Physiotherapy device, control method therefor, and physiotherapy system

Abstract

This application provides a physiotherapy device and its control method, as well as a physiotherapy system. The physiotherapy device includes multiple electrode pads and a controller. The controller is configured to: acquire real-time physiological data of a user; acquire the user's metabolic index based on the real-time physiological data; acquire a trend parameter of the user's metabolic index based on the user's metabolic index over a first preset time period; acquire a corresponding physiotherapy strategy based on the trend parameter of the user's metabolic index, the physiotherapy strategy indicating the physiotherapy site, physiotherapy mode, and corresponding physiotherapy intensity; and output corresponding electrical stimulation pulses to the user's body surface using the multiple electrode pads based on the user's physiotherapy strategy. Determining the physiotherapy strategy through the trend parameter of the user's metabolic index allows for precise electrical stimulation to be provided to users in a state of motion, resulting in better stimulation effects.

Description

Technical Field

[0001] This application relates to the field of wearable device technology, and in particular to physiotherapy equipment and its control methods and physiotherapy systems. Background Technology

[0002] Physiotherapy equipment is used to apply physical agents to the human body to induce improvement. Common physical agents include electricity, sound, light, magnetism, water, and pressure. Among these, electrotherapy is one of the most commonly used methods in physiotherapy. With the development of medical technology, electrotherapy has gradually been applied in medicine.

[0003] EMS (Electric Muscle Stimulation) is a type of electrotherapy that uses low- to medium-frequency pulsed currents to conduct to the motor system, simulating human brainwaves, thereby inducing muscle contraction.

[0004] RF (Radio Frequency) is also a type of electrotherapy. By converting electromagnetic energy into heat energy, it generates a radiofrequency thermoelectric effect on elastomeres at different depths under the skin, accelerating blood circulation in the subcutaneous elastic tissue and stimulating collagen components in the elastomer to fully participate in metabolism and regeneration, thereby achieving the effects of tightening and wrinkle removal.

[0005] Patent CN109999435B discloses an EMS-based fitness method, which includes: acquiring a user's physiological parameter data; determining exercise parameters based on the physiological parameter data; generating a control signal based on the exercise parameters; outputting a pulse signal in response to the control signal; and outputting an electrical stimulation signal based on the pulse signal. The exercise parameters include parameters such as frequency, pulse width, intensity, rise-off time, and exercise / rest time. However, this method does not consider that physiological parameter data is in a dynamic process, especially during user exercise. Relying solely on physiological parameter data cannot objectively reflect the changing trend of the user's exercise intensity, which directly affects the stimulation effect. This method cannot provide precise electrical stimulation to users in an active state.

[0006] Therefore, there is an urgent need to provide a physiotherapy device and its control method, as well as a physiotherapy system, to solve the problems of existing technologies. Summary of the Invention

[0007] The purpose of this application is to provide physiotherapy equipment and its control method, and physiotherapy system, to provide precise electrical stimulation for users in motion, with better stimulation effect.

[0008] The objective of this application is achieved through the following technical solution:

[0009] In a first aspect, this application provides a physiotherapy device for providing a baseline electrical stimulation physiotherapy function to a user in a state of motion, the physiotherapy device comprising a plurality of electrode pads and a controller, the controller being configured to:

[0010] Obtain the user's real-time physiological data;

[0011] Based on the real-time physiological data, the user's metabolic index is obtained;

[0012] Based on the user's metabolic index within a first preset time period, obtain the trend parameters of the user's metabolic index;

[0013] Based on the trend parameters of the user's metabolic index, the corresponding physiotherapy strategy for the user is obtained. The physiotherapy strategy is used to indicate the physiotherapy site, physiotherapy mode and corresponding physiotherapy intensity.

[0014] Based on the user's physiotherapy strategy, multiple electrode pads are used to output corresponding electrical stimulation pulses to the user's body surface in order to change the user's metabolic index.

[0015] The beneficial effects of this technical solution are as follows: based on the user's real-time physiological data, the user's metabolic index is obtained, and then the trend parameters of the user's metabolic index are obtained, thereby judging the changing trend of the user's exercise intensity, and thus determining the corresponding physiotherapy strategy for the user. Multiple electrode pads are used to output corresponding electrical stimulation pulses to the user's body surface to change the user's metabolic index.

[0016] The physiotherapy device of this application takes into account that the user's physiological data is in a dynamic process during exercise. The physiotherapy strategy is not determined directly based on the physiological data, but rather by obtaining the metabolic index within a first preset time period through real-time physiological data, and then obtaining the trend parameter of the metabolic index. The trend parameter of the metabolic index can evaluate the changing trend of the user's exercise intensity within the first preset time period. In this way, the physiotherapy strategy can be determined by the trend parameter of the user's metabolic index, which can provide precise electrical stimulation to the user in the exercise state, and the stimulation effect is better.

[0017] In some optional embodiments, the physiotherapy mode corresponds to one or more of the following stimulation types: EMS low-frequency electromyography stimulation, EMS sub-intermediate frequency smooth muscle stimulation, EMS intermediate frequency nerve stimulation, EMS intermediate frequency unbalanced pulse alternating electric field stimulation, and RF radiofrequency subcutaneous elastic tissue stimulation.

[0018] The stimulation parameters for each type of stimulation include one or more of the following: frequency, pulse width, amplitude, waveform, duty cycle, and power.

[0019] The beneficial effects of this technical solution are as follows: EMS low-frequency electromyography can improve muscle endurance, excitability, microcirculation efficiency, and nutrient metabolism efficiency, ultimately significantly increasing the body's basal metabolic rate and improving the user's metabolic index; EMS sub-mid-frequency smooth muscle stimulation can induce contraction and relaxation of smooth muscle through electrical stimulation under different frequency waveform modulation; EMS mid-frequency nerve stimulation can act on the peripheral nerves of the spinal cord abducting nerves for mid-frequency nerve regulation, using a low-frequency, low-energy induction method, modulating the excitability and balance of the sympathetic and parasympathetic nerves through low-frequency DC combined with mid-frequency AC electrical signals; EMS mid-frequency unbalanced pulsed alternating electric field stimulation can promote sebum metabolism and stimulate the skin's efficient absorption of nutrients through a mid-frequency alternating pulsed electric field; RF radiofrequency subcutaneous elastic tissue stimulation can act on skin tissue, restoring skin elasticity and achieving lifting and anti-wrinkle effects. The therapy mode can correspond to a single stimulation type or a simultaneous combination of multiple stimulation types to achieve multiple functions.

