Body carving slimming method for fat metabolism based on bioelectricity feedback and acupoint stimulation

Abstract

The invention relates to the technical field of rehabilitation medicine, in particular to a body carving slimming method for fat metabolism based on bioelectricity feedback and acupoint stimulation. The invention provides a body carving slimming method based on bioelectricity feedback and acupoint stimulation. The body carving slimming method comprises the four steps of obtaining, processing, acupoint stimulation and outputting. Bioelectricity signals and weight data of body parts of a user are collected through a bioelectricity and weight detection device. The muscle activity state and fat distribution are analyzed, a posture improvement value is calculated, and a personalized body carving training scheme is generated. In an acupoint stimulation link, electrical stimulation is performed on a specific acupoint according to the scheme, bioelectricity feedback is monitored in real time, and stimulation parameters are dynamically adjusted. And generating a personalized report containing the posture change and the training effect evaluation, and providing suggestions to the user. According to the method, the effect, safety and individuation are remarkably improved, and a new direction is opened up for development of the body carving and slimming field.

Description

Technical Field

[0001] The invention relates to the technical field of rehabilitation medicine, in particular to a body sculpting and slimming method based on bioelectric feedback and acupoint stimulation on fat metabolism. Background Art

[0002] As people's pursuit of healthy life and ideal body shape grows day by day, body sculpting and slimming methods are constantly being innovated. Traditional body sculpting and slimming methods mainly rely on diet control and aerobic exercise. Although simple and easy to implement, they are often slow in effect and difficult to accurately shape specific parts. In recent years, electrical stimulation technology has been widely used in the field of body sculpting and slimming. By applying electric current stimulation to specific muscle groups, fat decomposition and muscle shaping are promoted. However, such methods often use fixed stimulation parameters, which are difficult to adjust according to individual differences and real-time physical conditions, and are prone to insufficient or excessive stimulation.

[0003] On the other hand, TCM acupoint therapy has a long history of rich practical experience in regulating human metabolism. Some studies have tried to apply acupoint stimulation to body sculpting and weight loss, but due to the lack of precise positioning and dosage control methods, the effect often varies from person to person, making it difficult to achieve a stable and reliable weight loss effect. In addition, existing body sculpting and weight loss methods generally lack real-time monitoring and feedback mechanisms for the user's physical condition, making it difficult to ensure the safety and effectiveness of long-term use.

[0004] Although bioelectric feedback technology has been applied in some medical fields, attempts to combine it with body sculpting and weight loss are still relatively limited. Some existing bioelectric feedback devices are mainly used for stress relief or muscle training, and have not fully realized their potential in regulating fat metabolism.

[0005] In summary, existing body sculpting slimming methods generally have problems such as inaccurate effects, low personalization, difficult to ensure safety, and poor long-term effects. Therefore, there is an urgent need for a body sculpting slimming method that can combine bioelectric feedback technology and acupoint stimulation principles to achieve accurate, safe, and efficient results. Summary of the invention

[0006] The present invention aims to solve the above technical problems existing in the existing body sculpting and slimming methods. By innovatively combining bioelectric feedback technology and acupoint stimulation principle, the present invention provides a body sculpting and slimming method based on bioelectric feedback and acupoint stimulation on fat metabolism, which can achieve personalized and precise body sculpting and slimming effects, while ensuring the safety and long-term effectiveness of the use process.

[0007] The present invention proposes a body sculpting and slimming method based on bioelectric feedback and acupoint stimulation on fat metabolism, comprising:

[0008] The acquisition steps include:

[0009] Obtaining bioelectric signal data of multiple body parts of the user through a bioelectric detection device;

[0010] Acquiring the user's weight data through a weight detection device;

[0011] Processing steps include:

[0012] Analyzing the user's muscle activity status and fat distribution based on the bioelectric signal data;

[0013] Calculating a posture improvement value of the user according to the weight data and the muscle activity state;

[0014] Generate a personalized body sculpture training program based on the body posture improvement value and the fat distribution;

[0015] Acupoint stimulation steps include:

[0016] According to the personalized body sculpture training program, the user's specific acupoints are electrically stimulated by an acupoint stimulation device;

[0017] While performing the electrical stimulation, real-time monitoring of bioelectric feedback information of the user's acupuncture points;

[0018] Dynamically adjusting the parameters of the electrical stimulation based on the bioelectric feedback information;

[0019] Output steps include:

[0020] Generate personalized reports including user's body shape change data and training effect evaluation;

[0021] Provide users with personalized diet and exercise recommendations.

[0022] Preferably, the obtaining step specifically includes:

[0023] The bioelectric signals of the user's left arm, right arm, left leg and right leg are collected respectively through multiple sets of electrode arrays;

[0024] The weight detection device continuously collects the user's weight change data during the training process.

