described above.

[0143] In some embodiments, the processor 10 may be a central processing unit (CPU), controller, microcontroller, microprocessor or other data processing chip, used to run program code stored in memory 20 or process data, such as executing access restriction programs.

[0144] The memory 20 includes at least one type of readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 20 can be an internal storage unit of an electronic device, such as the hard disk of the electronic device. In other embodiments, the memory 20 can also be an external storage device of the electronic device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 20 can include both internal and external storage units of the electronic device. The memory 20 can be used not only to store application software and various types of data of the electronic device, but also to temporarily store data that has been output or will be output.

[0145] It should be pointed out that, Figure 3 The structure shown does not constitute a limitation on the electronic device. In other embodiments, the electronic device may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0146] This invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned control method for a multifunctional rehabilitation therapy device based on combined waveforms.

[0147] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0148] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0149] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0150] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0151] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A multifunctional rehabilitation therapy device based on combined waveforms, characterized in that, The physiotherapy device includes: The control unit is used to execute the control method of the multifunctional rehabilitation therapy instrument based on combined waveforms; The waveform generation unit is used to generate a corresponding therapeutic wave that matches the traditional Chinese medicine physiotherapy technique based on the target combined waveform data and target treatment parameters output by the control unit. A signal amplification unit is used to amplify the signal output by the waveform generation unit to ensure the stimulation intensity. The interactive unit is used to acquire patient data and display treatment parameters in real time; The wireless transmission unit is used to upload treatment data to the cloud, providing data support for remote diagnosis and treatment and efficacy evaluation. The control method for the multifunctional rehabilitation therapy instrument based on combined waveforms includes: A combined waveform database is constructed, which stores multiple preset physiotherapy modes. Each preset physiotherapy mode is a waveform sequence composed of at least two basic waveforms among square wave, trapezoidal wave and exponential wave according to preset timing parameters, used to simulate corresponding traditional Chinese medicine physiotherapy techniques. Acquire patient data, match the patient data with the database to determine a preliminary physiotherapy plan, and adjust the parameters of the preliminary physiotherapy plan according to the patient data through a preset dynamic parameter adjustment model to determine a target physiotherapy plan; The target physiotherapy plan is output based on a preset mechanism to provide physiotherapy to the patient. During waveform switching, a preset waveform adjustment algorithm is used to achieve a smooth transition between the previous and next waveforms. The steps to construct a combined waveform database include: Collect physical signals of the techniques used in TCM physiotherapy, patient body data, and patient subjective data to construct a multidimensional dataset; Based on the aforementioned multidimensional dataset, physical signals are preprocessed and features are extracted, and then filtered to determine target-related features. Based on the target association features, a gradient boosting tree model is used to construct a mapping model of combined waveform parameters and physiotherapy effects. The mapping model is then trained and optimized to determine waveform rules in order to construct a combined waveform database.

2. The multifunctional rehabilitation therapy device based on combined waveforms according to claim 1, characterized in that, The patient data includes at least disease condition data and pain score data. The steps of acquiring patient data, matching the patient data with the database to determine a preliminary physiotherapy plan, and adjusting the parameters of the preliminary physiotherapy plan using a preset dynamic parameter adjustment model based on the patient data to determine a target physiotherapy plan include: A preliminary physiotherapy plan is determined by matching the patient's condition data and pain score data against the database. The correction frequency is determined based on the pain score data, and the correction frequency is combined with the base frequency of the waveform in the preliminary physiotherapy plan to obtain the target frequency; Determine whether the damage time in the disease data is greater than a preset time threshold; If not, then the base amplitude of the waveform in the preliminary physiotherapy plan is the target amplitude; If so, the correction range is determined based on the damage time, and the correction range is combined with the base range to obtain the target range.

3. The multifunctional rehabilitation therapy device based on combined waveforms according to claim 2, characterized in that, Based on the pain score data and the injury time, the correction frequency and correction magnitude are determined using a first preset formula and a second preset formula, respectively. The first preset formula is: in, To correct the frequency, Based on the base frequency, For frequency correction parameters, Rate the pain. This is the frequency correction factor; The second preset formula is: in, To correct the magnitude, , Basic amplitude, For the limit amplitude, For the time of damage, For the preset time threshold, This is the amplitude correction factor.

4. The multifunctional rehabilitation therapy device based on combined waveforms according to claim 1, characterized in that, The target physiotherapy plan is output based on a preset mechanism to provide physiotherapy to the patient. The step of achieving a smooth transition between waveforms during waveform switching using a preset waveform adjustment algorithm includes: Based on the target physiotherapy plan, determine the preceding waveform data and the subsequent waveform data, so as to determine the preceding control parameters for physiotherapy to the patient based on the preceding waveform data and determine the subsequent control parameters based on the subsequent waveform data; Based on the subsequent waveform data and the preceding waveform data, buffer waveform data within a preset buffer time is determined, and buffer control parameters are determined according to the buffer waveform data. The physiotherapy device is controlled to perform physiotherapy on the patient according to the preceding control parameters, the buffer control parameters, the subsequent control parameters, and the preset timing parameters.