[0020] In some optional embodiments, the controller is further configured to obtain the user's metabolic index in the following manner:

[0021] Acquire real-time physiological data of multiple sample objects and their corresponding metabolic index annotation data, wherein the real-time physiological data of each sample object is obtained by actual measurement or generated by the generative network of the GAN model;

[0022] Based on the real-time physiological data of the multiple sample objects and their corresponding metabolic index annotation data, a metabolic index model is obtained by training a preset deep learning model.

[0023] The user's real-time physiological data is input into the metabolic index model to obtain the user's metabolic index.

[0024] The beneficial effects of this technical solution are as follows: A pre-defined deep learning model is trained using real-time physiological data and corresponding metabolic indices from multiple sample objects to obtain a metabolic index model. This model can be trained with a large amount of training data and can predict corresponding metabolic indices for various real-time physiological data, exhibiting wide applicability and high intelligence. By designing and establishing an appropriate number of neural computing nodes and a multi-layered computational hierarchy, and selecting suitable input and output layers, a pre-defined deep learning model can be obtained. Through learning and optimization of this pre-defined deep learning model, a functional relationship from input to output is established. Although it cannot find a 100% accurate functional relationship between input and output, it can approximate the real-world correlation as closely as possible. The metabolic index model trained in this way can analyze and process real-time physiological data with high reliability of the analysis results.

[0025] In some optional embodiments, the controller is further configured to acquire trend parameters of the user's metabolic index in the following manner:

[0026] Based on the user's metabolic index within a first preset time period, a metabolic index curve is plotted, and each point on the metabolic index curve is used to indicate the user's metabolic index at the corresponding time.

[0027] The metabolic index curve is differentiated to obtain the metabolic index trend parameter curve of the user within the first preset time period. Each point on the metabolic index trend parameter curve is used to indicate the trend parameter of the user's metabolic index at the corresponding time.

[0028] The beneficial effects of this technical solution are as follows: by calculating the derivative of the metabolic index curve, the metabolic index trend parameter curve is obtained. Thus, the metabolic index trend parameter at each point on the metabolic index trend parameter curve can be known. Compared with the method of calculating the metabolic index trend parameter by the ratio of the increase in metabolic index to the duration, the result obtained by the derivative method is not an average value over a period of time, but an instantaneous value. This calculation method is more accurate.

[0029] In some optional embodiments, the controller is further configured to obtain the physiotherapy strategy corresponding to the user in the following manner:

[0030] Obtain the user's personal information, which includes one or more of the following: age, gender, BMI, body fat percentage, and medical history.

[0031] Based on the user's personal information and the trend parameters of the user's metabolic index, the corresponding physiotherapy strategy for the user is obtained.

[0032] The beneficial effects of this technical solution are as follows: the applicable physiotherapy mode, intensity, and duration vary for different users. Older users and those with weaker constitutions can only tolerate limited stimulation. The physiotherapy strategy of this application also takes into account the individual differences of users. When determining the corresponding physiotherapy strategy for a user, it is necessary not only to look at the trend parameters of the user's metabolic index, but also to combine the user's personal information such as age, gender, BMI index, body fat percentage, and medical history. In this way, the physical conditions of different users can be taken into account, and more precise electrical stimulation can be provided for different users.

[0033] In some alternative embodiments, the controller is further configured to:

[0034] When the duration of physiotherapy on the user using the current physiotherapy strategy reaches the second preset duration, the metabolic index of the user at the current moment is obtained based on the real-time physiological data.

[0035] Calculate the difference between the user's current metabolic index and a preset metabolic index, and update the user's physiotherapy strategy based on the difference.

[0036] The beneficial effects of this technical solution are as follows: during the physiotherapy process, the physiotherapy strategy is not static, but is in a dynamic updating process. When the duration of a physiotherapy strategy reaches the second preset duration, the difference between the user's current metabolic index and the preset metabolic index is calculated. Based on this difference, the user's physiotherapy strategy is updated. In this way, the physiotherapy strategy can be updated with the preset metabolic index as a guide, providing the user with precise electrical stimulation.

[0037] In some optional embodiments, the physiotherapy strategy is also used to indicate a physiotherapy intensity threshold corresponding to the physiotherapy mode;

[0038] The controller is also configured to output corresponding electrical stimulation pulses to the user's body surface using the electrode pads in the following manner:

[0039] Receive adjustment commands for the physiotherapy strategy using an interactive device;

[0040] When the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is not greater than the physiotherapy intensity threshold, the electrode pads output corresponding electrical stimulation pulses to the user's body surface according to the adjusted physiotherapy strategy.

[0041] The beneficial effects of this technical solution are as follows: users can adjust the physiotherapy strategy, but this adjustment is not unlimited and needs to be controlled within a certain range. Most users do not have an intuitive understanding of the physiotherapy intensity and may not be able to make precise adjustments. By setting a physiotherapy intensity threshold, the adjustment is only effective when the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is not greater than the physiotherapy intensity threshold. This can prevent users from setting the physiotherapy intensity too high and ensure user safety.

[0042] In some optional embodiments, the controller is further configured to:

[0043] When the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is greater than the physiotherapy intensity threshold, perform any of the following operations:

[0044] Stop delivering electrical stimulation pulses to the user's body surface;

[0045] A prompt message is generated and sent to the interactive device.

[0046] The beneficial effect of this technical solution is that when the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is greater than the physiotherapy intensity threshold, the output of electrical stimulation pulses is stopped or a prompt message is generated and sent to the interactive device. This can further prevent users from setting the physiotherapy intensity too high and ensure user safety.

[0047] Secondly, this application provides a control method for a physiotherapy device, used to provide a baseline electrical stimulation physiotherapy function for a user in a state of motion, the physiotherapy device including multiple electrode pads and a controller, the method comprising:

[0048] Obtain the user's real-time physiological data;

[0049] Based on the real-time physiological data, the user's metabolic index is obtained;

[0050] Based on the user's metabolic index within a first preset time period, obtain the trend parameters of the user's metabolic index;

[0051] Based on the trend parameters of the user's metabolic index, the corresponding physiotherapy strategy for the user is obtained. The physiotherapy strategy is used to indicate the physiotherapy site, physiotherapy mode and corresponding physiotherapy intensity.