[0025] Preferably, analyzing the muscle activity state of the user in the processing step specifically includes:

[0026] Performing spectrum analysis on the bioelectric signal data to extract the main frequency of the current;

[0027] Based on the main frequency of the current, determining the activity level of muscles in various parts of the user;

[0028] The muscle proportions of various parts of the user are calculated according to the muscle activity levels.

[0029] Preferably, analyzing the user's fat distribution in the processing step specifically includes:

[0030] Performing amplitude analysis on the bioelectric signal data to extract current amplitude characteristics;

[0031] Calculating the fat thickness of each part of the user based on the current amplitude characteristics;

[0032] The user's abdominal fat content is estimated according to the fat thickness and the weight data.

[0033] Preferably, the acupoint stimulation step specifically includes:

[0034] According to the personalized body sculpture training program, select specific acupuncture points that need to be stimulated;

[0035] Applying a low-frequency pulse current to the specific acupuncture point through an adjustable electrode array;

[0036] Real-time monitoring of the electrical energy absorption of the specific acupuncture points;

[0037] Based on the electric energy absorption situation, the frequency and intensity of the low-frequency pulse current are dynamically adjusted.

[0038] Preferably, the specific acupoints include at least one of Shenque, Zhongwan, Tianshu, Liangmen and Shangqiu.

[0039] As a preferred embodiment, the cloud data processing step is also included:

[0040] Upload the user's training data to the cloud server;

[0041] Analyze users’ long-term training effects through deep learning algorithms;

[0042] Based on the analysis results, the personalized body sculpture training program is optimized.

[0043] Preferably, generating a personalized report in the output step specifically includes:

[0044] Calculate the user's posture improvement curve;

[0045] Analyze the user's fat metabolism efficiency;

[0046] Assess the user's overall health.

[0047] As a preferred embodiment, it also includes a safety monitoring step:

[0048] Real-time monitoring of user's heart rate and blood pressure data;

[0049] When the heart rate or blood pressure data exceeds a preset safety range, the acupoint stimulation intensity is automatically reduced or the stimulation is stopped.

[0050] As a preference, an expert evaluation step is also included:

[0051] Send users’ training data and personalized reports to professional health consultants;

[0052] Receive evaluation opinions from the professional health consultant;

[0053] Based on the evaluation opinions, the personalized body sculpting training program and diet and exercise recommendations are further adjusted.

[0054] The beneficial effects of the present invention are mainly reflected in the following aspects:

[0055] First, the present invention can accurately assess the user's muscle activity status and fat distribution by real-time collection and analysis of the user's bioelectric signals. This dynamic monitoring mechanism provides a scientific basis for formulating personalized body sculpting plans and greatly improves the accuracy of weight loss effects. Compared with traditional methods, the present invention can achieve local body shaping more quickly and more targeted, especially in difficult-to-reduce areas such as the abdomen.

[0056] Secondly, the present invention innovatively combines acupoint stimulation technology with bioelectric feedback to achieve real-time optimization of stimulation parameters. By monitoring the electrical energy absorption of acupoints, the system can dynamically adjust the stimulation intensity and frequency, ensuring the stimulation effect while avoiding the discomfort that may be caused by overstimulation. This intelligent adjustment mechanism greatly improves the safety and effectiveness of acupoint stimulation, allowing users to use it for a long time without developing tolerance.

[0057] Furthermore, the method of the present invention not only focuses on short-term weight loss effects, but also achieves a significant increase in basal metabolic rate through systematic metabolic regulation. Experimental data show that the metabolic rate of users using this method increased by an average of 7.2%, which is much higher than that of traditional methods. This improvement in metabolic function lays the foundation for users to maintain long-term weight loss effects and effectively solves the problem of easy rebound.

[0058] In addition, the present invention introduces cloud data processing and expert evaluation mechanisms, and continuously optimizes the body sculpture program through big data analysis and artificial intelligence algorithms. This continuous learning and improvement mechanism enables the method to adapt to the changing needs of different users and provide increasingly accurate personalized services. At the same time, the intervention of expert evaluation ensures the scientificity and safety of the program, providing users with all-round health protection.

[0059] Finally, the method of the present invention achieves synergistic effects in all links by comprehensively applying multiple advanced technologies. Bioelectric feedback provides precise positioning and dosage control for acupoint stimulation, and acupoint stimulation enhances the collection effect of bioelectric signals by activating specific meridians. This complementary technical advantages ultimately translates into a weight loss effect and user experience that is significantly superior to traditional methods.

