CN117476181B - Personalized static therapy plan generation method and system - Google Patents
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- G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
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Abstract
The invention discloses a personalized static treatment plan generation method and system, wherein a feature extraction module extracts a body movement frequency index and a sleep duration deviation index in sleep data, extracts a body weight deviation index in physiological data, extracts a metabolic rate floating coefficient in diet data, an analysis module comprehensively analyzes the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient to generate a static treatment coefficient, a reminding module compares the static treatment coefficient with a preset static treatment threshold value, and judges whether a static treatment plan reminding signal is required to be sent according to a comparison result, wherein the static treatment plan reminding signal comprises a voice prompt, a lamplight flickering prompt and the like. The generating system can comprehensively analyze the patient data in the untreated period, and then judge whether the patient needs medical treatment or not, and when the patient is judged to need medical treatment, a static treatment prompt is sent to the patient or the family members of the patient, so that the timely medical treatment of the patient with depression is ensured.
Description
Technical Field
The invention relates to the technical field of computer and equipment management and control, in particular to a personalized static therapy plan generation method and system.
Background
Psychological health problems are becoming more common in modern society, especially in the face of stress, anxiety, depression, post-traumatic stress disorder, etc., and with the increase in social stress and complexity of psychological health problems, personalized treatment and management plans become particularly important, and static therapy (also called psychotherapy), which is a common treatment method for helping patients to treat affective and psychological problems by means of talking and behavioral therapy, etc., and personalized static therapy plan generation system, which is an application based on artificial intelligence and medical technology aiming at helping individual patients manage psychological health and emotional problems, generates personalized psychological therapy plans for each patient using artificial intelligence technology such as natural language processing and machine learning in combination with medical professional knowledge.
The prior art has the following defects:
For patients with depression, after a doctor checks symptoms of depression, a regular static treatment plan is formulated for the patient, that is, the patient needs to go to a hospital for one treatment at intervals, however, because the patients with depression have uncontrollable factors (such as the patients with depression cannot know physical conditions and whether to start depression), if a regular treatment mode is adopted, the patients or families of the patients cannot know and seek medical attention in time when the patients get worse during the untreated period, and the patients with depression can have self-hazard behaviors or symptoms;
Therefore, the application provides a personalized static treatment plan generation method and a personalized static treatment plan generation system, which judge the condition of a patient by comprehensively analyzing daily life data of the patient, so that the patient or family members of the patient are reminded of medical treatment in advance when the condition of the patient is poor.
Disclosure of Invention
The invention aims to provide a personalized static therapy plan generation method and a personalized static therapy plan generation system, which aim to solve the defects in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: a personalized static therapy plan generation system comprises a sleep monitoring module, a physiological monitoring module, a diet monitoring module, a characteristic extraction module, an analysis module and a reminding module;
sleep monitoring module: the sleep data processing device is used for monitoring the sleep data of the patient when the patient is in a sleep state and preprocessing the sleep data;
The physiological monitoring module: the method is used for monitoring physiological data of a patient in daily life of the patient and preprocessing the physiological data;
diet monitoring module: the method comprises the steps of acquiring diet data of a patient at fixed time, and preprocessing physiological data;
And the feature extraction module is used for: extracting a body movement frequency index and a sleep duration deviation index in sleep data, extracting a body weight deviation index in physiological data, and extracting a metabolic rate floating coefficient in diet data;
And an analysis module: comprehensively analyzing the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient to generate a static therapy coefficient;
a reminding module: and comparing the static therapy coefficient with a preset static therapy threshold value, and judging whether a static therapy plan reminding signal needs to be sent out according to a comparison result.
Preferably, the analysis module generates the static therapy coefficient after comprehensively calculating the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient, and the expression is as follows:
wherein jl f is a static therapy coefficient, tp is a body movement frequency index, SPC is a sleep duration deviation index, TPC is a body weight deviation index, DXF is a metabolic rate floating coefficient, alpha, beta, gamma and delta are proportional coefficients of the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient respectively, and alpha, beta, gamma and delta are all larger than 0.
