19 results about "BMR measurement" patented technology

The invention discloses a fat reduction management consultation platform and an implementation method thereof, and relates to the technical field of fat reduction management, the fat reduction management consultation platform comprises a data collection module, a basic metabolism module, a body fat trend prediction module, a nutrition intervention module and an exercise intervention module; the data collection module is responsible for collecting user basic sign data including body weight and body fat rate; the exercise frequency, the training type and the diet record of the user are dynamically collected; the basic metabolism module calculates the basal metabolic rate BMR (t) according to the body weight and the body fat rate of the user; generating an activity correction factor AF (t) at each moment according to the heart rate frequency and the training type; a historical calorie intake and body fat change sequence is constructed through the body fat trend prediction module, an XGBoost model is called to predict future body fat change, a final body fat prediction value is output in combination with an error weighted fusion mechanism, whether the user enters a stable period or a fat reduction rebound trend is judged, the body fat change trend of the user is effectively monitored, and the user experience is improved. And the user is effectively helped to know the fat reduction process.

The application discloses an intelligent diet management method and system based on multi-source health data fusion, and relates to the field of health diet management. In order to solve the defects of the existing diet management system, such as data fragmentation, manual input dependence, incapability of generating personalized diet plans and incapability of forming a closed-loop feedback, basic information, medical information and body composition data of a user are acquired; initial health parameters are generated according to the basic information, the medical information and the body composition data of the user, wherein the initial health parameters include basal metabolic rate, total energy consumption and total energy requirement; and the initial health parameters are updated according to dynamic input real-time data, so as to realize intervention and management on diet behaviors, wherein the dynamic input real-time data includes diet data and exercise data. The application is mainly used for intervention and management on diet behaviors according to the results of multi-source health data fusion.

The invention is suitable for the technical field of data recognition, and provides a big data-based body state detection method and device, and the method comprises the steps: collecting high-frequency impedance, low-frequency impedance and a current phase difference through a foot electrode; calculating body composition according to the high-frequency impedance and the low-frequency impedance; wherein the body components comprise body fat percentage, muscle mass, total body moisture, basal metabolic rate and bone mass; calculating the body age according to the body composition; generating body change information according to the body composition and the current phase difference; wherein the body state information comprises body change information and body age. According to the technical scheme, comprehensive measurement of the high-frequency impedance, the low-frequency impedance and the current phase difference is introduced, and analysis and calculation based on big data are carried out, so that comprehensive, accurate and personalized detection of the body state is realized. And the accuracy and the reliability of a measurement result are improved.

The invention discloses a weight losing scheme generation method and system based on food share and exercise data, and the method comprises the steps: obtaining daily recommended intake energy data based on weight data and physical activity type data; distributing the daily recommended intake energy data to obtain share quantity data of each type of food; calculating according to the gender data, the age data, the height data and the weight data to obtain basal metabolic rate data; calculating physical activity energy consumption data according to the daily weight loss target data, the daily recommended intake energy data and the basal metabolic rate data; inputting the physical activity energy consumption data and the expected exercise type data into an energy consumption calculation formula to obtain daily activity duration data; and generating a weight losing plan based on the share quantity data of each type of food, the daily exercise type and the exercise duration data. According to the method, the user can be helped to efficiently reduce weight and maintain body health.

The invention discloses a personalized nutrition scheme generation method and system for height increase, and relates to the field of health management, and the method comprises the steps: collecting static biological data, parent height data, health background questionnaire data and intestinal flora samples of a user, calculating the genetic target height and basal metabolic rate of the user according to the static biological data and the parent height data, generating a personal file of the user; extracting height, weight and leg length data of the user from a personal file of the user, obtaining stress distribution of a lower limb epiphyseal plate of the user under different exercise types through biomechanical simulation, and generating a personalized exercise scheme according to a stress distribution result; and estimating daily energy consumption according to the basal metabolic rate and the personalized exercise scheme to obtain the daily total energy demand of the user. According to the method, the problem of insufficient scheme accuracy and adaptability caused by neglecting individual nutrition absorption difference and biomechanical response characteristics in a traditional method is effectively solved.