[0060] In general, the body sculpting and slimming method based on bioelectric feedback and acupoint stimulation on fat metabolism provided by the present invention has not only achieved a qualitative leap in effect, safety and personalization, but also pointed out a new direction for the future development of the body sculpting and slimming field. Its application is expected to completely change people's way of losing weight and provide a more scientific and effective solution for people who pursue a healthy life and an ideal body shape. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is the main flow chart of the present invention;

[0062] Figure 2 A detailed flow chart of the acquisition steps of the present invention;

[0063] Figure 3 A detailed flow chart of the processing steps of the present invention;

[0064] Figure 4 is a detailed flow chart of the acupoint stimulation steps of the present invention;

[0065] Figure 5 A detailed flow chart of the output step of the present invention;

[0066] Figure 6 It is a safety monitoring flow chart of the present invention. DETAILED DESCRIPTION

[0067] Please refer to the attached Figure 1-6 The present invention provides a body sculpting slimming method based on bioelectric feedback and acupoint stimulation on fat metabolism. The method achieves accurate and efficient body sculpting slimming effect by innovatively combining bioelectric detection technology and traditional Chinese medicine acupoint stimulation theory.

[0068] The method of the present invention comprises an acquisition step, a processing step, an acupoint stimulation step and an output step.

[0069] In the acquisition step, the method acquires bioelectric signal data of multiple body parts of the user through a bioelectric detection device, and acquires the user's weight data through a weight detection device. Preferably, the present invention uses multiple groups of electrode arrays to respectively acquire bioelectric signals of the user's left arm, right arm, left leg, and right leg. This multi-point acquisition method can fully reflect the muscle activity status and fat distribution of the user's entire body.

[0070] In the processing step, the method first analyzes the user's muscle activity status and fat distribution based on the acquired bioelectric signal data. Specifically, the present invention uses spectrum analysis technology to extract the main current frequency f0 of the bioelectric signal, and its calculation formula is:

[0071] f0=argmax f |X(f)| 2 ,

[0072] Among them, X(f) is the Fourier transform of the bioelectric signal.

[0073] Based on the main current frequency f0, this method determines the activity level of muscles in various parts of the user. For example, when f0 falls within the range of 20-50Hz, the muscles are determined to be active. This is because the normal discharge frequency of human muscles is usually within this range.

[0074] Next, this method calculates the user's posture improvement value Q based on the weight data and muscle activity status. The calculation formula of the posture improvement value is:

[0075]

[0076] Among them, ΔW is the weight change, W0 is the initial weight, Δf is the change in muscle activity frequency, and α and β are weight coefficients. Usually, we take α = 0.6, β = 0.4 to balance the effects of weight change and muscle activity.

[0077] Based on the posture improvement value Q and fat distribution, this method generates a personalized body sculpture training program. The program will formulate targeted acupoint stimulation strategies and exercise suggestions according to the specific situation of the user.

[0078] In the acupoint stimulation step, the method electrically stimulates the user's specific acupoints through an acupoint stimulation device according to a personalized body sculpture training program. Preferably, the present invention selects acupoints such as Shenque, Zhongwan, Tianshu, Liangmen and Shangqiu for stimulation, which are considered to be closely related to fat metabolism in traditional Chinese medicine theory.

[0079] While conducting electrical stimulation, the method monitors the bioelectric feedback information of the user's acupoints in real time and dynamically adjusts the parameters of the electrical stimulation based on this information. For example, when the resistance value $R at the acupoint is detected to be lower than 80% of the initial value, the intensity of the stimulation current can be appropriately reduced to avoid discomfort.

[0080] In the output step, the method generates a personalized report containing the user's body shape change data and training effect evaluation, and outputs personalized diet and exercise recommendations to the user. These recommendations are customized according to the user's physical characteristics and training effects to achieve the best weight loss effect.

[0081] The acquisition step of the present invention specifically includes respectively collecting bioelectric signals of the user's left arm, right arm, left leg and right leg through multiple groups of electrode arrays, and continuously collecting the user's weight change data during training through a weight detection device.

[0082] Preferably, the present invention uses an eight-channel bioelectric acquisition system, with two acquisition channels for each limb. This configuration can capture the electrical activity of both surface and deep muscles at the same time, improving the comprehensiveness and accuracy of the data. The weight detection device uses a high-precision pressure sensor, which can record slight changes in the user's weight in real time, with a sensitivity of up to 0.1kg.

[0083] When analyzing the muscle activity state of the user in the processing step, the present invention specifically includes performing spectrum analysis on bioelectric signal data to extract the main frequency of the current; judging the activity level of muscles in various parts of the user based on the main frequency of the current; and calculating the muscle proportions in various parts of the user according to the muscle activity levels.

[0084] In the spectrum analysis process, this method uses the fast Fourier transform (FFT) algorithm, whose computational complexity is O(nlogn), which can efficiently extract the frequency characteristics of the signal. The extraction formula of the current main frequency f0 is as follows:

[0085] f0=argmax f |X(f)| 2 ,

[0086] Among them, X(f) is the Fourier transform of the bioelectric signal.

[0087] This method sets different frequency thresholds when judging muscle activity. For example:

[0088] When f0 < 20 Hz, the muscle is judged to be in a resting state;

[0089] When 20Hz≤f0<50Hz, the muscle is judged to be in a normal activity state;

[0090] When f0 ≥ 50 Hz, the muscle is judged to be in a highly active state;

[0091] These thresholds are set based on a large amount of clinical data and exercise physiology research results.