Preferably, the reminding module compares the static therapy coefficient with a preset static therapy threshold value, and judges whether a static therapy plan reminding signal needs to be sent according to a comparison result, wherein the method comprises the following steps of:
If the value of the static therapy coefficient jl f is greater than or equal to the static therapy threshold value, the reminding module judges that a static therapy plan reminding signal needs to be sent out;
if the value of the static therapy coefficient jl f is smaller than the Yu Jing therapy threshold, the reminding module judges that the static therapy plan reminding signal does not need to be sent.
Preferably, the data acquisition mode of the body movement frequency index is as follows:
Where tp is the body movement frequency index, i=1, 2, 3,..and n, n is the number of sampling periods, and n is a positive integer, pl i represents the body movement frequency of the i-th sampling period.
Preferably, the sleep duration deviation index is calculated by the following expression:
Where SPC is the sleep duration deviation index, sjm is the actual sleep duration, and jys is the proposed sleep duration.
Preferably, the calculation expression of the body weight deviation index is:
Where TPC is a body weight deviation index, sjt is an actual body weight, and bzt is a healthy body weight.
Preferably, the metabolic rate floating coefficient is calculated by the following expression:
Wherein DXF is metabolic rate floating coefficient, F (t) is metabolic rate variation of a patient, [ t x,ty ] is cortisol level early-warning period, and [ t i,tj ] is diet early-warning period.
Preferably, the acquisition logic for the period of cortisol level warning is: the period when the cortisol content exceeds the content threshold value is the period of cortisol level early warning;
the acquisition logic of the diet early warning period is as follows: the period when the non-dietary time exceeds the time threshold is the period of dietary pre-warning.
The invention also provides a personalized static therapy plan generation method, which comprises the following steps:
S1: the monitoring end monitors sleep data of the patient when the patient is in a sleep state, monitors physiological data of the patient in daily life of the patient, and regularly acquires diet data of the patient;
S2: preprocessing sleep data, physiological data and diet data;
S3: the processing end extracts a body movement frequency index and a sleep duration deviation index in sleep data, extracts a body weight deviation index in physiological data and extracts a metabolic rate floating coefficient in diet data;
s4: comprehensively analyzing the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient to generate a static therapy coefficient;
s5: and comparing the static therapy coefficient with a preset static therapy threshold value, judging whether a static therapy plan reminding signal needs to be sent out according to a comparison result, and sending the reminding signal to a mobile terminal of a patient or a family member of the patient for reminding.
In the technical scheme, the invention has the technical effects and advantages that:
According to the invention, the body movement frequency index and the sleep duration deviation index in the sleep data are extracted through the characteristic extraction module, the body weight deviation index in the physiological data is extracted, the metabolic rate floating coefficient in the diet data is extracted, the analysis module comprehensively analyzes the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient to generate the static therapy coefficient, the reminding module compares the static therapy coefficient with a preset static therapy threshold value, and judges whether a static therapy plan reminding signal is required to be sent according to a comparison result, wherein the static therapy plan reminding signal comprises a voice prompt, a lamplight flashing prompt and the like, and can be sent to a mobile terminal of a patient or a patient family for reminding, so that the patient is required to seek medical treatment, and when the patient or the patient family receives the reminding signal, the patient is required to seek medical treatment in time. The generating system can comprehensively analyze the patient data in the untreated period, and then judge whether the patient needs medical treatment or not, and when the patient is judged to need medical treatment, a static treatment prompt is sent to the patient or the family members of the patient, so that the timely medical treatment of the patient with depression is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a block diagram of a system according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: referring to fig. 1, the personalized static therapy plan generating system according to the embodiment includes a sleep monitoring module, a physiological monitoring module, a diet monitoring module, a feature extraction module, an analysis module, and a reminding module;
Sleep monitoring module: the method is used for monitoring sleep data of a patient when the patient is in a sleep state, preprocessing the sleep data and then sending the sleep data to the feature extraction module, and comprises the following steps of:
And (3) data acquisition: sleep data is acquired for the patient using suitable sensors (e.g., sleep tracker, EEG electroencephalogram, heart rate monitor, breath detector, etc.). These sensors may measure data from different aspects, including sleep cycle, heart rate, respiration, body movement, etc.