[0052] Based on the user's physiotherapy strategy, multiple electrode pads are used to output corresponding electrical stimulation pulses to the user's body surface in order to change the user's metabolic index.

[0053] In some optional embodiments, the physiotherapy mode corresponds to one or more of the following stimulation types: EMS low-frequency electromyography stimulation, EMS sub-intermediate frequency smooth muscle stimulation, EMS intermediate frequency nerve stimulation, EMS intermediate frequency unbalanced pulse alternating electric field stimulation, and RF radiofrequency subcutaneous elastic tissue stimulation.

[0054] The stimulation parameters for each type of stimulation include one or more of the following: frequency, pulse width, amplitude, waveform, duty cycle, and power.

[0055] In some optional embodiments, obtaining the user's metabolic index based on the real-time physiological data includes:

[0056] Acquire real-time physiological data of multiple sample objects and their corresponding metabolic index annotation data, wherein the real-time physiological data of each sample object is obtained by actual measurement or generated by the generative network of the GAN model;

[0057] Based on the real-time physiological data of the multiple sample objects and their corresponding metabolic index annotation data, a metabolic index model is obtained by training a preset deep learning model.

[0058] The user's real-time physiological data is input into the metabolic index model to obtain the user's metabolic index.

[0059] In some optional embodiments, obtaining the trend parameter of the user's metabolic index based on the user's metabolic index within a first preset time period includes:

[0060] Based on the user's metabolic index within a first preset time period, a metabolic index curve is plotted, and each point on the metabolic index curve is used to indicate the user's metabolic index at the corresponding time.

[0061] The metabolic index curve is differentiated to obtain the metabolic index trend parameter curve of the user within the first preset time period. Each point on the metabolic index trend parameter curve is used to indicate the trend parameter of the user's metabolic index at the corresponding time.

[0062] In some optional embodiments, obtaining the physiotherapy strategy corresponding to the user based on the trend parameter of the user's metabolic index includes:

[0063] Obtain the user's personal information, which includes one or more of the following: age, gender, BMI, body fat percentage, and medical history.

[0064] Based on the user's personal information and the trend parameters of the user's metabolic index, the corresponding physiotherapy strategy for the user is obtained.

[0065] In some optional embodiments, the method further includes:

[0066] When the duration of physiotherapy on the user using the current physiotherapy strategy reaches the second preset duration, the metabolic index of the user at the current moment is obtained based on the real-time physiological data.

[0067] Calculate the difference between the user's current metabolic index and a preset metabolic index, and update the user's physiotherapy strategy based on the difference.

[0068] In some optional embodiments, the physiotherapy strategy is also used to indicate a physiotherapy intensity threshold corresponding to the physiotherapy mode;

[0069] The physiotherapy strategy based on the user, which utilizes the electrode pads to output corresponding electrical stimulation pulses to the user's body surface, includes:

[0070] Receive adjustment commands for the physiotherapy strategy using an interactive device;

[0071] When the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is not greater than the physiotherapy intensity threshold, the electrode pads output corresponding electrical stimulation pulses to the user's body surface according to the adjusted physiotherapy strategy.

[0072] In some optional embodiments, the method further includes:

[0073] When the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is greater than the physiotherapy intensity threshold, perform any of the following operations:

[0074] Stop delivering electrical stimulation pulses to the user's body surface;

[0075] A prompt message is generated and sent to the interactive device.

[0076] Thirdly, this application provides a physiotherapy system, which includes a sensor and any of the aforementioned physiotherapy devices. Attached Figure Description

[0077] The present application will be further described below with reference to the accompanying drawings and embodiments.

[0078] Figure 1 This is a schematic diagram of the structure of a physiotherapy device provided in an embodiment of this application;

[0079] Figure 2 This is a schematic diagram of a process for detecting a user's physiological condition provided in an embodiment of this application;

[0080] Figure 3 This is a schematic diagram of a process for detecting a user's heart rate (HR) and heart rate variability (HRV) according to an embodiment of this application;

[0081] Figure 4 This is a schematic diagram of a process for detecting a user's blood oxygen saturation, provided in an embodiment of this application.

[0082] Figure 5 This is a schematic diagram of a process for detecting user microcirculation provided in an embodiment of this application;

[0083] Figure 6 This is a flowchart illustrating a control method for a physiotherapy device provided in an embodiment of this application;

[0084] Figure 7 This is a schematic diagram of a process for obtaining metabolic index provided in an embodiment of this application;

[0085] Figure 8 This is a schematic diagram of a process for obtaining trend parameters of metabolic index provided in an embodiment of this application;

[0086] Figure 9 This is a schematic diagram of a process for obtaining a physiotherapy strategy provided in an embodiment of this application;

[0087] Figure 10 This is a flowchart illustrating another control method for a physiotherapy device provided in an embodiment of this application;

[0088] Figure 11 This is a flowchart illustrating an adjustment of a physical therapy strategy provided in an embodiment of this application;

[0089] Figure 12 This is a flowchart illustrating another adjustment of physiotherapy strategy provided in an embodiment of this application;

[0090] Figure 13 This is a structural block diagram of a physiotherapy system provided in an embodiment of this application. Detailed Implementation

[0091] The present application will now be further described in conjunction with the accompanying drawings and specific embodiments. It should be noted that, without conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0092] See Figure 1 This application provides a physiotherapy device 100 for providing a baseline electrical stimulation physiotherapy function to a user in a state of motion. The physiotherapy device 100 includes a plurality of electrode pads 102 and a controller 101, the controller 101 being configured to:

[0093] Obtain the user's real-time physiological data;

[0094] Based on the real-time physiological data, the user's metabolic index is obtained;

[0095] Based on the user's metabolic index within a first preset time period, obtain the trend parameters of the user's metabolic index;

[0096] Based on the trend parameters of the user's metabolic index, the corresponding physiotherapy strategy for the user is obtained. The physiotherapy strategy is used to indicate the physiotherapy site, physiotherapy mode and corresponding physiotherapy intensity.