[0092] When calculating muscle ratio, this method introduces the muscle activity index MAI (Muscle Activity Index), and its calculation formula is:

[0093]

[0094] Among them, f min and f maxThey are the minimum and maximum frequencies of the muscles in that part. By comparing the MAI values ​​of different parts, the relative muscle proportion of each part can be obtained.

[0095] Through the above steps, the present invention can comprehensively and accurately evaluate the user's muscle activity status, and provide reliable data support for the subsequent formulation of personalized body sculpting training programs. Compared with traditional body shape measurement, this analysis method based on bioelectric feedback can more directly reflect the actual muscle activity, thereby achieving a more accurate body sculpting effect.

[0096] When analyzing the fat distribution of the user in the processing step, the present invention specifically includes performing amplitude analysis on the bioelectric signal data to extract the current amplitude characteristics; calculating the fat thickness of various parts of the user based on the current amplitude characteristics; and estimating the user's abdominal fat content based on the fat thickness and weight data.

[0097] When performing amplitude analysis, the method uses envelope detection technology to extract the amplitude characteristics of the bioelectric signal. Preferably, the original signal is processed using Hilbert transform to obtain the analytical envelope of the signal. The calculation formula of the current amplitude characteristic A is:

[0098]

[0099] Among them, s(t) is the original signal, is its Hilbert transform.

[0100] Based on the extracted current amplitude characteristics, this method estimates the fat thickness of each part of the user by establishing a regression model. The calculation formula of fat thickness D is:

[0101]

[0102] Wherein, k1, k2 and k3 are regression coefficients obtained by fitting a large amount of experimental data. Preferably, for the abdominal region, k1 = 15, k2 = 0.8, k3 = 2. This set of parameters shows good estimation accuracy in practical applications.

[0103] When estimating abdominal fat content, this method takes into account fat thickness and weight data. The calculation formula for abdominal fat content F is:

[0104]

[0105] Where W is the user’s current weight, W i deal is ideal body weight, H is height, α and β are weight coefficients. Usually, α = 0.7, β = 0.3 to balance the influence of local fat thickness and overall body weight.

[0106] Through the above analysis, the present invention can accurately evaluate the user's fat distribution, especially the abdominal fat content, which provides an important basis for formulating targeted body sculpting plans and helps to achieve a more accurate weight loss effect.

[0107] The acupoint stimulation step of the present invention specifically includes selecting specific acupoints that need to be stimulated according to a personalized body sculpture training program; applying low-frequency pulse current to the specific acupoints through an adjustable electrode array; real-time monitoring of the electrical energy absorption of the specific acupoints; and dynamically adjusting the frequency and intensity of the low-frequency pulse current based on the electrical energy absorption.

[0108] When selecting acupoints for stimulation, the method selects the most suitable acupoint combination from a preset acupoint library according to the user's fat distribution and body sculpture goals. Preferably, the acupoint library of the present invention includes but is not limited to important acupoints related to fat metabolism such as Shenque, Zhongwan, Tianshu, Liangmen and Shangqiu.

[0109] For selected acupoints, this method uses an adjustable electrode array for precise positioning and stimulation. The electrode array consists of multiple tiny electrode units, each of which can be controlled independently. This design allows the system to adjust the stimulation range and intensity based on real-time feedback to accommodate users of different body types.

[0110] When applying low-frequency pulse current, this method uses a biphasic square wave pulse, and its parameter range is usually:

[0111] Frequency: 1-100Hz,

[0112] Pulse width: 50-500μs,

[0113] Current intensity: 0-50mA,

[0114] These parameter ranges were selected based on extensive clinical research data to effectively stimulate acupuncture points without causing discomfort or skin irritation.

[0115] This method monitors the electrical energy absorption of acupoints in real time, mainly by measuring the impedance change of the electrode-skin interface. The calculation formula of the electrical energy absorption rate E is:

[0116]

[0117] Where V is the applied voltage, R is the measured skin impedance, and t is the stimulation time.

[0118] Based on the energy absorption, this method dynamically adjusts the parameters of the pulse current. For example, when the energy absorption rate E is detected to decrease, the system will appropriately increase the current intensity or adjust the frequency to maintain an effective stimulation effect. The adjustment strategy follows the following principles:

[0119] 1.When E <E thresholdWhen the current intensity is increased, the increase is ΔI=k·(E threshold -E);

[0120] 2.When E>E max When the current intensity is reduced, the reduction range is ΔI=k·(E max -E);

[0121] 3.When E threshold ≤E≤E max When , keep the current parameters unchanged;

[0122] Among them, E threshold and E max are the preset minimum effective absorption rate and maximum safe absorption rate respectively, and k is the adjustment coefficient.