5. The multifunctional rehabilitation therapy device based on combined waveforms according to claim 4, characterized in that, The buffered waveform data is determined by a third preset formula based on the subsequent waveform data and the preceding waveform data. The third preset formula is: in, The amplitude of the preceding waveform. The amplitude of the subsequent waveform. The transition time constant, t For time.

6. The multifunctional rehabilitation therapy device based on combined waveforms according to claim 1, characterized in that, Following the step of outputting the waveform of the target physiotherapy plan based on a preset mechanism to perform physiotherapy on the patient, the following steps are included: Real-time acquisition of patient feedback data to determine discrepancies based on the patient feedback data and patient data; Determine whether the ratio of the difference data to the patient data is greater than a preset difference threshold; If so, the target physiotherapy plan is adjusted based on the difference data using a preset dynamic parameter adjustment model.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of a control method for a multifunctional rehabilitation therapy instrument based on combined waveforms. The control method for the multifunctional rehabilitation therapy instrument based on combined waveforms includes: A combined waveform database is constructed, which stores multiple preset physiotherapy modes. Each preset physiotherapy mode is a waveform sequence composed of at least two basic waveforms among square wave, trapezoidal wave and exponential wave according to preset timing parameters, used to simulate corresponding traditional Chinese medicine physiotherapy techniques. Acquire patient data, match the patient data with the database to determine a preliminary physiotherapy plan, and adjust the parameters of the preliminary physiotherapy plan according to the patient data through a preset dynamic parameter adjustment model to determine a target physiotherapy plan; The target physiotherapy plan is output based on a preset mechanism to provide physiotherapy to the patient. During waveform switching, a preset waveform adjustment algorithm is used to achieve a smooth transition between the previous and next waveforms. The steps to construct a combined waveform database include: Collect physical signals of the techniques used in TCM physiotherapy, patient body data, and patient subjective data to construct a multidimensional dataset; Based on the aforementioned multidimensional dataset, physical signals are preprocessed and features are extracted, and then filtered to determine target-related features. Based on the target association features, a gradient boosting tree model is used to construct a mapping model of combined waveform parameters and physiotherapy effects. The mapping model is then trained and optimized to determine waveform rules in order to construct a combined waveform database.

8. The computer-readable storage medium according to claim 7, characterized in that, The patient data includes at least disease condition data and pain score data. The steps of acquiring patient data, matching the patient data with the database to determine a preliminary physiotherapy plan, and adjusting the parameters of the preliminary physiotherapy plan using a preset dynamic parameter adjustment model based on the patient data to determine a target physiotherapy plan include: A preliminary physiotherapy plan is determined by matching the patient's condition data and pain score data against the database. The correction frequency is determined based on the pain score data, and the correction frequency is combined with the base frequency of the waveform in the preliminary physiotherapy plan to obtain the target frequency; Determine whether the damage time in the disease data is greater than a preset time threshold; If not, then the base amplitude of the waveform in the preliminary physiotherapy plan is the target amplitude; If so, the correction range is determined based on the damage time, and the correction range is combined with the base range to obtain the target range.

9. The computer-readable storage medium according to claim 8, characterized in that, Based on the pain score data and the injury time, the correction frequency and correction magnitude are determined using a first preset formula and a second preset formula, respectively. The first preset formula is: in, To correct the frequency, Based on the base frequency, For frequency correction parameters, Rate the pain. This is the frequency correction factor; The second preset formula is: in, To correct the magnitude, , Basic amplitude, For the limit amplitude, For the time of damage, For the preset time threshold, This is the amplitude correction factor.

10. The computer-readable storage medium according to claim 7, characterized in that, The target physiotherapy plan is output based on a preset mechanism to provide physiotherapy to the patient. The step of achieving a smooth transition between waveforms during waveform switching using a preset waveform adjustment algorithm includes: Based on the target physiotherapy plan, determine the preceding waveform data and the subsequent waveform data, so as to determine the preceding control parameters for physiotherapy to the patient based on the preceding waveform data and determine the subsequent control parameters based on the subsequent waveform data; Based on the subsequent waveform data and the preceding waveform data, buffer waveform data within a preset buffer time is determined, and buffer control parameters are determined according to the buffer waveform data. The physiotherapy device is controlled to perform physiotherapy on the patient according to the preceding control parameters, the buffer control parameters, the subsequent control parameters, and the preset timing parameters.

11. The computer-readable storage medium according to claim 10, characterized in that, The buffered waveform data is determined by a third preset formula based on the subsequent waveform data and the preceding waveform data. The third preset formula is: in, The amplitude of the preceding waveform. The amplitude of the subsequent waveform. The transition time constant, t For time.