[0097] Based on the user's physiotherapy strategy, multiple electrode pads 102 are used to output corresponding electrical stimulation pulses to the user's body surface to change the user's metabolic index.

[0098] Therefore, based on the user's real-time physiological data, the user's metabolic index is obtained, and then the trend parameter of the user's metabolic index is obtained, thereby judging the trend of the user's exercise intensity. Based on this, the corresponding physical therapy strategy for the user is determined, and multiple electrode pads 102 are used to output corresponding electrical stimulation pulses to the user's body surface to change the user's metabolic index.

[0099] The physiotherapy device 100 of this application takes into account that during exercise, the user's physiological data is in a dynamic process of change. The physiotherapy strategy is not determined directly based on the physiological data, but rather by obtaining the metabolic index within a first preset time period through real-time physiological data, and then obtaining the trend parameter of the metabolic index. The trend parameter of the metabolic index can evaluate the changing trend of the user's exercise intensity within the first preset time period. In this way, by determining the physiotherapy strategy through the trend parameter of the user's metabolic index, precise electrical stimulation can be provided to the user in the exercise state, and the stimulation effect is better.

[0100] This application does not limit the physiotherapy device 100. The physiotherapy device 100 can be in the form of a wearable sports vest, shorts, knee pads, neck warmer, socks, etc. The physiotherapy device 100 can be worn locally or all over the body. The electrode pad 102 can be a silver-doped conductive gel electrode pad.

[0101] In some embodiments, the real-time physiological data includes one or more of the following: electrocardiogram data, pulse data, heart rate data, heart rate variability data, blood oxygen data, microcirculation data, electromyography data, electrooculography data, body temperature data, skin humidity data, and skin pigmentation data.

[0102] Among them, electrocardiogram (ECG) data can be used, and heart rate variability (HRV), also known as heart rate fluctuation, refers to the changes in the difference between successive heartbeat cycles, or the changes in the speed of the heartbeat.

[0103] In some implementations, sensors can be used to collect real-time physiological data from the user every third preset time interval. This application does not limit the third preset time interval; it can be 5 seconds, 10 seconds, or 15 seconds.

[0104] Sensors may include pulse wave sensors, blood oxygen sensors, heart rate sensors, temperature sensors, skin humidity sensors, etc.

[0105] In some implementations, the acquired real-time physiological data can be preprocessed, and the preprocessing methods may include one or more of the following: filtering, noise reduction, and compression.

[0106] After preprocessing, the data can be uploaded to an interactive device for further processing to obtain the user's metabolic index.

[0107] This application does not limit the interactive device, which may be a mobile phone, tablet computer (PAD), laptop computer, desktop computer, smart wearable device or standalone computing workstation.

[0108] When obtaining metabolic index, visual detection devices can be used to acquire the user's local limb movement trajectory to determine the user's current movement state. Then, combined with the user's real-time physiological data, machine learning and deep learning algorithms can be used to obtain the user's metabolic index.

[0109] When determining a physiotherapy strategy, the user's physiotherapy needs can be obtained first (using interactive devices). Based on these needs, the appropriate physiotherapy areas and modes can be automatically matched. These needs could include neck wrinkle reduction, pelvic floor muscle repair, abdominal muscle building, and waist fat reduction.

[0110] The treatment area may include one or more of the following: neck, abdomen, buttocks, waist, legs, upper arms, and feet.

[0111] Physiotherapy intensity can be expressed as strong, medium, or weak, or it can be expressed as a numerical level. The higher the level, the stronger the intensity. For example, physiotherapy intensity can include levels 1 to 9.

[0112] In some optional embodiments, the physiotherapy mode corresponds to one or more of the following stimulation types: EMS low-frequency electromyography stimulation, EMS sub-intermediate frequency smooth muscle stimulation, EMS intermediate frequency nerve stimulation, EMS intermediate frequency unbalanced pulse alternating electric field stimulation, and RF radiofrequency subcutaneous elastic tissue stimulation.

[0113] The stimulation parameters for each type of stimulation include one or more of the following: frequency, pulse width, amplitude, waveform, duty cycle, and power.

[0114] Therefore, EMS low-frequency electromyography can improve muscle endurance, excitability, microcirculation efficiency, and nutrient metabolism efficiency, ultimately significantly increasing the body's basal metabolic rate and improving the user's metabolic index. EMS sub-mid-frequency smooth muscle stimulation can induce contraction and relaxation of smooth muscle through electrical stimulation under different frequency waveform modulations. EMS mid-frequency nerve stimulation can act on the peripheral nerves of the spinal cord abducting nerves for mid-frequency nerve regulation, using a low-frequency, low-energy induction method, modulating the excitability and balance of the sympathetic and parasympathetic nerves respectively through low-frequency DC combined with mid-frequency AC electrical signals. EMS mid-frequency unbalanced pulsed alternating electric field stimulation can promote sebum metabolism and stimulate the skin's efficient absorption of nutrients through a mid-frequency alternating pulsed electric field. RF radiofrequency subcutaneous elastic tissue stimulation can act on skin tissue, restoring skin elasticity and achieving lifting and anti-wrinkle effects. The therapy modes can correspond to a single stimulation type or a simultaneous combination of multiple stimulation types to achieve multiple functions.

[0115] The frequency range of EMS low-frequency electromyography stimulation can be 0-1000Hz, and the frequency range of EMS medium-frequency nerve stimulation can be 1000Hz-10000Hz. The waveform can include one or more of the following: sine wave, square wave, triangular wave, exponential wave, sharp wave, sawtooth wave, and constant amplitude wave.

[0116] For low-frequency EMS electromyography, corresponding electrode pads can be placed near the major skeletal muscle groups of the trunk, forming eight circuits. For smooth muscle, a separate circuit can be set up for postpartum recovery of the female genital area.

[0117] For EMS mid-frequency nerve stimulation, the corresponding electrode pads can be placed at the base of the ear or the earlobe, which are relatively convenient stimulation locations for the human body; or, the corresponding electrode pads can be placed at the lower back, which is a major superficial ganglion location of the spinal cord nerve endings.

