18 results about "RESTING HEART RATE" patented technology

This application relates to the field of pet product technology, and in particular to a soothing control method for a pet toy and the pet toy itself. The soothing control method includes acquiring the resting heart rate data of a target pet; determining the beat frequency per second based on the resting heart rate data; calculating a preset time period based on the beat frequency per second; determining a vibration sequence simulating a heartbeat rhythm based on the preset time period; and, in response to a soothing mode activation command, controlling a drive component in the pet toy to drive a drive gear and a ring gear to reciprocate in both directions to execute the vibration sequence, causing the pet toy to produce regular vibrations simulating a heartbeat. This application can alleviate the tension and fear of timid pets, stressed pets, and pets encountering toys for the first time, achieving a soothing effect.

The invention discloses a sleep health management method based on multi-modal physiological data, which comprises the following steps of: acquiring a multi-modal physiological signal of a user, extracting characteristic parameters of the multi-modal physiological signal to obtain multi-modal physiological characteristic parameters of the user, the multi-modal physiological signal comprises resting heart rate, heartbeat interval, exercise load, pressure level, sleep liability, sleep storage and personal information of the user; preprocessing the multi-modal physiological feature parameters of the user to obtain a physiological signal feature set of the user; a first neural network model is adopted to calculate the user physiological signal feature set, and a user sleep staging result is obtained; performing primary feature fusion on the user sleep staging result and the user physiological signal feature set, and inputting into a first training set to obtain a user sleep score; and performing secondary feature fusion on the user sleep score, the user sleep staging result and the user physiological signal feature set, and inputting into a random forest model to obtain a user sleep guidance suggestion.

The invention relates to the technical field of judgment penalty, in particular to a judgment oxygen-free judgment training method based on a treadmill and a display system, and the method comprises the steps: collecting the physiological state of a user as a reference, inducing and generating a dynamic scene through an oxygen-free state, completing the penalty interaction of the user, feeding back the penalty interaction to a display screen in time, and evaluating based on a penalty interaction result. According to the method, the individual resting heart rate and age characteristics are calibrated to generate the personalized anaerobic threshold interval, so that the limitation of traditional unified intensity training is eliminated, the training starting point and load regulation and control better fit the individual physiological characteristics, the training safety and pertinence are ensured, and adaptive anaerobic state induction schemes are provided for referees with different physical ability levels.

: A technique for estimating whether or not a subject has an infection from their physiology that is capable of adjusting for menstrual cycle states. The technique comprises obtaining physiological data representing one or more of the subject's physiological parameters at a first point in time, such as skin temperature and resting heart rate. The physiological data is then used to estimate the subject's menstrual state at the first point in time. In particular, the subject's menstrual state is known to impact their physiology. Both the physiological data and the estimated menstrual state are then subsequently used to estimate the subject's infection state. Specifically, the physiological data and the menstrual state are processed using a machine-learning model.

The invention discloses a control method and device for a low-load household cardiovascular rehabilitation training instrument. The method comprises the steps that a resting heart rate value, a real-time heart rate value and a heart rate variability set of a user are collected; according to the resting heart rate value, an initial safety threshold value is determined; according to the heart rate variability set, determining a fatigue evaluation result for indicating the fatigue degree of the user; according to the heart rate variability set and the fatigue evaluation result, the initial safety threshold is dynamically adjusted, and a target safety threshold is determined; according to the real-time heart rate value and a target safety threshold value, the current load when the user uses the training instrument at the current moment is dynamically adjusted, and the target load when the user uses the training instrument at the next moment is determined; and controlling the training instrument by adopting the target load. The load of the user using the training instrument can be dynamically adjusted according to the fatigue degree of the user, and the effectiveness and safety of the user using the training instrument for heart rehabilitation are effectively improved.

The invention provides a method for acquiring a maximum heart rate, which is applied to first electronic equipment and comprises the following steps: judging that a motion record exists in the first electronic equipment, the motion record comprises data recorded in a process of performing at least one type of motion, judging that various types of data are updated at least once in a first duration, and if the data are updated at least once in the first duration, determining that the data are updated at least once in the first duration. The multiple types of data comprise the physical activity level, the maximum oxygen uptake and the resting heart rate, the physical activity level represents the strength of physical strength consumed within the second duration, and the first maximum heart rate is obtained based on the multiple types of data and a preset model. The maximum heart rate obtained based on various types of data has higher accuracy.