[0123] This dynamic adjustment mechanism ensures the continued effectiveness and safety of acupoint stimulation and is one of the key technologies for achieving precise body sculpture in the present invention.

[0124] The specific acupuncture points of the present invention include at least one of Shenque, Zhongwan, Tianshu, Liangmen and Shangqiu, which are considered to be closely related to fat metabolism and body shape regulation in traditional Chinese medicine theory.

[0125] Preferably, this method uses different stimulation parameters according to the characteristics and functions of different acupoints:

[0126] 1. Shenque point: frequency 20-30Hz, pulse width 200μs, helps regulate whole body metabolism;

[0127] 2. Zhongwan acupoint: frequency 15-25Hz, pulse width 250μs, promotes gastrointestinal function and accelerates fat decomposition;

[0128] 3. Tianshu acupoint: frequency 10-20Hz, pulse width 300μs, improves abdominal blood circulation and promotes fat metabolism;

[0129] 4. Liangmen acupoint: frequency 5-15Hz, pulse width 350μs, regulate endocrine and balance metabolism;

[0130] 5. Shangqiu acupoint: frequency 30-40Hz, pulse width 150μs, enhances spleen and stomach function and promotes water metabolism;

[0131] The selection of these parameters is based on a large amount of clinical experimental data, which can maximize the effect of acupoint stimulation while ensuring safety.

[0132] The present invention also includes a cloud data processing step, which includes uploading the user's training data to a cloud server; analyzing the user's long-term training effect through a deep learning algorithm; and optimizing a personalized body sculpture training program based on the analysis results.

[0133] During the data upload process, this method uses encrypted transmission technology to ensure the security of user data. Preferably, the data is encrypted using the AES-256 encryption algorithm and then transmitted to the cloud server via the HTTPS protocol.

[0134] After receiving the data, the cloud server will first clean and preprocess the data. This includes removing outliers, filling missing values, standardizing, etc. The preprocessed data will be input into the deep learning model for analysis.

[0135] This method uses a deep learning model based on long short-term memory network (LSTM) to analyze the long-term training effect of users. The structure of the model is as follows:

[0136] 1. Input layer: receives the user's time series training data;

[0137] 2. LSTM layer: captures the long-term dependencies of data, with 3 layers and 128 neurons in each layer; 3. Fully connected layer: maps the output of the LSTM layer to the final prediction result;

[0138] 4. Output layer: predict the user's future body shape change trend;

[0139] The loss function of the model uses mean square error (MSE), and the optimization algorithm uses Adam optimizer. During the training process, the early stopping method is used to prevent overfitting.

[0140] Based on the analysis results of the deep learning model, this method optimizes the personalized body sculpture training program through a reinforcement learning algorithm. The optimization goal is to maximize the expected body shape improvement effect while considering user acceptance and sustainability. The optimization algorithm uses a deep Q network (DQN), and its reward function $R is defined as:

[0141] R=w1·ΔQ+w2·C-w3·D,

[0142] Among them, ΔQ is the increment of posture improvement value, C is the user's compliance, D is the difficulty coefficient of the program, and w1, w2 and w3 are weight coefficients.

[0143] Through this cloud data processing and optimization mechanism, the present invention can continuously improve the effect of the body sculpting training program and provide users with a long-term and effective weight loss solution. This data-driven method greatly improves the accuracy and personalization of the body sculpting process, which is one of the important advantages of the present invention compared to traditional methods.

[0144] The output step of the present invention generates a personalized report, which specifically includes calculating the user's body shape improvement curve, analyzing the user's fat metabolism efficiency, and evaluating the user's overall health. These steps are intended to provide users with comprehensive and intuitive feedback on training effects, which helps to improve user participation and sustainability.

[0145] When calculating the posture improvement curve, this method uses time series analysis technology. Preferably, the exponential smoothing method is used to process the original data to eliminate the influence of short-term fluctuations. The calculation formula of the posture improvement index $I_t is:

[0146] I t =α·Q t +(1-α)·I t-1 ,

[0147] Among them, Q t is the current posture improvement value, I t-1 is the posture improvement index of the previous moment, and α is the smoothing coefficient. Usually, α is set to 0.2-0.3, which can effectively balance short-term changes and long-term trends.

[0148] This method also introduces a trend prediction function, using the ARIMA (Autoregressive Integrated Moving Average) model to predict future body shape changes. The prediction results will be presented on the body shape improvement curve in the form of confidence intervals, providing users with a possible development trend reference.

[0149] When analyzing fat metabolism efficiency, the present invention adopts a multi-factor comprehensive evaluation method. The evaluation indicators include but are not limited to:

[0150] 1. Fat consumption rate: the amount of fat lost per unit time;

[0151] 2. Metabolic activity: metabolic level reflected by changes in bioelectric signal intensity;

[0152] 3. Changes in body fat distribution: relative changes in fat thickness in different parts of the body;

[0153] The calculation formula of fat metabolism efficiency index E is:

[0154] E=w1·R f +w2·A m +w3·ΔD,

[0155] Among them, R f is the fat consumption rate, A m is metabolic activity, ΔD is the change in body fat distribution, w1, w2 and w3 are weight coefficients. Preferably, w1 = 0.5, w2 = 0.3, w3 = 0.2. This set of weights shows good evaluation effect in practice.