[0118] For EMS mid-frequency unbalanced pulse alternating electric field stimulation, an alternating conductive circuit can be used to apply the electrode pads used in EMS low-frequency electromyography. The corresponding electrode pads are placed on the skin surface, and eight signals are processed synchronously to form a single effect. In this way, the eight circuits in EMS low-frequency electromyography become one large circuit. However, the frequency, power, and waveform of the electrical signals in EMS mid-frequency unbalanced pulse alternating electric field stimulation can differ from those in EMS low-frequency electromyography, specifically targeting cortical nutrition and metabolism.

[0119] In a specific application, the user's physiotherapy needs are abdominal muscle growth, the physiotherapy area is determined to be the abdomen, and the physiotherapy mode is EMS low-frequency electromyography stimulation.

[0120] When there are multiple treatment areas, different treatment modes and intensities can be selected for different areas. Furthermore, the treatment mode can be a combination of various stimulation types, but the user's metabolic index trend parameter must not be less than a preset trend parameter threshold. The metabolic index can be expressed as a number or a percentage, and the metabolic index trend parameter can be expressed as a percentage. The preset trend parameter threshold is, for example, 20%, 30%, or 50%.

[0121] In a specific application, the treatment areas include the thighs and neck. The treatment mode for the thighs can be EMS mid-frequency electromyography stimulation, and the treatment mode for the neck can be a simultaneous combination of RF subcutaneous elastic tissue stimulation and EMS low-frequency electromyography stimulation (the user's metabolic index trend parameter is not less than 20% of the preset trend parameter threshold).

[0122] In some implementations, low-frequency EMS (electromyography) can be combined with skin nutrients to achieve iontophoresis skin therapy through transdermal nutrient delivery. The skin in the corresponding area is infiltrated with a nutrient solution (primarily sweat produced during exercise), and then iontophoresis is achieved through specific EMS pulse waves. Skin nutrients may include collagen, low-molecule hyaluronic acid, fruit acid oils, vitamins, etc.

[0123] In some optional embodiments, the controller 101 is further configured to obtain the user's metabolic index in the following manner:

[0124] Acquire real-time physiological data of multiple sample objects and their corresponding metabolic index annotation data, wherein the real-time physiological data of each sample object is obtained by actual measurement or generated by the generative network of the GAN model;

[0125] Based on the real-time physiological data of the multiple sample objects and their corresponding metabolic index annotation data, a metabolic index model is obtained by training a preset deep learning model.

[0126] The user's real-time physiological data is input into the metabolic index model to obtain the user's metabolic index.

[0127] Therefore, by training a pre-defined deep learning model using real-time physiological data from multiple sample objects and their corresponding labeled metabolic indices, a metabolic index model is obtained. This model can be trained with a large amount of training data and can predict corresponding metabolic indices for various real-time physiological data, exhibiting wide applicability and high intelligence. Through design, by establishing an appropriate number of neural computing nodes and a multi-layered computational hierarchy, and selecting suitable input and output layers, a pre-defined deep learning model can be obtained. Through learning and optimization of this pre-defined deep learning model, a functional relationship from input to output is established. Although it cannot find a 100% accurate functional relationship between input and output, it can approximate the real-world correlation as closely as possible. The metabolic index model trained in this way can analyze and process real-time physiological data with high reliability.

[0128] The metabolic index refers to the human body's energy metabolism rate, which can be used in this application to evaluate the user's exercise intensity.

[0129] This application does not limit the method of obtaining the labeled data of metabolic index. For example, manual labeling, automatic labeling, or semi-automatic labeling can be used.

[0130] This application does not limit the training process of the metabolic index model. For example, it can adopt the supervised learning training method described above, or the semi-supervised learning training method, or the unsupervised learning training method.

[0131] The step of training a metabolic index model using a pre-defined deep learning model based on the real-time physiological data of the multiple sample objects and their corresponding labeled metabolic index data includes:

[0132] The model parameters of the preset deep learning model are updated based on the real-time physiological data of the multiple sample objects and their corresponding metabolic index annotation data.

[0133] If the preset training termination condition is met, training is stopped and the preset deep learning model obtained from the training is used as the metabolic index model. If not, the preset deep learning model is trained using the real-time physiological data and labeled metabolic index of the next sample object.

[0134] This application does not limit the preset training termination conditions. For example, it may be that the number of training sessions reaches a preset number (the preset number of training sessions may be 1, 3, 10, 100, 1000, 10000, etc.), or it may be that the training data in the training set has been trained once or multiple times, or it may be that the total loss value obtained in this training is not greater than the preset loss value.

[0135] In some alternative implementations, the step of obtaining the user's metabolic index may include: inputting the obtained real-time physiological data of the user into a preset metabolic index calculation formula to calculate the user's metabolic index.

[0136] This application does not limit the preset formula for calculating the metabolic index, which can be, for example, a univariate polynomial or a multivariate polynomial, or a linear polynomial or a nonlinear polynomial. Using this metabolic index calculation formula and independent variables (one or more, generally multiple, of the user's electrocardiogram data, pulse data, heart rate data, heart rate variability data, blood oxygen data, microcirculation data, electromyography data, electrooculography data, body temperature data, skin moisture data, and skin pigmentation data), the dependent variable (the user's metabolic index) is calculated. The calculation process based on the formula consumes fewer computational resources, takes less time, and has high computational efficiency.

[0137] In a specific application, the formula for calculating the metabolic index is, for example: Metabolic index = (heart rate + pulse pressure difference) - 111.

[0138] In some optional embodiments, the controller 101 is further configured to acquire the trend parameters of the user's metabolic index in the following manner:

[0139] Based on the user's metabolic index within a first preset time period, a metabolic index curve is plotted, and each point on the metabolic index curve is used to indicate the user's metabolic index at the corresponding time.

[0140] The metabolic index curve is differentiated to obtain the metabolic index trend parameter curve of the user within the first preset time period. Each point on the metabolic index trend parameter curve is used to indicate the trend parameter of the user's metabolic index at the corresponding time.

[0141] Therefore, by differentiating the metabolic index curve, the metabolic index trend parameter curve can be obtained. This allows us to determine the metabolic index trend parameter at each point on the curve. Compared to calculating the ratio of the increase in metabolic index to the duration, the derivative method yields an instantaneous value rather than an average value over a period of time. This method is more accurate.