And (3) data storage: the acquired sleep data is stored for later analysis and recording. The data can be stored on the local device or can be transmitted to a cloud database for further processing.
Data preprocessing: sleep data typically contains some noise and unnecessary information. Data cleaning and preprocessing is required prior to further analysis. This may include outlier removal, noise filtering, data interpolation and data calibration.
Data segmentation: sleep data typically exists in a continuous time series that needs to be cut into smaller time segments, for example, every 30 seconds or 1 minute. This facilitates subsequent feature extraction and analysis.
The physiological monitoring module: the method is used for monitoring physiological data of a patient in daily life of the patient, preprocessing the physiological data and then sending the preprocessed physiological data to the feature extraction module, and comprises the following steps of:
And (3) data acquisition: appropriate sensors and devices are used to acquire physiological data of the patient. Such data may include heart rate, blood pressure, respiratory rate, body temperature, blood oxygen saturation, exercise, weight, and various physiological parameters. The sensor may be embedded in a wearable device, such as a smart watch, heart rate monitor, weight scale, or may be a portable device or sensor array.
And (3) data storage: the collected physiological data is stored in a proper place, and can be a storage device in the local equipment or a cloud database. It is very important to ensure data security and privacy.
And (3) data transmission: the collected data is transmitted to the physiological monitoring system, which may be via Bluetooth, wi-Fi, or other communication protocol. The data transmission can be performed in real time or periodically in batch.
Data cleaning and pretreatment: physiological data typically contains noise and outliers, requiring data cleansing and preprocessing. This includes outlier removal, noise filtering, data interpolation and data calibration.
Data segmentation: the continuous physiological data stream is divided into small time periods for subsequent analysis. This helps to correlate data with events and activities within a particular time window.
Diet monitoring module: the method is used for acquiring diet data of a patient at fixed time, preprocessing physiological data and then sending the preprocessed physiological data to a feature extraction module, and comprises the following steps of:
And (3) data acquisition: dietary data of the patient is acquired, including information on eating content, food type, intake, eating time, eating location, and the like. Such data may be collected in a variety of ways, such as manual entry, photo recording, intelligent diet tracking applications, or intelligent diet scales.
And (3) data storage: the collected diet data is stored in the system for later analysis and recording. The data may be stored in a local device or cloud database.
And (3) data transmission: if the diet data is collected by a mobile application or an online platform, the data is transmitted to a diet monitoring system for further processing and analysis.
Data cleaning and pretreatment: the diet data may include input errors, incomplete information, or other noise. Data cleansing and preprocessing are performed to remove inaccurate or invalid data.
Data classification: the diet data is classified by time and type to better understand diet patterns. The data may be categorized on a per meal, daily, or custom time period basis.
And the feature extraction module is used for: extracting a body movement frequency index and a sleep duration deviation index in sleep data, extracting a body weight deviation index in physiological data, extracting a metabolic rate floating coefficient in diet data, and then sending the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient to an analysis module.
And an analysis module: comprehensively analyzing the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient to generate a static therapy coefficient, and sending the static therapy coefficient to the reminding module.