The invention provides an infant feeding demand assessment method and device. The method comprises the following steps: acquiring basic physiological information, activity data and nutrition intake information of a to-be-assessed infant; the obtained basic physiological information, activity data and nutrition intake information are input into an energy consumption model, and the energy consumption model determines the basal metabolic rate of the infant based on the weight and gender information and determines the exercise consumption of the infant based on the heart rate, the activity level, the body temperature, the weight and the non-eating duration; total consumed energy of the infant is determined based on the basal metabolic rate and the exercise consumption; the current total intake energy of the infant is obtained through the energy intake model; and inputting the acquired basic physiological information, nutrition intake information, total consumed energy and current intake total energy into a feeding demand evaluation model to obtain a feeding demand evaluation result of the to-be-evaluated infant. According to the invention, real-time feeding demand evaluation and personalized evaluation of infants can be realized, and the accuracy of feeding suggestions is improved.

To propose a technique for providing a menu suitable for the user's health condition.SOLUTION: A basal metabolic rate estimation unit 40 estimates a user's daily basal metabolic rate. An exercise metabolic rate estimation unit 50 estimates the user's daily exercise metabolic rate. A consumption amount estimation unit 60 estimates the user's daily consumption amount based on the estimated basal metabolic rate and the estimated exercise metabolic rate. A menu determination unit 62 determines a menu for the user based on the estimated consumption amount.SELECTED DRAWING: Figure 3

An electrical muscle stimulation (EMS) device for the soleus muscle group includes an electrical stimulation generating unit, a working mode setting module, and an electrical stimulation output control module. The electrical stimulation generating unit generates an electrical stimulation signal. The working mode setting module configures at least one working mode, including configuring the frequency and duration of the electrical stimulation signal. The frequency of the electrical stimulation signal is within a preset frequency range, and the duration of the electrical stimulation signal is within a preset time range. The upper limit of the frequency range is less than a first threshold, and the lower limit of the time range is greater than a second threshold, thereby increasing the basal metabolic rate of the soleus muscle group. The electrical stimulation output control module transmits the electrical stimulation signal to the soleus muscle group according to one of the working modes configured by the working mode setting module. This addresses the technical problem that existing EMS devices have limited effectiveness in increasing basal metabolic rate and struggle to achieve sustained and effective metabolic enhancement.

The invention discloses a continuous metabolism monitoring system and method for critical patients, and belongs to the technical field of energy metabolism monitoring. Comprising an input unit, a monitor, an index standardization unit, a basal metabolic rate model unit and an output unit, and the monitor is used for collecting the body temperature, the body weight, the heart rate and the blood pressure of a patient in real time; the index standardization unit is used for taking the height of the patient and the collected body temperature, body weight, heart rate and blood pressure as basic metabolism indexes; performing data standardization on the collected basic metabolism indexes and removing dimensions to obtain basic metabolism standardized indexes; and the basal metabolic rate model unit is used for obtaining the basal metabolic rate based on the basal metabolic rate model according to the basal metabolic standardized index. Energy metabolism can be continuously monitored, trachea cannula is not needed, and meanwhile the monitoring precision is high.

The invention relates to the technical field of nutrition state evaluation, in particular to an evaluation system and method for a nutrition state, and the method comprises the steps: obtaining the basic information and health data of a patient, and matching a suitable nutrition screening scale; a nutrition screening result is generated through weighted score calculation; performing diet review survey for preset continuous time on the patient with the nutrition risk, calculating nutrient intake level, and generating diet survey data; collecting a biochemical detection result and body measurement data of the patient, combining dietary survey data and basic information, performing comprehensive evaluation through a multi-dimensional evaluation scale, and generating a nutrition evaluation result; the basal metabolic rate is calculated, the nutrition proportion is optimized through a linear programming algorithm, and a nutrition prescription is formulated; integrating the nutrition screening result, the dietary survey data, the body measurement data, the biochemical detection result, the nutrition evaluation result and the nutrition prescription to generate a nutrition evaluation report. According to the invention, individualized and refined nutrition intervention is realized.