[0142] This application does not limit the first preset duration, which may be, for example, 5 seconds, 10 seconds, or 15 seconds.

[0143] In some optional embodiments, the controller 101 is further configured to obtain the physiotherapy strategy corresponding to the user in the following manner:

[0144] Obtain the user's personal information, which includes one or more of the following: age, gender, BMI, body fat percentage, and medical history.

[0145] Based on the user's personal information and the trend parameters of the user's metabolic index, the corresponding physiotherapy strategy for the user is obtained.

[0146] Therefore, the appropriate physiotherapy mode, intensity, and duration vary for different users. Older users and those with weaker constitutions can only tolerate limited stimulation. The physiotherapy strategy in this application also takes into account individual differences among users. When determining the appropriate physiotherapy strategy for a user, it is necessary not only to consider the trend parameters of the user's metabolic index, but also to combine the user's personal information such as age, gender, BMI, body fat percentage, and medical history. In this way, the physical conditions of different users can be taken into account, and more precise electrical stimulation can be provided for different users.

[0147] BMI stands for Body Mass Index, which is calculated as: BMI = weight (kg) / height (m) squared.

[0148] In some optional embodiments, before applying EMS stimulation to the user, the user's own physiological condition can be determined based on the user's real-time physiological data to determine whether the user is suitable for exercise. If the user's physiological condition is not suitable for exercise, the user will be given a warning to avoid accidents caused by the user receiving EMS stimulation when the user is in a physically uncomfortable state.

[0149] See Figure 2 , Figure 2 This is a schematic diagram of a process for detecting a user's physiological condition, provided as an embodiment of this application.

[0150] First, the user's heart rate variability (HR) is measured. If the HR is ≤20%, the user's heart rate is measured again.

[0151] When HR > 20% (HR can increase due to exercise), further testing is performed to check whether heart rate variability (HRV) is within the normal range. If HRV is not within the normal range, the user is alerted to pay attention to heart safety.

[0152] If HR and HRV are within the normal range, monitor the user's blood oxygen saturation rate. When the blood oxygen saturation rate is ≤10% (low), control the physiotherapy equipment to enter the soothing mode or stop working.

[0153] When the blood oxygen saturation rate is >10%, the user's aerobic exercise index is detected. When the aerobic exercise index is ≤0, it is determined that the user is performing anaerobic exercise. The waveform corresponding to the physiotherapy mode is controlled to be a wide waveform, and the user's microcirculation index is detected.

[0154] When the aerobic exercise index is >0, it is determined that the user is performing aerobic exercise. The waveform corresponding to the physiotherapy mode is controlled to be a narrow waveform, and the user's microcirculation index is detected.

[0155] When the microcirculation index is ≤0, it is determined that the user is in an abnormal exercise mode and the user is not suitable for exercise.

[0156] When the microcirculation index > 0, it is determined that the user is in the normal exercise mode and is suitable for exercise, and EMS stimulation is applied to the user.

[0157] See Figure 3 , Figure 3 This is a schematic diagram of a process for detecting a user's heart rate (HR) and heart rate variability (HRV) according to an embodiment of this application.

[0158] First, the user's heart rate variability (HRV) is continuously calculated to monitor the user's cardiac safety. When it is determined that the user's cardiac safety is good and suitable for exercise, the user is advised to start exercising.

[0159] When it is determined that a user's heart is not safe and exercise is risky, the change rate of LF (low frequency) and HF (high frequency) is detected. When the change rate of both LF (low frequency) and HF (high frequency) is 0, it indicates that the user is at risk of heart failure.

[0160] When at least one of the LF (low frequency) and HF (high frequency) change rates is >0, obtain the sympathetic nerve excitation and parasympathetic nerve excitation, calculate the user's exercise fatigue level (HPF), and determine whether the HRV is greater than the mean.

[0161] When HRV is less than or equal to the mean, it is recommended that the user rest or engage in low-intensity exercise.

[0162] When HRV > mean, it is recommended that users engage in high-intensity exercise, and users should be prompted to warm up before starting the exercise.

[0163] When the user is exercising, the user's heart rate change (HR) is detected. When HR ≤ 20%, no high-power EMS electromyography stimulation is applied. Only continuous low-power micro-electrical stimulation for the basic warm-up mode is provided.

[0164] When HRV>20%, apply EMS electromyography stimulation.

[0165] See Figure 4 , Figure 4 This is a schematic diagram of a process for detecting a user's blood oxygen, provided in an embodiment of this application.

[0166] First, the user's blood oxygen saturation rate is detected. When the blood oxygen saturation rate is less than 10%, it indicates that the user's blood oxygen saturation rate has dropped significantly. The user is given an early warning, and the physiotherapy equipment is controlled to enter a soothing mode or stop working.

[0167] When the blood oxygen saturation rate changes by ≥10%, the user is tested to determine whether they are engaging in aerobic exercise.

[0168] When the user is performing anaerobic exercise, the waveform corresponding to the physiotherapy mode is a wide waveform.

[0169] When a user is doing aerobic exercise, the waveform corresponding to the physiotherapy mode is a narrow waveform.

[0170] See Figure 5 , Figure 5 This is a schematic diagram of a process for detecting user microcirculation provided in an embodiment of this application.

[0171] First, the user's blood oxygen saturation rate is detected. When the blood oxygen saturation rate is >0, the user's microcirculation index is detected. When the microcirculation index is >0, it is determined that the user is in the normal exercise mode and the user is suitable for exercise. When the microcirculation index is ≤0, it is determined that the user is in the abnormal exercise mode and the intermediate index B is output.

[0172] When the blood oxygen saturation rate is 0, the user's microcirculation index is detected. When the microcirculation index is >0, it is determined that the user is in the normal exercise mode and the user is suitable for exercise. When the microcirculation index is ≤0, it is determined that the user is in the abnormal exercise mode and the intermediate index A is output.

[0173] When a user is in an abnormal exercise mode, the system uses the HRV data algorithm to calculate the user's heart rate variability based on the output intermediate index, and determines whether the user has exercise risks. If there are risks, the system controls the physiotherapy device to enter a soothing mode or stop working.

[0174] In some alternative embodiments, the controller 101 is further configured to:

[0175] When the duration of physiotherapy on the user using the current physiotherapy strategy reaches the second preset duration, the metabolic index of the user at the current moment is obtained based on the real-time physiological data.