[0156] When evaluating the user's overall health status, this method takes into account multiple physiological indicators, including but not limited to heart rate variability, blood pressure, body mass index (BMI), etc. The calculation formula for the health status score $H is:

[0157]

[0158] Among them, X i is the actual value of the ith physiological index, Its ideal value, k i is the weight coefficient. This scoring method can intuitively reflect the user's health status and point out the direction that needs improvement.

[0159] The personalized report generated through the above steps can not only fully reflect the training effect of the user, but also provide the user with scientific and feasible improvement suggestions. This data-driven feedback mechanism is one of the key factors for improving user compliance and training effect of the present invention.

[0160] The present invention also includes a safety monitoring step: real-time monitoring of the user's heart rate and blood pressure data; when the heart rate or blood pressure data exceeds a preset safety range, the acupoint stimulation intensity is automatically reduced or the stimulation is stopped. This step is intended to ensure the safety of the user during the training process and is an important safety guarantee mechanism of the method.

[0161] During real-time monitoring, the method uses non-invasive, continuous monitoring technology. Preferably, photoplethysmography (PPG) technology is used to monitor heart rate, and pulse wave transit time (PTT) technology is used to estimate blood pressure. These technologies are easy to operate and user-friendly, and are suitable for long-term continuous monitoring.

[0162] This method sets safe ranges for heart rate and blood pressure. For heart rate, the safe range is usually:

[0163] HR min ≤HR≤HR max ,

[0164] Among them, HR m in=220-age-50,HR max =220-age, where age is the user's age.

[0165] For blood pressure, safe ranges are usually:

[0166] 90≤SBP≤140mmHg (systolic blood pressure);

[0167] 60≤DBP≤90mmHg (diastolic blood pressure);

[0168] When the heart rate or blood pressure is detected to be outside the safe range, this method will trigger the safety response mechanism. The response strategy is as follows:

[0169] 1. Mild excess: reduce acupoint stimulation intensity by 20%;

[0170] 2. Moderate excess: reduce acupoint stimulation intensity by 50%;

[0171] 3. Severely exceeded: Immediately stop acupoint stimulation and sound an alarm to alert the user;

[0172] The formula for reducing the stimulus intensity by $\Delta I is:

[0173] ΔI=k·(XX threshold ),

[0174] Among them, X is the actual measured value, X hreshol is the threshold value, and k is the adjustment coefficient. Preferably, k is set to 0.1-0.2, which can achieve smooth intensity adjustment.

[0175] In addition, this method also introduces a progressive recovery mechanism. When the physiological indicators return to normal, the system will gradually increase the stimulation intensity until it returns to the original level. This mechanism can maximize the training effect while ensuring safety.

[0176] Through this real-time safety monitoring and intelligent adjustment mechanism, the present invention greatly improves the safety of the body sculpting process, allowing users to carry out long-term training with confidence.

[0177] The present invention also includes an expert evaluation step: sending the user's training data and personalized report to a professional health consultant; receiving the professional health consultant's evaluation opinions; and further adjusting the personalized body sculpting training program and diet and exercise recommendations based on the evaluation opinions. This step is intended to combine the experience of artificial experts with intelligent algorithms to further improve the scientificity and personalization of the body sculpting program.

[0178] During the data transmission process, this method adopts strict privacy protection measures. Preferably, multi-layer encryption technology is used to encrypt user data, and blockchain technology is used to ensure that the data cannot be tampered with. These measures can effectively protect the user's privacy information.

[0179] After receiving the user's data, the professional health consultant will conduct a comprehensive assessment. The assessment content includes but is not limited to:

[0180] 1. The rationality of the training plan;

[0181] 2. The user's progress;

[0182] 3. Potential health risks;

[0183] 4. The feasibility of long-term goals;

[0184] The consultant's evaluation opinions will be returned to the system in the form of structured data. This method uses natural language processing (NLP) technology to parse the evaluation opinions and extract key information. The parsing process uses named entity recognition (NER) and relationship extraction technology to identify key entities and relationships in the opinions.

[0185] Based on the analyzed evaluation opinions, this method uses a rule-based expert system to adjust the body sculpture training program. The general form of the adjustment rule is:

[0186] IF [condition] THEN [action]

[0187] For example:

[0188] IF fat metabolism efficiency is lower than expected THEN increase aerobic exercise time AND adjust acupoint stimulation frequency.

[0189] Preferably, the method also introduces a machine learning model to automatically generate adjustment suggestions by learning historical adjustment records. This hybrid method combining rule systems and machine learning can continuously optimize adjustment strategies while ensuring the rationality of adjustments.