A method for monitoring cardiac health of a user of a mobile computing device includes activating an imaging device associated with the mobile computing device in response to a trigger indicating a presence of the user within a field of view of the imaging device. The method also includes obtaining, via the imaging device, video data of a face of the user for a time period. Further, the method includes determining, via a machine-learned model programmed in a processor of the mobile computing device, one or more cardiac metrics based on, at least, the video data. The one or more cardiac metrics includes, at least, a heart rate of the user. Furthermore, the method includes determining one or more cardiac biomarkers based, at least in part, on the one or more cardiac metrics. The one or more cardiac biomarkers includes, at least, a resting heart rate of the user. In addition, the method includes causing a display of the mobile computing device to display, at least, the one or more cardiac biomarkers in response to a user input.

This application discloses a method and apparatus for assessing breathing training. The method includes: acquiring physiological signals of a target user before, during, and after breathing training, and performing feature extraction and quantification to form feature arrays for pre-training, during-training, and post-training; obtaining normalized resting heart rate and normalized resting respiratory rate of the target user in the current state based on the target user's historical resting heart rate and historical resting respiratory rate; normalizing the feature arrays before and after breathing training based on the target user's current resting heart rate and resting respiratory rate; and performing a breathing training assessment based on the feature arrays during and after breathing training, as well as the normalized feature arrays before and after breathing training. By incorporating resting heart rate and resting respiratory rate into the three parts of the breathing training process (pre-training, during, and post-training), a comprehensive assessment of the entire breathing training cycle is achieved.

A motion index calculation system suitable for obtaining a motion index that matches the physical characteristics of a subject is provided. [Solution] An exercise index calculation device 100 inputs a subject's age and resting heart rate, calculates a target heart rate using the Karvonen method calculation formula based on the subject's age, resting heart rate, and intensity (%), and calculates a baseline NSM using the CLS theory calculation formula based on the calculated target heart rate.The subject then exercises with a target NSM set based on the calculated baseline NSM, reads out measurement data measuring the NSM and heart rate at that time, and calculates an adapted NSM using an individualization formula based on the calculated target heart rate and the read out measurement data.The device also determines parameters of the individualization formula based on the measurement data, and classifies the subject's physical characteristics based on the determined parameters.

Embodiments of the present application provide a fatigue detection method, device and storage medium. The method combines the index data provided by the wearable device for calculating the fatigue level and the self-evaluation results collected by various subjective evaluation questionnaires displayed on the electronic device used by the user, and determines the fatigue level from the morning pulse resting heart rate data, the HRV data, the ACWR data and the sleep data. The four specific fatigue level determination methods are mutually complementary, and the priority order is that the morning pulse resting heart rate is higher than the HRV, the HRV is higher than the ACWR, and the ACWR is higher than the sleep. One fatigue level is selected from the fatigue levels determined from the four dimensions, so that the finally determined fatigue level is more reasonable and accurate, thereby suitable guidance suggestions can be given to the user before exercise, and damage to the body caused by excessive exercise of the user can be avoided.

This invention discloses a system and method for intelligently adjusting a car massage mode based on AI in the field of automotive intelligent cockpit technology. The system includes: after the user sits down, collecting initial pressure distribution data and initial resting heart rate data from a pressure sensor array and a heart rate sensor, respectively; comparing the initial pressure distribution data with standard pressure distribution data in a pre-stored ergonomic database, calculating a pressure distribution matching score for each monitored area, determining the target body parts requiring focused massage based on the matching score, and thus locating the initial point of action of the massage actuator. This invention can automatically identify user vital signs, perceive fatigue status in real time, and provide a personalized, adaptive massage adjustment intelligent solution, achieving an intelligent massage experience of "one plan per person, real-time adjustment, and memory reuse."

An electronic apparatus according to an embodiment may cause to be identified, on the basis of biometric information, whether a first condition related to heatstroke is satisfied. The first condition may include conditions in which the skin temperature is higher than or equal to a first threshold temperature, the ratio of a heart rate to a resting heart rate is greater than or equal to a threshold ratio, and a sweat output is greater than or equal to a threshold sweat output. The electronic apparatus may cause, on the basis of the first condition being satisfied, a display to display first information indicating that a user is at risk of heatstroke.

This invention discloses a method and system for inferring female physiological states based on multimodal physiological signals. The method acquires multimodal physiological signals from a user through a wearable device, including but not limited to skin temperature, heart rate variability, resting heart rate, sleep data, and electrodermal signals; constructs an individual baseline model for the user; based on the deviation characteristics of the current signal from the individual baseline, constructs a state space containing multiple candidate physiological states; performs constraint contraction processing on the state space, the constraints including at least two of the following: state transition order constraint, state duration constraint, signal change direction constraint, and multimodal signal collaborative constraint; and determines the final physiological state from the contracted state space. The method also includes a cycle segmentation mechanism, data sufficiency judgment and degradation control, closed-loop intervention effect evaluation, risk probability calculation, and intelligent suggestion generation based on a language model. This invention upgrades female cycle management from calendar-time-based calculation to constrained state inference based on real-time multimodal physiological signals, applicable to applications such as premenstrual syndrome prediction, ovulation period identification, assisted conception, contraception reference, early pregnancy monitoring, postpartum recovery tracking, and perimenopausal management.