A reminding module: the method comprises the steps of comparing a static therapy coefficient with a preset static therapy threshold value, judging whether a static therapy plan reminding signal needs to be sent out according to a comparison result, wherein the static therapy plan reminding signal comprises a voice prompt, a lamplight flashing prompt and the like, and the reminding signal can be sent to a mobile terminal of a patient or a patient family member to remind the patient of needing medical treatment, and after the patient or the patient family member receives the reminding signal, the patient needs to timely seek medical treatment, and the method comprises the following steps:
Judging reminding conditions: based on the result of the data analysis, the reminding module judges whether the condition for sending out the reminding is met. These conditions may include exacerbation of symptoms, changes in physiological parameters, improper diet, poor performance of the treatment plan, etc.
The reminding mode is selected: if the reminding needs to be sent out, the reminding module selects a proper reminding mode. This may include voice prompts, flashing lights, text messages, mobile application notifications, etc. The alert mode should be selected based on patient preferences and availability of the device.
And (5) reminding sending: the reminding module sends a reminding signal to the mobile terminal of the patient or the family member of the patient. This may be accomplished through a communication channel connected to the mobile application or device.
Feedback mechanism: the reminder module may include a feedback mechanism to see if the patient has been reminded and has taken action. The patient may send feedback information to the system via a mobile application or other means.
Reminding record: the reminding module records the sending time, the content and the feedback of the patient of the reminding. These recordings help track the response and reminding effects of the patient.
Reminding follow-up: the reminder module may set a follow-up mechanism for the reminder to ensure that the patient takes appropriate action after receiving the reminder. This may include periodic reminders and subsequent feedback.
According to the application, the body movement frequency index and the sleep duration deviation index in the sleep data are extracted through the characteristic extraction module, the body weight deviation index in the physiological data is extracted, the metabolic rate floating coefficient in the diet data is extracted, the analysis module comprehensively analyzes the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient to generate the static therapy coefficient, the reminding module compares the static therapy coefficient with a preset static therapy threshold value, and judges whether a static therapy plan reminding signal is required to be sent according to a comparison result, wherein the static therapy plan reminding signal comprises a voice prompt, a lamplight flashing prompt and the like, and can be sent to a mobile terminal of a patient or a patient family for reminding, so that the patient is required to seek medical treatment, and when the patient or the patient family receives the reminding signal, the patient is required to seek medical treatment in time. The generating system can comprehensively analyze the patient data in the untreated period, and then judge whether the patient needs medical treatment or not, and when the patient is judged to need medical treatment, a static treatment prompt is sent to the patient or the family members of the patient, so that the timely medical treatment of the patient with depression is ensured.
Example 2: the feature extraction module extracts a body movement frequency index and a sleep duration deviation index in sleep data, extracts a body weight deviation index in physiological data and extracts a metabolic rate floating coefficient in diet data;
The data acquisition mode of the body movement frequency index is as follows:
Where tp is the body movement frequency index, i=1, 2, 3,..and n, n is the number of sampling time periods, and n is a positive integer, pl i represents the body movement frequency of the i-th sampling time period;
the acquisition mode of the body movement frequency data comprises the following steps:
wearable device: using wearable devices, such as smartwatches, smartphones or sleep trackers, which typically have built-in acceleration sensors, the patient's movements and body movements can be monitored;
Mattress sensor: mattress sensors can be placed under the mattress for monitoring movement of the patient in the bed, and these sensors can typically detect body movement, turning over, tumbling, etc. of the person in the bed;
An infrared sensor: some sleep studies use infrared sensors to monitor the body movements of a patient, which sensors can detect changes in movement;
shooting and monitoring: camera monitoring can be used to observe the patient's body movements during sleep, which requires patient consent and is typically used only in clinical studies;
The greater the body movement frequency index, the following problems may be present for depressed patients:
Anxiety or anxiety: the high body movement frequency may reflect restlessness or anxiety in night sleep, depression and anxiety are often accompanied by sleep problems, and the patient may wake up many times at night, with an increase in body movement frequency;
Sleep quality is degraded: high body movement frequency may indicate a decrease in sleep quality, and depressed patients often experience sleep problems, including difficulty falling asleep, frequent waking and early waking, etc., which may result in increased body movement frequency, as the patient wakes up multiple times during the night's sleep cycle;
Symptoms of motor anxiety: some of the depressed patients may experience symptoms of motor anxiety, which may cause them to move more frequently during the night, which may be associated with anxiety or stress;
Potential sleep disorders: high body movement frequency may also reflect potential sleep disorders that the patient may have, such as periodic limb movement disorder (Periodic Limb Movement Disorder) or restless leg Syndrome (RESTLESS LEG syncrome), which may result in nocturnal limb movement, and thus increased body movement frequency.