[0190] When generating diet and exercise recommendations, this method takes into account the user's personal preferences, living habits, and physical condition. Preferably, a collaborative filtering algorithm is used to recommend suitable recipes and exercise methods for users. The calculation formula for the recommendation similarity $S is:

[0191]

[0192] Among them, r ai and r bi are the ratings of item i by users a and b respectively, and The average rating for each.

[0193] By introducing expert evaluation and intelligent adjustment mechanisms, the present invention realizes the organic combination of artificial intelligence and human experts, greatly improving the scientificity and personalization of body sculpting solutions. This combination can not only provide users with more accurate and effective training guidance, but also timely identify and respond to potential health risks, ensuring that users maintain physical and mental health while pursuing an ideal body shape.

[0194] In order to verify the superiority of the present invention's "body sculpting and slimming method based on bioelectric feedback and acupoint stimulation on fat metabolism", a set of comparative experiments was designed. This experiment simulated a typical body sculpting and slimming application scenario, and selected 30 volunteers (15 males, 15 females, aged 25-45 years old), randomly divided them into three groups, 10 people in each group, and used different slimming methods for 12 weeks of training.

[0195] Embodiment 1: The method of the present invention is used to combine bioelectric feedback and acupoint stimulation to perform body sculpting and weight loss.

[0196] Comparative Example 1: Only traditional aerobic exercise and diet control were used for weight loss.

[0197] Comparative Example 2: Using a common electrical stimulation weight loss device on the market to lose weight, but without bioelectric feedback regulation.

[0198] During the experiment, we focused on the following indicators: weight loss percentage, body fat percentage change, abdominal fat thickness change, metabolic rate change, and user satisfaction. These indicators can fully reflect the weight loss effect, body metabolic changes, and user experience.

[0199] The testing methods and standards are as follows:

[0200] 1. Weight loss percentage: measured using a high-precision electronic scale, accurate to 0.1 kg. Calculation formula: (initial weight × final weight) / initial weight × 100%.

[0201] 2. Change in body fat percentage: measured using bioelectrical impedance (BIA) with an accuracy of 0.1%. Calculate the difference between the final body fat percentage and the initial body fat percentage.

[0202] 3. Changes in abdominal fat thickness: Use B-ultrasound to measure the thickness of abdominal subcutaneous fat, accurate to 0.1mm. Calculate the difference between the final thickness and the initial thickness.

[0203] 4. Change in metabolic rate: Measure basal metabolic rate (BMR) in kcal / day using indirect calorimetry. Calculate the percentage change from the final BMR to the initial BMR.

[0204] 5. User satisfaction: A Likert 5-point scale was used for the questionnaire survey, with 1 being the most dissatisfied and 5 being the most satisfied. The average score was calculated.

[0205] After 12 weeks of experimentation, the following test results were obtained:

[0206] index Example 1 Comparative Example 1 Comparative Example 2 Percentage of weight loss 12.5％ 8.7％ 10.2％ Changes in body fat percentage -4.8％ -3.2％ -3.9％ Changes in abdominal fat thickness -8.6 -5.3 -6.8 Changes in metabolic rate 7.2％ 3.5％ 4.8％ Customer satisfaction 4.7 3.8 4.1

[0207] It can be seen from the test results that the method of the present invention (Example 1) is significantly superior to the traditional method (Comparative Example 1) and the common electrical stimulation method (Comparative Example 2) in all indicators.

[0208] In terms of the percentage of weight loss, Example 1 reached 12.5%, which was 43.7% higher than Comparative Example 1 and 22.5% higher than Comparative Example 2. This shows that the method of the present invention has obvious advantages in promoting weight loss.

[0209] In terms of the change in body fat percentage, the reduction in Example 1 reached 4.8%, which was 1.6 percentage points lower than that in Comparative Example 1 and 0.9 percentage points lower than that in Comparative Example 2. This shows that the method of the present invention can not only effectively reduce weight, but also better target fat reduction.

[0210] The change in abdominal fat thickness is an important indicator for measuring the local slimming effect. The reduction in Example 1 reached 8.6 mm, which was 62.3% more than that in Comparative Example 1 and 26.5% more than that in Comparative Example 2. This result fully proves the excellent effect of the present invention in local body shaping.

[0211] The change in metabolic rate reflects the degree of improvement of the body's metabolic function. Example 1 achieved a 7.2% improvement, which is 2.06 times that of comparative example 1 and 1.5 times that of comparative example 2. This shows that the method of the present invention can more effectively activate body metabolism and is conducive to maintaining a long-term weight loss effect.

[0212] In terms of user satisfaction, Example 1 scored a high score of 4.7, which was significantly higher than 3.8 of Comparative Example 1 and 4.1 of Comparative Example 2. This shows that users are more satisfied with the experience and effect of the method of the present invention.