To provide a more accurate resting heart rate regardless of an action time of a subject.SOLUTION: A resting heart rate calculation device includes a heart rate calculation unit for calculating a heart rate from electrocardiographic data measured by an electrocardiograph, and a resting heart rate calculation unit for calculating a heart rate at a given quantile as the resting heart rate, among heart rates recorded for 24 hours or longer.SELECTED DRAWING: Figure 2

The application discloses a sleep health management method based on multi-modal physiological data, comprising the following steps: acquiring multi-modal physiological signals of a user, extracting feature parameters of the multi-modal physiological signals to obtain multi-modal physiological feature parameters of the user, wherein the multi-modal physiological signals comprise resting heart rate, heartbeat interval, exercise load, stress level, sleep debt, sleep savings and personal information of the user; preprocessing the multi-modal physiological feature parameters of the user to obtain a user physiological signal feature set; calculating the user physiological signal feature set by using a first neural network model to obtain a sleep staging result of the user; performing first-level feature fusion on the sleep staging result of the user and the user physiological signal feature set and inputting the first-level feature fusion result into a first training set to obtain a sleep score of the user; and performing second-level feature fusion on the sleep score of the user, the sleep staging result of the user and the user physiological signal feature set and inputting the second-level feature fusion result into a random forest model to obtain a sleep guidance suggestion of the user.

The present application belongs to the technical field of wearable smart health monitoring, and specifically discloses a wearable electrocardiogram monitoring system based on a clothes-type thermoelectric generator. The wearable electrocardiogram monitoring system comprises a clothes-type thermoelectric generator unit, a heartbeat monitoring unit and an upper computer. The heartbeat monitoring unit comprises a voltage conversion circuit and an electrocardiogram data acquisition module, and the upper computer comprises an analysis unit and a display. The clothes-type thermoelectric generator unit is used to power the heartbeat monitoring unit, the electrocardiogram data acquisition module is used to acquire and wirelessly transmit electrocardiogram data and heart rate data, and the analysis unit is used to analyze the heart rate data stored in the upper computer and to determine the risk of pulmonary arterial hypertension according to the resting heart rate data, the maximum heart rate data and the time-varying heart rate data. The clothes-type thermoelectric generator unit and the heartbeat monitoring unit of the present application are both worn on the human arm, have the advantages of portability and lightness, and can be used as an auxiliary diagnostic device for determining pulmonary arterial hypertension, thus having great potential in medicine.

The invention relates to the technical field of medical information processing and health behavior intervention, and discloses an alcohol use disorder rehabilitation data processing system and method. A physiological monitoring terminal is deployed in the system, heart rate variability, galvanic conductance and skin temperature in a resting state are imported, and two-point calibration is performed to obtain an individual baseline; high-risk points are recorded, fences are arranged, and entering and exiting are judged according to the spherical distance; three parameters are collected regularly, channel availability and sample effectiveness are judged according to medical upper and lower limit normalization, and real-time deviation is calculated; summarizing according to natural days to generate a next-day individualized threshold value; when entering the fence and deviating from the over threshold, triggering the risk, calculating the intensity and performing four-stage grading; implementing micro intervention according to grades and recording; after intervention, a fixed window is used for re-measurement, and deviation, improvement and summarization are obtained; and performing seven-day statistical triggering and risk, aggregating daily average deviation and daily improvement, and generating a training duration and period report.

The invention relates to the technical field of medical care informatics, and discloses a personalized training scheme dynamic generation method and system, and the system comprises a safety boundary setting module which is used for generating a safety inhibition coefficient according to the frequency domain characteristic deviation degree of resting heart rate variability data, and correcting a limit load threshold value through the safety inhibition coefficient to determine a dynamic safety response boundary; the response model calibration module is used for calibrating a heart rate response gain coefficient of the first-order heart rate dynamics model based on the ratio of the intensity of the step load instruction to the heart rate change rate; and the training plan generation module is used for constructing an optimization calculation model limited by a dynamic safety response boundary, and substituting a heart rate response gain coefficient into a solution training load control sequence. The risk of parameter mismatching of the heart rate response model caused by fluctuation of drugs or physiological states is eliminated, and accurate dynamic matching of training loads and the current actual endurance capacity of the patient is achieved.