[0176] Calculate the difference between the user's current metabolic index and a preset metabolic index, and update the user's physiotherapy strategy based on the difference.

[0177] Therefore, during the physiotherapy process, the physiotherapy strategy is not static, but is in a dynamic updating process. When the duration of a physiotherapy strategy reaches the second preset duration, the difference between the user's current metabolic index and the preset metabolic index is calculated. Based on this difference, the user's physiotherapy strategy is updated. In this way, the physiotherapy strategy can be updated with the preset metabolic index as a guide, providing the user with precise electrical stimulation.

[0178] This application does not limit the second preset duration and preset metabolic index. The second preset duration is, for example, 1 minute, 2 minutes, or 10 minutes. The preset metabolic index is, for example, 10, 20, or 30.

[0179] In some optional embodiments, the physiotherapy strategy is also used to indicate a physiotherapy intensity threshold corresponding to the physiotherapy mode;

[0180] The controller 101 is also configured to output corresponding electrical stimulation pulses to the user's body surface using the electrode pads 102 in the following manner:

[0181] Receive adjustment commands for the physiotherapy strategy using an interactive device;

[0182] When the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is not greater than the physiotherapy intensity threshold, the electrode 102 outputs the corresponding electrical stimulation pulse to the user's body surface according to the adjusted physiotherapy strategy.

[0183] Therefore, users can adjust the physiotherapy strategy, but this adjustment is not unlimited and needs to be controlled within a certain range. Most users do not have an intuitive understanding of the physiotherapy intensity and may not be able to make precise adjustments. By setting a physiotherapy intensity threshold, the adjustment is only effective when the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is not greater than the physiotherapy intensity threshold. This can prevent users from setting the physiotherapy intensity too high and ensure the safety of users.

[0184] Physiotherapy intensity can be expressed as strong, medium, or weak, or it can be expressed as a numerical level. The higher the level, the stronger the intensity. For example, physiotherapy intensity can include levels 1 to 9.

[0185] This application does not limit the threshold for physiotherapy intensity; it can be either "strong" or "level 6".

[0186] This application does not limit the interactive device, which may be a mobile phone, tablet computer (PAD), desktop computer, smart wearable device, etc.

[0187] In some alternative embodiments, the controller 101 is further configured to:

[0188] When the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is greater than the physiotherapy intensity threshold, perform any of the following operations:

[0189] Stop delivering electrical stimulation pulses to the user's body surface;

[0190] A prompt message is generated and sent to the interactive device.

[0191] Therefore, when the physiotherapy intensity corresponding to the adjusted physiotherapy strategy exceeds the physiotherapy intensity threshold, the output of electrical stimulation pulses is stopped or a prompt message is generated and sent to the interactive device. This can further prevent users from setting excessively high physiotherapy intensity and ensure user safety.

[0192] This application does not limit the type of prompt information, which may be one or more of text, images, audio, and video.

[0193] In a specific application, the prompt message might be something like, "The physiotherapy intensity is too high; it is recommended to reduce the intensity."

[0194] See Figure 6 This application also provides a control method for a physiotherapy device, which provides a baseline electrical stimulation physiotherapy function for a user in motion. The physiotherapy device includes multiple electrode pads and a controller. The method includes steps S101 to S105.

[0195] Step S101: Obtain the user's real-time physiological data;

[0196] The real-time physiological data includes one or more of the following: electrocardiogram data, heart rate data, heart rate variability data, blood oxygen data, microcirculation data, electromyography data, electrooculography data, body temperature data, skin humidity data, and skin pigmentation data.

[0197] Step S102: Based on the real-time physiological data, obtain the user's metabolic index;

[0198] Step S103: Based on the user's metabolic index within a first preset time period, obtain the trend parameters of the user's metabolic index;

[0199] Step S104: Based on the trend parameters of the user's metabolic index, obtain the physiotherapy strategy corresponding to the user. The physiotherapy strategy is used to indicate the physiotherapy site, physiotherapy mode and corresponding physiotherapy intensity.

[0200] Step S105: Based on the user's physiotherapy strategy, use multiple electrode pads to output corresponding electrical stimulation pulses to the user's body surface to change the user's metabolic index.

[0201] In some optional embodiments, the physiotherapy mode corresponds to one or more of the following stimulation types: EMS low-frequency electromyography stimulation, EMS sub-intermediate frequency smooth muscle stimulation, EMS intermediate frequency nerve stimulation, EMS intermediate frequency unbalanced pulse alternating electric field stimulation, and RF radiofrequency subcutaneous elastic tissue stimulation.

[0202] The stimulation parameters for each type of stimulation include one or more of the following: frequency, pulse width, amplitude, waveform, duty cycle, and power.

[0203] See Figure 7 In some optional embodiments, step S102 may include steps S201 to S203.

[0204] Step S201: Obtain real-time physiological data of multiple sample objects and their corresponding metabolic index annotation data. The real-time physiological data of each sample object is obtained by actual measurement or generated by the generative network of the GAN model.

[0205] Step S202: Based on the real-time physiological data of the multiple sample objects and their corresponding labeled metabolic index data, a preset deep learning model is used for training to obtain a metabolic index model.

[0206] Step S203: Input the user's real-time physiological data into the metabolic index model to obtain the user's metabolic index.

[0207] See Figure 8 In some optional embodiments, step S103 may include steps S301 to S302.

[0208] Step S301: Based on the user's metabolic index within a first preset time period, a metabolic index curve is plotted, where each point on the metabolic index curve indicates the user's metabolic index at the corresponding time.

[0209] Step S302: Differentiate the metabolic index curve to obtain the metabolic index trend parameter curve of the user within the first preset time period. Each point on the metabolic index trend parameter curve is used to indicate the trend parameter of the user's metabolic index at the corresponding time.

[0210] See Figure 9 In some optional embodiments, step S104 may include steps S401 to S402.

[0211] Step S401: Obtain the user's personal information, which includes one or more of the following: age, gender, BMI, body fat percentage, and medical history;

[0212] Step S402: Based on the user's personal information and the trend parameters of the user's metabolic index, obtain the physiotherapy strategy corresponding to the user.