12. The computer-readable storage medium according to claim 7, characterized in that, Following the step of outputting the waveform of the target physiotherapy plan based on a preset mechanism to perform physiotherapy on the patient, the following steps are included: Real-time acquisition of patient feedback data to determine discrepancies based on the patient feedback data and patient data; Determine whether the ratio of the difference data to the patient data is greater than a preset difference threshold; If so, the target physiotherapy plan is adjusted based on the difference data using a preset dynamic parameter adjustment model.

13. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a control method for a multifunctional rehabilitation therapy device based on combined waveforms; The control method for the multifunctional rehabilitation therapy instrument based on combined waveforms includes: A combined waveform database is constructed, which stores multiple preset physiotherapy modes. Each preset physiotherapy mode is a waveform sequence composed of at least two basic waveforms among square wave, trapezoidal wave and exponential wave according to preset timing parameters, used to simulate corresponding traditional Chinese medicine physiotherapy techniques. Acquire patient data, match the patient data with the database to determine a preliminary physiotherapy plan, and adjust the parameters of the preliminary physiotherapy plan according to the patient data through a preset dynamic parameter adjustment model to determine a target physiotherapy plan; The target physiotherapy plan is output based on a preset mechanism to provide physiotherapy to the patient. During waveform switching, a preset waveform adjustment algorithm is used to achieve a smooth transition between the previous and next waveforms. The steps to construct a combined waveform database include: Collect physical signals of the techniques used in TCM physiotherapy, patient body data, and patient subjective data to construct a multidimensional dataset; Based on the aforementioned multidimensional dataset, physical signals are preprocessed and features are extracted, and then filtered to determine target-related features. Based on the target association features, a gradient boosting tree model is used to construct a mapping model of combined waveform parameters and physiotherapy effects. The mapping model is then trained and optimized to determine waveform rules in order to construct a combined waveform database.

14. The electronic device according to claim 13, characterized in that, The patient data includes at least disease condition data and pain score data. The steps of acquiring patient data, matching the patient data with the database to determine a preliminary physiotherapy plan, and adjusting the parameters of the preliminary physiotherapy plan using a preset dynamic parameter adjustment model based on the patient data to determine a target physiotherapy plan include: A preliminary physiotherapy plan is determined by matching the patient's condition data and pain score data against the database. The correction frequency is determined based on the pain score data, and the correction frequency is combined with the base frequency of the waveform in the preliminary physiotherapy plan to obtain the target frequency; Determine whether the damage time in the disease data is greater than a preset time threshold; If not, then the base amplitude of the waveform in the preliminary physiotherapy plan is the target amplitude; If so, the correction range is determined based on the damage time, and the correction range is combined with the base range to obtain the target range.

15. The electronic device according to claim 14, characterized in that, Based on the pain score data and the injury time, the correction frequency and correction magnitude are determined using a first preset formula and a second preset formula, respectively. The first preset formula is: in, To correct the frequency, Based on the base frequency, For frequency correction parameters, Rate the pain. This is the frequency correction factor; The second preset formula is: in, To correct the magnitude, , Basic amplitude, For the limit amplitude, For the time of damage, For the preset time threshold, This is the amplitude correction factor.

16. The electronic device according to claim 13, characterized in that, The target physiotherapy plan is output based on a preset mechanism to provide physiotherapy to the patient. The step of achieving a smooth transition between waveforms during waveform switching using a preset waveform adjustment algorithm includes: Based on the target physiotherapy plan, determine the preceding waveform data and the subsequent waveform data, so as to determine the preceding control parameters for physiotherapy to the patient based on the preceding waveform data and determine the subsequent control parameters based on the subsequent waveform data; Based on the subsequent waveform data and the preceding waveform data, buffer waveform data within a preset buffer time is determined, and buffer control parameters are determined according to the buffer waveform data. The physiotherapy device is controlled to perform physiotherapy on the patient according to the preceding control parameters, the buffer control parameters, the subsequent control parameters, and the preset timing parameters.

17. The electronic device according to claim 16, characterized in that, The buffered waveform data is determined by a third preset formula based on the subsequent waveform data and the preceding waveform data. The third preset formula is: in, The amplitude of the preceding waveform. The amplitude of the subsequent waveform. The transition time constant, t For time.

18. The electronic device according to claim 13, characterized in that, Following the step of outputting the waveform of the target physiotherapy plan based on a preset mechanism to perform physiotherapy on the patient, the following steps are included: Real-time acquisition of patient feedback data to determine discrepancies based on the patient feedback data and patient data; Determine whether the ratio of the difference data to the patient data is greater than a preset difference threshold; If so, the target physiotherapy plan is adjusted based on the difference data using a preset dynamic parameter adjustment model.

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