The calculation expression of the sleep duration deviation index is as follows:
Wherein SPC is sleep duration deviation index, sjm is actual sleep duration, jys is recommended sleep duration;
Wherein:
Actual sleep duration: firstly, measuring or recording the actual sleep time of a patient, which is usually obtained by a sleep monitoring instrument, wherein the actual sleep time is usually in units of hours;
Suggested or ideal sleep duration: the recommended or ideal sleep duration is determined, typically based on the age, stage of life and individual differences of the patient, e.g., an adult typically needs to sleep 7-9 hours per night, which value may typically be determined according to the recommendations of a health professional.
The data acquisition mode is as follows:
Sleep monitoring instrument: measuring the actual sleep duration of the patient using a sleep monitoring instrument (e.g., sleep tracker, EEG electroencephalogram, multichannel sleep monitoring device) which can provide detailed sleep data including fall asleep time, wake-up time, and distribution of different sleep stages;
mobile application: some mobile applications may be used to monitor and record the sleep time of a patient, and these applications typically use smart phone acceleration and other sensors to estimate the sleep duration.
The greater the sleep duration deviation index, the more likely a depressed patient is:
Sleep insufficiency: a large sleep duration deviation index generally indicates that the actual sleep time of the patient is significantly insufficient, which may lead to the patient not getting enough rest at night, and may be symptomatic of hyposleep, such as daytime sleepiness, fatigue, mental confusion, etc., which is also associated with exacerbation of depression symptoms;
difficulty falling asleep: depression patients often accompany difficulty falling asleep, and longer falling asleep times may lead to prolonged sleep durations, which may indicate that the patient takes longer to fall asleep, and may even wake up at night, making it difficult to fall asleep again;
Sleep quality is degraded: a large sleep duration deviation index may reflect a decrease in sleep quality, and patients may experience shallow sleep, frequent wakefulness, nightmares, and other sleep problems, all of which may lead to deviations in sleep duration;
secondary insomnia: depression can lead to secondary insomnia, i.e. insomnia caused by symptoms of depression, the patient may wake up at night and then have difficulty falling asleep, which can increase the irregularity of the actual sleep duration;
Varying day and night rhythms: depression patients often accompany disturbances in day and night rhythms, they may have a tendency to fall asleep and wake up at any time, which can lead to instability in the sleep duration.
The calculation expression of the body weight deviation index is as follows:
wherein TPC is a body weight deviation index, sjt is an actual body weight, bzt is a healthy body weight;
Wherein:
actual body weight: first, the actual weight of the patient, typically in kilograms or pounds, is measured or recorded;
healthy body weight: the ideal or healthy weight of the patient is determined, which is typically based on age, sex, height and health condition, and can be determined according to the advice of the health professional.
The data acquisition mode is as follows:
Weight measurement: the use of a weight scale or weighing instrument to measure the actual weight of a patient is the most common method of data acquisition, typically performed by a medical professional;
height measurement: in order to calculate the body weight deviation index, it is often necessary to know the height of the patient, since the body weight deviation index is calculated based on the ratio of body weight to height, which can be measured by a height instrument or physical examination.