The application discloses a bone fracture risk and drug efficacy evaluation system based on bone metabolism-activity-autonomic nerve coupling, and belongs to the fields of digital medical treatment and bone metabolism pharmacology. The application comprises a bone metabolism evaluation module (L23), a fall risk evaluation module (L24), an autonomic nerve evaluation module (L25) and a bone fracture risk evaluation module (L26). The bone metabolism evaluation module (L23) is used for estimating a basal metabolic rate (BMR) based on a temperature-heart rate coupling model. The fall risk evaluation module (L24) is used for calculating a fall risk index (FRI) based on daily activity spectrum analysis of gait stability, balance ability and activity mode. The autonomic nerve evaluation module (L25) is used for calculating an autonomic nerve regulation index (ARI) based on heart rate variability. The bone fracture risk evaluation module (L26) is used for calculating a bone fracture risk score (FRS) based on the BMR, the FRI and the ARI. The application realizes three-dimensional coupling evaluation of bone metabolism state, fall risk and autonomic nerve function, and provides an objective and quantitative tool for the bone fracture risk reduction effect of an osteoporosis drug.

The invention provides an intelligent health interactive physical exercise service system, which relates to the technical field of intelligent health management, and comprises S1, a multi-dimensional data acquisition module, S2, a real-time monitoring and dynamic adjustment module, S3, a real-time monitoring and dynamic adjustment module and S4, a physical exercise service module. The exercise scheme adjusting module is used for dynamically adjusting an exercise scheme of a user according to the multi-dimensional exercise data and the physiological data which are collected in real time, and the individual physique analysis module is used for establishing an individual physique analysis model according to physique indexes, including BM I, basal metabolic rate and past health medical history, of the user. The intelligent health interactive physical exercise service system provides highly personalized exercise suggestions for different users by introducing learning and analysis of historical exercise behavior modes of the users, and can perform dynamic adjustment according to health states, exercise intensity bearing capacities and fatigue thresholds of the users, thereby effectively improving the exercise effect and improving the user experience. Excessive exercise or insufficient exercise is avoided, and exercise safety of the user is guaranteed.

The application discloses a kind of childbirth process energy consumption monitoring and individualized energy demand management system, including multimodal perception layer, transmission layer, computing layer and application decision layer, multimodal perception layer includes multimodal data acquisition module and non-invasive metabolism monitoring module, and the physiological sign parameter of pregnant woman is collected using wearable sensor array in multimodal data acquisition module, and non-invasive metabolism monitoring module carries out collection to tissue oxygenation data, body fluid distribution and metabolism data and oxygen consumption and carbon dioxide production data, computing layer establishes individual basal metabolic rate model by intelligent computing module, and calculates total energy consumption of labor, and then balanced state is calculated by dynamic energy demand prediction model, and application decision layer generates individualized nutrition supplement scheme according to the data of computing layer, triggers multi-level early warning and pushes clinical decision suggestion, and the application realizes real-time accurate monitoring of childbirth process energy consumption, individualized energy demand prediction and dynamic nutrition supplement.

The application discloses a kind of fat reduction management consultation platform and its implementation method, the present application relates to fat reduction management technical field, including data collection module, basal metabolism module, body fat trend prediction module, nutrition intervention module and exercise intervention module;Data collection module is responsible for the acquisition including the basic sign data of user, body weight, body fat rate;And dynamic acquisition user exercise frequency, training type and diet record;Basal metabolism module calculates basal metabolic rate BMR (t) according to user body weight and body fat rate;According to heart rate frequency and training type, generate activity correction factor AF (t) every time;The present application constructs historical heat input and body fat change sequence by body fat trend prediction module, calls XGBoost model to predict future body fat change, combines error weighted fusion mechanism to output final body fat prediction value, and judges whether to enter stable period or fat reduction rebound trend, effectively monitors user body fat change trend, effectively helps user to understand fat reduction process.

The application discloses a cooking method and a cooking device. The method comprises the following steps: obtaining a basal metabolic rate of a target user, a current activity state, an activity coefficient corresponding to the current activity state, and current cooking temperatures and current cooking times corresponding to a plurality of cooking materials respectively; determining a real-time metabolic rate based on the basal metabolic rate and the activity coefficient; performing metabolic rate comparison based on the real-time metabolic rate and a metabolic rate threshold to obtain a metabolic comparison result; then adjusting the current cooking temperatures and the current cooking times corresponding to the plurality of cooking materials respectively based on the real-time metabolic rate and the metabolic comparison result to obtain target cooking temperatures and target cooking times corresponding to the plurality of cooking materials respectively; and performing cooking on the corresponding cooking materials based on the target cooking temperatures and the target cooking times corresponding to the plurality of cooking materials respectively. The technical scheme provided by the application improves the real-time and accuracy of determining the real-time metabolic rate, and improves the adaptability and real-time of cooking to the user's demand.

