54 results about "RESTING HEART RATE" patented technology

The invention discloses a maximum oxygen uptake estimation method and system and an estimation interaction system, and the estimation method comprises the steps: obtaining heart rate data, power data and / or speed data from a historical motion data sequence of a motion user, and forming a fit file; based on the fit file, extracting the maximum average power and / or the maximum average speed and the average heart rate corresponding to the maximum average power and / or the maximum average speed; acquiring real-time oxygen uptake in the corresponding motion mode based on a preset model, the maximum average speed and / or the maximum average power in different motion modes; acquiring a heart rate correction coefficient based on the maximum heart rate, the average heart rate and the resting heart rate of the exercise user; and estimating the maximum oxygen uptake in different exercise modes based on the height, weight and heart rate correction coefficient of the exercise user and the real-time oxygen uptake in different exercise modes. According to the method, the maximum oxygen uptake of the exercise user can be reliably and accurately obtained by using the historical exercise data.

The invention relates to a sudden cardiac death risk prediction method and device based on cardiovascular indexes and a medium. According to the method, the machine learning model can be trained and formed based on the basic feature set formed by the resting data of the heart rate of the sample human body in the resting state and the stress data of the heart rate in the stress state, and sudden cardiac death risk probability prediction is realized through feature weighting by using the machine learning model. The sudden cardiac death risk score of the individual can be obtained only by collecting the resting heart rate data of the individual in the resting state and the heart rate dynamic change data in the induced stress test process, experience deviation is avoided, and the prediction accuracy of sudden cardiac death is improved.

A method for baseline blood pressure estimation of a user of a wearable physiological monitor. The method comprising identifying a segment of pulse data related to cardiac activity of the user during a portion of a sleep session, wherein the segment of pulse data is obtained by the wearable physiological monitor; determining, from the segment of pulse data, a resting heart rate value of the user during the portion of the sleep session; identifying a machine learning model trained to receive as input one or more features including an input resting heart rate value obtained during a first time period and predict an indicator of blood pressure during a second time period; and providing the resting heart rate value to the machine learning model to obtain an indicator of baseline blood pressure for the user.

The invention discloses a drunkenness prediction system based on long-term sleep resting heart rate and respiration rate data, and relates to the technical field of drunkenness prediction, the drunkenness prediction system comprises a sleep monitoring subsystem, a heart rate data analysis subsystem and a regulation and control intervention subsystem; the sleep monitoring subsystem comprises a sleep respiration monitoring module and a sleep heart rate monitoring module; the sleep monitoring system is used for collecting data of a human body in the sleep process, electronic equipment is used for continuously collecting resting heart rate and breathing rate data in sleep for a long time, and the electronic equipment comprises an intelligent bracelet, a sports bracelet and a cardiotachometer and needs to have the advantages of being high in precision and good in stability so as to ensure the accuracy and reliability of the data; and meanwhile, the continuity of data acquisition is ensured, and the influence of data missing on subsequent analysis is avoided. According to the drunkenness prediction system, the influence of drunkenness on the body can be found in time by monitoring the sleep heart rate and the respiration rate for a long time, people are helped to know the physical condition of themselves, corresponding prevention measures are taken, and harm of drunkenness to health is reduced.

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 present disclosure relates to sleep scoring based on physiological information. Assessing sleep quality of a user in conjunction with an electronic device having one or more physiological sensors includes detecting that the user is attempting to fall asleep; and collecting physiological information associated with the user. The disclosed method of assessing sleep quality can include determining respective values of one or more sleep quality metrics based at least in part on the collected physiological information and at least one wake rest heart rate of the user, the one or more sleep quality metrics including a first set of sleep quality metrics associated with sleep quality of a plurality of users and a second set of sleep quality metrics associated with historical sleep quality of the user; and determining a unified score of sleep quality of the user based at least in part on the respective values of the one or more sleep quality metrics.

A computer-implemented method for predicting a date of delivery, comprising acquiring heart rate data for a pregnant user from wearable sensor system, determining a heart rate variability and / or a res

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.