The greater the body mass deviation index, the more likely a depressed patient is to have the following problems:
Weight gain: a larger body weight deviation index may indicate that the actual body weight of the patient significantly exceeds the ideal or healthy body weight, possibly because depressed patients may more easily seek food as a countermeasure when feeling low, resulting in weight gain;
Diet problems: some of the depressed patients may experience eating problems, such as binge eating or emotional feeding, which may lead to unhealthy eating habits, thereby affecting body weight;
sleep problems: some of the depressed patients may experience sleep problems, including night awakenings, insomnia, and irregular sleep times, which may have an impact on body weight;
Drug treatment: certain antidepressants may cause weight gain as a side effect, which may cause weight deviation during the course of depression treatment;
physical inactivity is inadequate: symptoms of depression are often associated with insufficient physical activity, which may lead to weight gain;
Self-negligence: depression may lead to self-negligence, including neglect of diet and body weight, and patients may lack attention to their own diet and body weight changes, resulting in weight gain;
Health problems: high body mass deviation index may be a sign of potential health problems, such as obesity, diabetes, cardiovascular disease, etc., which are more common in depressed patients.
The metabolic rate floating coefficient is calculated as:
Wherein DXF is metabolic rate floating coefficient, F (t) is metabolic rate variation of a patient, [ t x,ty ] is cortisol level early-warning period, and [ t i,tj ] is diet early-warning period;
Depression is associated with abnormal activities of the neuroendocrine system, such as an increase in cortisol (a stress hormone) level, which leads to a decrease in metabolic rate in depressed patients, is a stress hormone, and when the body is in a stress state, its secretion amount increases, which includes emotional stress, such as depression, high levels of cortisol can promote development of adipocytes and storage of fat, especially in the abdomen, which leads to an increase in body fat, increase in body weight, high levels of cortisol can affect energy metabolism, decrease basal metabolic rate, i.e., energy burned by the body in a resting state, which leads to a decrease in energy consumption, which easily leads to an increase in body weight, especially in case of long-term depression, high cortisol levels can affect insulin sensitivity, which leads to insulin resistance, which can lead to problems in blood glucose control, possibly leading to diet selection and change in appetite, especially increasing intake of carbohydrates, which can also affect body weight, so that the period of cortisol content exceeds the content threshold is a period of cortisol level early warning;
saliva testing is a relatively easy way to do at home for monitoring fluctuations in cortisol levels, and doctors give patients saliva test kits to collect saliva samples according to instructions provided by the kit. Typically, saliva samples are collected at specific points in time (e.g., morning, noon, afternoon, evening) and sent back to the hospital for analysis, where the analysis results are matched to the patient and then uploaded directly to the system;
long jumps or no meals may lead to a decrease in metabolic rate, as the body may enter an energy saving state to accommodate irregular food supply, and hence periods when the non-dietary duration exceeds the duration threshold are periods of dietary pre-warning.
The analysis module comprehensively calculates a body movement frequency index, a sleep duration deviation index, a body weight deviation index and a metabolic rate floating coefficient to generate a static therapy coefficient, and the expression is as follows:
Wherein jl f is a static therapy coefficient, tp is a body movement frequency index, SPC is a sleep duration deviation index, TPC is a body weight deviation index, DXF is a metabolic rate floating coefficient, alpha, beta, gamma and delta are proportional coefficients of the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient respectively, and alpha, beta, gamma and delta are all larger than 0;
According to the calculation expression of the static therapy coefficient and the acquisition logic of each parameter, the larger the value of the static therapy coefficient jl f is, the more the patient needs to seek medical treatment.
The reminding module compares the static therapy coefficient with a preset static therapy threshold value, and judges whether a static therapy plan reminding signal needs to be sent out according to a comparison result;
If the value of the static therapy coefficient jl f is greater than or equal to the static therapy threshold value, the reminding module judges that a static therapy plan reminding signal needs to be sent out;
if the value of the static therapy coefficient jl f is smaller than the Yu Jing therapy threshold, the reminding module judges that the static therapy plan reminding signal does not need to be sent.