[0213] See Figure 10 In some optional embodiments, the method may further include steps S106 to S107.

[0214] Step S106: When the duration of physiotherapy on the user with the current physiotherapy strategy reaches the second preset duration, the metabolic index of the user at the current moment is obtained based on the real-time physiological data.

[0215] Step S107: Calculate the difference between the user's metabolic index at the current moment and the preset metabolic index, and update the user's physiotherapy strategy based on the difference.

[0216] In some optional embodiments, the physiotherapy strategy is also used to indicate a physiotherapy intensity threshold corresponding to the physiotherapy mode;

[0217] See Figure 11 Step S105 may include steps S501 to S502.

[0218] Step S501: Receive adjustment operations for the physiotherapy strategy using an interactive device;

[0219] Step S502: When the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is not greater than the physiotherapy intensity threshold, the electrode pads are used to output the corresponding electrical stimulation pulses to the user's body surface according to the adjusted physiotherapy strategy.

[0220] See Figure 12 In some optional embodiments, the method may further include step S503.

[0221] Step S503: When the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is greater than the physiotherapy intensity threshold, perform any of the following operations:

[0222] Stop delivering electrical stimulation pulses to the user's body surface;

[0223] A prompt message is generated and sent to the interactive device.

[0224] See Figure 13 This application also provides a physiotherapy system 300, which includes a sensor 200 and any of the aforementioned physiotherapy devices 100. The physiotherapy device 100 includes a plurality of electrode pads 102.

[0225] The sensor 200 may include one or more of the following: a pulse wave sensor, a blood oxygen sensor, a heart rate sensor, a temperature sensor, and a skin humidity sensor.

[0226] This application does not limit the physiotherapy device 100. The physiotherapy device 100 can be in the form of a wearable sports vest, shorts, knee pads, neck warmer, socks, etc. The physiotherapy device 100 can be worn locally or as a whole body device.

[0227] This application describes the invention from the perspectives of purpose, performance, progress, and novelty, and it meets the functional enhancement and use requirements emphasized by the Patent Law. The above description and drawings are merely preferred embodiments of this application and are not intended to limit this application. Therefore, all structures, devices, features, etc., that are similar to or identical to those of this application, i.e., all equivalent substitutions or modifications made in accordance with the scope of this patent application, shall fall within the scope of protection of this patent application.

Claims

1. A physiotherapy device, characterized in that, The therapeutic device, used to provide baseline electrical stimulation therapy for users in motion, includes multiple electrode pads and a controller configured to: Obtain the user's real-time physiological data; Based on the real-time physiological data, the user's metabolic index is obtained; Based on the user's metabolic index within a first preset time period, obtain the trend parameters of the user's metabolic index; Based on the trend parameters of the user's metabolic index, the corresponding physiotherapy strategy for the user is obtained. The physiotherapy strategy is used to indicate the physiotherapy site, physiotherapy mode and corresponding physiotherapy intensity. Based on the user's physiotherapy strategy, multiple electrode pads are used to output corresponding electrical stimulation pulses to the user's body surface in order to change the user's metabolic index; The controller is further configured to obtain the user's metabolic index in the following manner: Acquire real-time physiological data of multiple sample objects and their corresponding metabolic index annotation data, wherein the real-time physiological data of each sample object is obtained by actual measurement or generated by the generative network of the GAN model; Based on the real-time physiological data of the multiple sample objects and their corresponding metabolic index annotation data, a metabolic index model is obtained by training a preset deep learning model. The user's real-time physiological data is input into the metabolic index model to obtain the user's metabolic index; The controller is further configured to acquire the trend parameters of the user's metabolic index in the following manner: Based on the user's metabolic index within a first preset time period, a metabolic index curve is plotted, and each point on the metabolic index curve is used to indicate the user's metabolic index at the corresponding time. The metabolic index curve is differentiated to obtain the metabolic index trend parameter curve of the user within the first preset time period. Each point on the metabolic index trend parameter curve is used to indicate the trend parameter of the user's metabolic index at the corresponding time.

2. The physiotherapy device according to claim 1, characterized in that, The physiotherapy mode corresponds to one or more of the following stimulation types: EMS low-frequency electromyography stimulation, EMS sub-intermediate frequency smooth muscle stimulation, EMS intermediate frequency nerve stimulation, EMS intermediate frequency unbalanced pulse alternating electric field stimulation, and RF radiofrequency subcutaneous elastic tissue stimulation. The stimulation parameters for each type of stimulation include one or more of the following: frequency, pulse width, amplitude, waveform, duty cycle, and power.

3. The physiotherapy device according to claim 1, characterized in that, The controller is further configured to obtain the user's corresponding physiotherapy strategy in the following manner: Obtain the user's personal information, which includes one or more of the following: age, gender, BMI, body fat percentage, and medical history. Based on the user's personal information and the trend parameters of the user's metabolic index, the corresponding physiotherapy strategy for the user is obtained.

4. The physiotherapy device according to claim 1, characterized in that, The controller is also configured to: When the duration of physiotherapy on the user using the current physiotherapy strategy reaches the second preset duration, the metabolic index of the user at the current moment is obtained based on the real-time physiological data. Calculate the difference between the user's current metabolic index and a preset metabolic index, and update the user's physiotherapy strategy based on the difference.

5. The physiotherapy device according to claim 1, characterized in that, The physiotherapy strategy is also used to indicate the physiotherapy intensity threshold corresponding to the physiotherapy mode; The controller is also configured to output corresponding electrical stimulation pulses to the user's body surface using the plurality of electrode pads in the following manner: Receive adjustment commands for the physiotherapy strategy using an interactive device; When the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is not greater than the physiotherapy intensity threshold, the multiple electrode pads are used to output corresponding electrical stimulation pulses to the user's body surface according to the adjusted physiotherapy strategy.

6. The physiotherapy device according to claim 5, characterized in that, The controller is also configured to: When the physiotherapy intensity corresponding to the adjusted physiotherapy strategy is greater than the physiotherapy intensity threshold, perform any of the following operations: Stop delivering electrical stimulation pulses to the user's body surface; A prompt message is generated and sent to the interactive device.

7. A physiotherapy system, characterized in that, The physiotherapy system includes sensors and the physiotherapy device according to any one of claims 1-6.

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