An ergonomic heart rate tracking and bias detecting system and method is provided. The system is configured to determine if users exhibit biases by measuring their heartrates and / or recording audio via the smartwatch during simulated tests or real-world situations. The system is intended to help those supervisors of employees by requiring employees to wear the smartwatch and using the system track and record their behavior. The system is configured to transmit data from the smartwatch to the system application for analysis to measure the heartrate of users. A low or resting heartrate may denote a pass situation, where a high heartrate may denote a failure situation, meaning a potential bias is detected in the user. Audio recorded by the system is also analyzed for tone and language to further determine the presence of bias in the user. Supervisors may then decide whether to hire / fire / or retrain the user.

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.

This invention discloses a real-time health warning system and method based on a time-aware autoencoder, belonging to the field of intelligent system technology, applied to the analysis of heart rate data in wearable devices for non-diagnostic or therapeutic purposes. The implementation method of this invention is as follows: 1. Fusion of time-series data of heart rate and steps using a two-stage filtering method; 2. Generation of a resting heart rate matrix using the maximum power of the resting heart rate power spectrum; 3. Real-time health warning based on the resting heart rate matrix. Compared with existing technologies, this invention constructs a resting heart rate matrix that, by calculating the power spectrum of the resting heart rate time-domain sequence, can capture the user's unique heart rate cycle pattern. It proposes a global time interval analysis method and an exponentially decaying time interval mapping method to effectively adapt to processing non-equidistant time-series data. Simultaneously, it proposes a resting heart rate error model based on dynamic thresholds for personalized monitoring of the health status of wearable device users, thereby improving the practicality of the health warning model.

A computer-implemented method for predicting a date of delivery, comprising acquiring heart rate data for a pregnant user from wearable sensor system, determining a heart rate variability and / or a res

The invention discloses a real-time health early warning system and method based on a time perception auto-encoder, belongs to the technical field of intelligent systems, and is applied to heart rate data analysis of wearable equipment for non-diagnosis or disease treatment. The implementation method comprises the following steps of: 1, fusing time sequence data of heart rate and step number by using a two-stage filtering method; 2, generating a resting heart rate matrix by using the maximum power of the resting heart rate power spectrum; 3, realizing real-time health early warning based on the resting heart rate matrix; compared with the prior art, the method has the advantages that the resting heart rate matrix is constructed, the specific heart rate cycle mode of the user can be captured by calculating the resting heart rate time domain sequence power spectrum, and the global time interval analysis method and the exponential decay time interval mapping method are proposed to be effectively suitable for processing non-equal interval time sequence data; and meanwhile, a resting heart rate error model based on a dynamic threshold value is provided to construct the health condition for monitoring the wearable equipment wearer in a personalized manner, so that the practicability of the health early warning model is improved.

This invention discloses a method, system, and interactive system for estimating cycling power. The estimation method includes: obtaining the user's maximum heart rate; obtaining the user's real-time oxygen uptake based on the maximum heart rate, average heart rate, and resting heart rate; back-estimating cycling power based on the real-time oxygen uptake, the user's weight, and the bicycle's weight; calculating bias corrections for different exercise states based on the user's heart rate gradients under different exercise states; and using the bias corrections for different exercise states to correct the estimated cycling power, thereby obtaining the user's cycling power under different exercise states. This invention eliminates the need for power monitoring equipment, estimating cycling power under different exercise states based on the user's physiological information, bicycle weight, and heart rate data.

The invention discloses a riding power estimation method, an estimation system and an estimation interaction system. The estimation method comprises the following steps: acquiring the maximum heart rate of a sports user; based on the maximum heart rate, the average heart rate and the resting heart rate, the real-time oxygen uptake of the exercise user is obtained; based on the real-time oxygen uptake, the weight of the exercise user and the weight of the bicycle, the riding power is reversely estimated; based on the heart rate gradients of the moving user in the different moving states, offset correction in the different moving states is calculated; and correcting the estimated riding power by using offset correction in different motion states so as to obtain the riding power of the moving user in different motion states. According to the method, power monitoring equipment does not need to be adopted, and the riding power in different exercise states is estimated based on the physiological information of the exercise user, the weight of the bicycle and the heart rate data.

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.

The invention discloses a method for calculating a target heart rate of exercise, which comprises the following steps of: 1, obtaining the consensus of the relation among age, resting heart rate and target heart rate in the biomedical field and the sports science field at present by looking up academic literatures; 2, designing a research scheme, and determining a method and a tool for collecting data; 3, collecting data on site to obtain 324 samples; 4, through statistical analysis, carrying out multivariate stepwise regression analysis by taking the heart rate after exercise as a dependent variable and taking age and resting heart rate as independent variables, so as to obtain a double-drop target heart rate model; 5, establishing and calculating target heart rate matrix data, determining the age range to be 20-70 years old, determining the resting heart rate range to be 55-95 times per minute, and calculating the matrix data of the target heart rate lower limit value; the problem of correctly calculating the bull's-eye rate according with the medical physiology principle is solved.