[0213] This set of experimental results fully proves the superiority of the present invention's "body sculpting and slimming method based on bioelectric feedback and acupoint stimulation on fat metabolism". Its advantages are mainly reflected in the following aspects:

[0214] 1. More efficient weight loss effect: Through precise bioelectric feedback and acupoint stimulation, this method can more effectively activate fat metabolism and achieve faster and more significant weight loss effects.

[0215] 2. More precise local body shaping: Combining bioelectric signal analysis and dynamically adjusted acupoint stimulation, this method can achieve more precise local fat targeted reduction, especially in key areas such as the abdomen.

[0216] 3. More lasting metabolic improvement: This method not only focuses on short-term weight loss effects, but also effectively increases the basal metabolic rate, which helps users maintain an ideal body shape for a long time.

[0217] 4. Better user experience: Through personalized solutions and real-time adjustments, this method can provide users with a more comfortable and effective weight loss experience, thereby achieving higher satisfaction.

[0218] 5. More comprehensive health benefits: This method not only focuses on changes in weight and body shape, but also brings more comprehensive health benefits to users by improving metabolic function.

[0219] In summary, the method of the present invention has shown obvious advantages in terms of effect, experience and health benefits, and provides an innovative solution for the field of body sculpting and weight loss. This method of combining bioelectric feedback and acupoint stimulation can not only meet the user's needs for fast and effective weight loss, but also provide a more scientific and healthier way of body management.

[0220] It should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A body sculpting slimming method based on bioelectric feedback and acupoint stimulation on fat metabolism, characterized in that: include: The acquisition steps include: Obtaining bioelectric signal data of multiple body parts of the user through a bioelectric detection device; Acquiring the user's weight data through a weight detection device; Processing steps include: Analyzing the user's muscle activity status and fat distribution based on the bioelectric signal data; Calculating a posture improvement value of the user according to the weight data and the muscle activity state; Generate a personalized body sculpture training program based on the body posture improvement value and the fat distribution; Acupoint stimulation steps include: According to the personalized body sculpture training program, the user's specific acupoints are electrically stimulated by an acupoint stimulation device; While performing the electrical stimulation, real-time monitoring of bioelectric feedback information of the user's acupuncture points; Dynamically adjusting the parameters of the electrical stimulation based on the bioelectric feedback information; Output steps include: Generate personalized reports including user's body shape change data and training effect evaluation; Provide users with personalized diet and exercise recommendations.

2. The method according to claim 1, characterized in that The acquisition step specifically includes: The bioelectric signals of the user's left arm, right arm, left leg and right leg are collected respectively through multiple sets of electrode arrays; The weight detection device continuously collects the user's weight change data during the training process.

3. The method according to claim 1, characterized in that Analyzing the muscle activity state of the user in the processing step specifically includes: Performing spectrum analysis on the bioelectric signal data to extract the main frequency of the current; Based on the main frequency of the current, determining the activity level of muscles in various parts of the user; The muscle proportions of various parts of the user are calculated according to the muscle activity levels.

4. The method according to claim 1, characterized in that: Analyzing the user's fat distribution in the processing step specifically includes: Performing amplitude analysis on the bioelectric signal data to extract current amplitude characteristics; Calculating the fat thickness of each part of the user based on the current amplitude characteristics; The user's abdominal fat content is estimated according to the fat thickness and the weight data.

5. The method according to claim 1, characterized in that The acupoint stimulation step specifically includes: According to the personalized body sculpture training program, select specific acupuncture points that need to be stimulated; Applying a low-frequency pulse current to the specific acupuncture point through an adjustable electrode array; Real-time monitoring of the electrical energy absorption of the specific acupuncture points; Based on the electric energy absorption situation, the frequency and intensity of the low-frequency pulse current are dynamically adjusted.

6. The method according to claim 5, characterized in that The specific acupuncture points include at least one of Shenque, Zhongwan, Tianshu, Liangmen and Shangqiu.

7. The method according to claim 1, characterized in that It also includes cloud data processing steps: Upload the user's training data to the cloud server; Analyze users’ long-term training effects through deep learning algorithms; Based on the analysis results, the personalized body sculpture training program is optimized.

8. The method according to claim 1, characterized in that Generating a personalized report in the output step specifically includes: Calculate the user's posture improvement curve; Analyze the user's fat metabolism efficiency; Assess the user's overall health.

9. The method according to claim 1, characterized in that: It also includes safety monitoring steps: Real-time monitoring of user's heart rate and blood pressure data; When the heart rate or blood pressure data exceeds a preset safety range, the acupoint stimulation intensity is automatically reduced or the stimulation is stopped.

10. The method according to claim 1, characterized in that Also includes an expert evaluation step: Send users’ training data and personalized reports to professional health consultants; Receive evaluation opinions from the professional health consultant; Based on the evaluation opinions, the personalized body sculpting training program and diet and exercise recommendations are further adjusted.

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