Example 3: the personalized static therapy plan generating method of the embodiment comprises the following steps:
The monitoring end monitors sleep data of a patient when the patient is in a sleep state, monitors physiological data of the patient in daily life of the patient, acquires dietary data of the patient at regular time, pre-processes the sleep data, the physiological data and the dietary data, the processing end extracts a body movement frequency index and a sleep duration deviation index in the sleep data, extracts a body weight deviation index in the physiological data, extracts a metabolic rate floating coefficient in the dietary data, comprehensively analyzes the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient, generates a static therapy coefficient, compares the static therapy coefficient with a preset static therapy threshold, judges whether a static therapy plan reminding signal is required to be sent according to a comparison result, the static therapy plan reminding signal comprises a voice prompt, a lamplight flickering prompt and the like, can send the reminding signal to a mobile terminal of the patient or a patient family member to remind the patient that medical treatment is required, and after the patient or the patient family member receives the reminding signal, the patient needs to take medical treatment in time.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (3)
1. A personalized static therapy plan generation system, characterized by: the device comprises a sleep monitoring module, a physiological monitoring module, a diet monitoring module, a characteristic extraction module, an analysis module and a reminding module;
sleep monitoring module: the sleep data processing device is used for monitoring the sleep data of the patient when the patient is in a sleep state and preprocessing the sleep data;
The physiological monitoring module: the method is used for monitoring physiological data of a patient in daily life of the patient and preprocessing the physiological data;
diet monitoring module: the method comprises the steps of acquiring diet data of a patient at fixed time, and preprocessing physiological data;
And the feature extraction module is used for: extracting a body movement frequency index and a sleep duration deviation index in sleep data, extracting a body weight deviation index in physiological data, and extracting a metabolic rate floating coefficient in diet data;
And an analysis module: comprehensively analyzing the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient to generate a static therapy coefficient;
A reminding module: comparing the static therapy coefficient with a preset static therapy threshold value, and judging whether a static therapy plan reminding signal needs to be sent out according to a comparison result;
The analysis module comprehensively calculates a body movement frequency index, a sleep duration deviation index, a body weight deviation index and a metabolic rate floating coefficient to generate a static therapy coefficient, and the expression is as follows:
Wherein jlf is a static therapy coefficient, tp is a body movement frequency index, SPC is a sleep duration deviation index, TPC is a body weight deviation index, DXF is a metabolic rate floating coefficient, alpha, beta, gamma and delta are proportional coefficients of the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient respectively, and alpha, beta, gamma and delta are all larger than 0;
The reminding module compares the static therapy coefficient with a preset static therapy threshold value, and judges whether a static therapy plan reminding signal needs to be sent according to a comparison result, wherein the method comprises the following steps of:
If the value of the static therapy coefficient jlf is greater than or equal to the static therapy threshold value, the reminding module judges that a static therapy plan reminding signal needs to be sent out;
if the value of the static therapy coefficient jlf is smaller than the Yu Jing therapy threshold, the reminding module judges that the static therapy plan reminding signal does not need to be sent out;
the data acquisition mode of the body movement frequency index is as follows:
Where tp is the body movement frequency index, i=1, 2, 3,..and n, n is the number of sampling time periods, and n is a positive integer, pl i represents the body movement frequency of the i-th sampling time period;
The calculation expression of the sleep duration deviation index is as follows:
Wherein SPC is sleep duration deviation index, sjm is actual sleep duration, jys is recommended sleep duration;
The calculation expression of the body weight deviation index is as follows:
wherein TPC is a body weight deviation index, sjt is an actual body weight, bzt is a healthy body weight;
the calculation expression of the metabolic rate floating coefficient is as follows:
Wherein DXF is metabolic rate floating coefficient, F (t) is metabolic rate variation of a patient, [ t x,ty ] is cortisol level early-warning period, and [ t i,tj ] is diet early-warning period.