Physiological metrics such as respiratory rate, resting heart rate, heart rate variability, temperature, and the like can be measured over time for a user and correlated to reproductive phases. By det

Provided are a system and a computer program for managing and predicting thyrotoxicosis using a wearable device. The system for predicting thyrotoxicosis is a system for predicting thyrotoxicosis using a resting heart rate, the system including a wearable device for measuring the heart rate of a patient at regular intervals, and a bio-signal computing device for receiving heart rate information from the wearable device, the bio-signal computing device outputting a warning alarm when a resting heart rate is greater than a reference heart rate when the patient is in a normal state.

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.

The invention relates to the technical field of medical care informatics, in particular to a standardized intelligent training method and system for peritoneal dialysis. The method comprises the following steps: firstly, according to the change condition of serum creatinine concentration and urea nitrogen concentration of a patient in a training stage at any time and sequence and the overall distribution condition of values of vital capacity and resting heart rate corresponding to all detection, obtaining the body function measurement of the patient in the training stage; according to all the training records and the examination scores of the to-be-analyzed items of the patient, obtaining the training acceptability of the patient to the to-be-analyzed items in the training stage; further obtaining the training intensity demand degree of the patient on the to-be-analyzed item in the training stage; finally, a training scheme of the next training stage of the patient is formulated. According to the method, the body function condition of the patient in the training stage and the acceptance condition of the patient on the training items are fully considered, so that the individual requirements of the patient in the training stage are better met, and the training effect of the training scheme is improved.

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.

The present invention discloses a human body state detection method, apparatus, terminal device, and storage medium. The method acquires a user's heart rate data; based on a predetermined human body state domain, the human body state of the user is detected according to the heart rate data, wherein the human body state domain is calculated by a resting heart rate baseline value, a maximum exercise heart rate value, and a minimum sleep heart rate value; and the user's environmental state is adjusted according to the human body state. The human body state domain is determined by the resting heart rate baseline value, the maximum exercise heart rate value, and the minimum sleep heart rate value, and the acquired user's heart rate data is compared with the human body state domain to make an accurate judgment on the user's human body state, thereby meeting people's requirements for a comfortable life and improving human comfort.

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."

The invention discloses a plateau exercise risk assessment device, which comprises a personal information acquisition module used for acquiring birth date information of an individual sporter; the altitude information acquisition module is used for acquiring the altitude of the current location of the sporter individual; the exercise heart rate acquisition module is used for acquiring the exercise heart rate of the exerciser during exercise; the resting heart rate acquisition module is used for acquiring the resting heart rate of the sporter during resting; the real-time calculation module is used for receiving the birth date information, the altitude, the exercise heart rate and the resting heart rate sent by the modules to calculate the exercise intensity level of the current human body; the early warning module is used for receiving the exercise intensity judgment result obtained by the real-time calculation module and carrying out early warning prompt or alarm on the plateau exercise risk according to the judgment result; and the storage module is used for receiving and storing the exercise intensity judgment result sent by the early warning module.

The present application proposes a method for calculating resting heart rate based on wearable device data, which includes: a data preprocessing step: an acceleration resting judgment step: using the difference of three-axis acceleration signals to judge whether the electrocardiogram signal corresponding to the three-axis acceleration signal is in a resting state and determine the preliminary resting segment of the electrocardiogram signal, and screening out the resting segment based on the acceleration signal based on the length and interval of the preliminary resting segment; a heart rate recovery judgment step: obtaining the second-by-second heart rate trend from the electrocardiogram signal; calculating the inflection point of the heart rate trend decline and rise, which is the heart rate recovery point; a resting segment heart rate calculation step: the heart rate recovery point to the end point of the resting segment based on the acceleration signal is the resting segment of the acceleration combined heart rate; calculating the resting heart rate of the resting segment of the acceleration combined heart rate; a resting heart rate aggregation step: the resting heart rates of all the resting segments of the acceleration combined heart rate in a day are aggregated using the median to obtain the resting heart rate of the day.