The invention discloses a diabetes patient nutrition proportioning scheme generation method based on a calorie prediction network, which comprises the following steps: acquiring own physiological indexes and recent blood glucose monitoring data of a patient, designing a basal metabolic rate BMR calculation module according to the physiological indexes of the patient, designing a blood glucose level encoder according to a blood glucose monitoring result, and calculating the nutrition proportioning scheme of the patient according to the blood glucose level encoder. A calorie prediction space is constructed according to physiological indexes of a patient and output results of a blood glucose level encoder, a calorie prediction head is constructed according to output results of the calorie prediction space, a calorie prediction network is constructed on the basis of the calorie prediction head, and finally, body indexes, namely individual parameters, of the patient are input into a BMR calculation module during use. The daily fasting blood glucose value of the patient is input into the blood glucose level encoder, and the calorie prediction network outputs the daily nutrient substance intake proportion of the patient. The method solves the problems that an existing blood glucose management scheme depends on a traditional formula to calculate the basal metabolic rate and the daily heat, accuracy is low, and individual differences are difficult to consider.

FIELD: pediatrics; endocrinology.SUBSTANCE: invention can be used to predict the development of idiopathic precocious development (IPD) in obese girls under 9 years of age. The method includes determining the patient's height and BMI. Then, bioimpedance analysis is performed and the basal metabolic rate (BM), percentage of adipose tissue, active cellular mass (ACM) and skeletal muscle mass (SMM), and the proportion of SMM are determined. In addition, X-rays of the hands are performed and bone age is determined using the Greulich atlas. Furthermore, with normal values of BM, percentage of body fat and ACM, elevated values of BMI, SMM, proportion of SMM, the presence of tall stature and bone age ahead of biological age by 1-3 years, the development of IPD is predicted in girls with obesity up to 9 years of age.EFFECT: method is simple, accessible, and allows for the effective prediction of the development of IPD in obese girls.1 cl, 2 ex

The invention relates to a lymphedema rehabilitation assisting method, device and equipment and a medium. The method comprises the following steps: acquiring sparse monitoring data of a patient, wherein the sparse monitoring data comprises a circumference measurement value, a biochemical index, a skin image and a basal metabolic rate; based on a pathological constraint generative adversarial network, performing time sequence enhancement processing on the circumference measurement value to generate a continuous circumference measurement value; according to the basal metabolic rate and the historical false alarm rate of the patient, constructing a dynamic threshold model to obtain an optimized edema early warning threshold; and performing fusion processing on the skin image, the biochemical index and the continuous circumference measurement value through a cross-modal diagram attention network, outputting a fibrosis risk score, and generating a rehabilitation scheme in combination with an optimized edema early warning threshold. According to the method, through the technical means of sparse data enhancement, dynamic threshold optimization, cross-modal pathological association modeling and the like, the accuracy of lymphedema monitoring evaluation and the effectiveness of a rehabilitation scheme are remarkably improved, and reliable technical support is provided for rehabilitation treatment of lymphedema.

The invention discloses a multi-modal biological characteristic and dynamic learning lifting table self-adaption method which is characterized by comprising the following steps of S1, basic information collection, S2, dynamic body type classification, S3, health monitoring and reminding, S4, environment characteristic collection and classification, S5, behavior similarity evaluation and S5, dynamic learning lifting table self-adaption. According to the method, a multi-dimensional data fusion modeling method is adopted, physiological parameters, sensor data and behavior modes of users are organically combined, so that in the using process, the system intelligently adjusts the desktop height to the height suitable for the physical state of each user according to dynamic changes of the basal metabolic rate and daily behavior habits of the users, and the user experience is improved. And the requirements of the user under different body parameters and different situations are met.