2. A personalized static therapy plan generation system according to claim 1, wherein: the acquisition logic of the cortisol level pre-warning period is: the period when the cortisol content exceeds the content threshold value is the period of cortisol level early warning;
the acquisition logic of the diet early warning period is as follows: the period when the non-dietary time exceeds the time threshold is the period of dietary pre-warning.
3. A personalized static therapy plan generation method implemented by the generation system of claim 1 or 2, characterized in that: the generation method comprises the following steps:
S1: the monitoring end monitors sleep data of the patient when the patient is in a sleep state, monitors physiological data of the patient in daily life of the patient, and regularly acquires diet data of the patient;
S2: preprocessing sleep data, physiological data and diet data;
S3: the processing end extracts a body movement frequency index and a sleep duration deviation index in sleep data, extracts a body weight deviation index in physiological data and extracts a metabolic rate floating coefficient in diet data;
s4: comprehensively analyzing the body movement frequency index, the sleep duration deviation index, the body weight deviation index and the metabolic rate floating coefficient to generate a static therapy coefficient;
s5: and comparing the static therapy coefficient with a preset static therapy threshold value, judging whether a static therapy plan reminding signal needs to be sent out according to a comparison result, and sending the reminding signal to a mobile terminal of a patient or a family member of the patient for reminding.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110459325A (en) * | 2019-07-01 | 2019-11-15 | 江苏环亚医用科技集团股份有限公司 | A kind of method and apparatus of health control |
| CN110464319A (en) * | 2019-08-22 | 2019-11-19 | 合肥学院 | A kind of bracelet health monitoring systems |
| CN111063411A (en) * | 2019-10-23 | 2020-04-24 | 王向欣 | Overall healthy ecological management method and system |
| CN114639463A (en) * | 2022-03-21 | 2022-06-17 | 庞海丰 | Four-in-one comprehensive health management method and system |
| CN115590483A (en) * | 2022-10-12 | 2023-01-13 | 深圳市联代科技有限公司（Cn） | Smart phone with health measurement system |
| CN115910346A (en) * | 2022-12-28 | 2023-04-04 | 张家口市第一医院 | Monitoring and early warning system and method for diabetic patient |
| CN116741377A (en) * | 2023-06-19 | 2023-09-12 | 联硕智能(深圳)有限公司 | Health target recommendation record tracking method and device based on intelligent watch |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20210118547A1 (en) * | 2019-10-21 | 2021-04-22 | Singapore Ministry of Health Office for Healthcare Transformation | Systems, devices, and methods for self-contained personal monitoring of behavior to improve mental health and other behaviorally-related health conditions |
| US20230335258A1 (en) * | 2022-04-13 | 2023-10-19 | Food Rx and AI, Inc. | Methods and systems for multi-omic interventions for multiple health conditions |
-
2023
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Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110459325A (en) * | 2019-07-01 | 2019-11-15 | 江苏环亚医用科技集团股份有限公司 | A kind of method and apparatus of health control |
| CN110464319A (en) * | 2019-08-22 | 2019-11-19 | 合肥学院 | A kind of bracelet health monitoring systems |
| CN111063411A (en) * | 2019-10-23 | 2020-04-24 | 王向欣 | Overall healthy ecological management method and system |
| CN114639463A (en) * | 2022-03-21 | 2022-06-17 | 庞海丰 | Four-in-one comprehensive health management method and system |
| CN115590483A (en) * | 2022-10-12 | 2023-01-13 | 深圳市联代科技有限公司（Cn） | Smart phone with health measurement system |
| CN115910346A (en) * | 2022-12-28 | 2023-04-04 | 张家口市第一医院 | Monitoring and early warning system and method for diabetic patient |
| CN116741377A (en) * | 2023-06-19 | 2023-09-12 | 联硕智能(深圳)有限公司 | Health target recommendation record tracking method and device based on intelligent watch |
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