TW201629892A - Physiological monitoring feedback system and operating method thereof - Google Patents

Abstract

This invention provides an innovative physiological standard setting up way and methods, basing on the acknowledged physiological standard, and sets up the homogenous users' standard through classifying and statist via the theory of big data analysis. Furthermore, this invention is able to form a standard of individual person, for resting on the physiological monitoring feedback. Also, it generates the testing report via visualized method, assisting the users managing their health.

Description

Physiological monitoring feedback system and its operation method

A physiological monitoring feedback system and a method for operating the same, in particular, a physiological monitoring feedback system and a method for operating the same, which can perform big data analysis at different ratios and feed back to the user with an image report.

With the advancement of modern medical science and technology, the human society is aging, health-related products and services such as health, health care, music, sports, etc. are flourishing, and health has become the basic demand of the elderly society. The social cost of the country is important, and health management should be oriented towards life. Promote, automate and personalize the development of living standards.

For the public, the value of medical care is to save lives and help patients to slow down their health and deterioration. In essence, they need high professionalism and high cost, which is a remedy measure.

On the other hand, the needs of the elderly are to maintain a high standard of health, to delay aging, to be highly personalized and independent, and to be friendly and friendly, to become a life aid, and to treat medical conditions and improve living standards before treatment. Avoid the waste of medical resources.

It can be seen from the above that the traditional periodic health check is based on a clear disease, which is usually expensive and complicated, and the feedback information tends to be professionally diagnosed. For those who want to manage their own health, it is not easy to understand and cannot be integrated into daily life. Live and help a healthy life.

Gradually, many people seek to truly monitor health rather than detect disease, using fast and simple detection tools, hoping to interpret their physiological status at any time in life, autonomic nerve detection is one of them.

In the field of medical and health management, autonomic nerve detection is not only a reference for medical diagnosis, but also more valuable for self-health management and monitoring. The Autonomic nervous system (ANS) can be classified into two major categories, namely, the Sympathetic nervous system and the Parasympathetic nervous system.

Because most of the above two types of autonomic nerves have their own physiological response mechanisms, most of them have an Antagonist effect. The most classic example is the Fight-or-flight response. The sympathetic nervous system mainly helps the human body to enter. Focus, vigilance, preparation, and even excitement, so that we can cope with daytime work, thinking, on-the-spot reaction, exercise, and the system that enhances the ability of the human body to cope with external stress.

The relative parasympathetic nervous system assists the body to restore relaxation, allowing the system to recover, regain nutrient energy, and even repair abnormal organs, fight against bacterial invasion, and temporarily imbalance the system. This is a system for coping with internal physiological stress.

Autonomic nerve detection technology, clinically used Heart Rate Variability (HRV) analysis, which is the physiological signal collected by the subject's electrocardiogram (ECG) or photoplethysmography (PPG) for a period of time, that is, heartbeat The interval (RR interval, RRI) is used as a sample, and then comprehensive analysis is performed by using time domain analysis method, frequency domain analysis method or a combination thereof.

Undoubtedly, HRV testing has the price of medical testing and independent health management. Values, many studies have reported that heart rate variability indicators are closely related to physiological feelings, psychological levels, clinical outcomes, etc., and even can be used to target specific diseases such as cardiovascular disease, stroke, and surrounding areas. More facial nerve palsy, mental illness, depression, schizophrenia, etc. for more in-depth research.

At the level of healthy living, autonomic dysfunction is related to modern life stress. Patients with dysregulation are often identified as “no abnormalities in the examination results” through traditional health examinations, but they do have facts that affect their quality of life and affect health for a long time. If heart rate mutation detection can be properly applied, it can create the value of preventive medicine as a basis for personal self-care management, delaying disease occurrence and aging.

However, the first problem that exists is the lack of practical comparative data. The disease is objective, but there is subjective consciousness and feeling in health, just like the B0dy Mass Index (BMI). There are different health reference standards for different genders, different races, different ages and different occupations.

The same autonomic activity (SDNN), sympathetic activity (or low frequency index LF), parasympathetic activity (or high frequency index HF), etc., in different ethnic groups (eg gender, age, occupation) There are different health standards between different environments (eg climate, seasons), and it is clear that a mechanism is needed to establish appropriate health references for each ethnic group.

The second shortcoming is the need for personal monitoring. It is mentioned that the disease discrimination is objective, and the implementation of health management relies on subjective feelings. In addition, the self-regulating instinct of self-regulatory nerves will gradually adapt the body and feelings to stress and improper diet. Such as affecting a healthy life; at this time, the difference between physiological testing and health standards is provided, and the subject is not only feeling unconscious, but also too late; if he can monitor his or her physiological condition, he can immediately compare the physiological conditions of the individual for a short period of time. Significant Sudden changes provide warnings to remind users to adjust their diet and work and rest in order to achieve the efficacy of independent health assistants and to develop towards life.

The third is to test the readability of the report affects the willingness to automate the application. Heart rate variability test results are a series of complex data information. Interpretation requires comprehensive analysis. The traditional report readers are medical professionals. This problem does not exist. However, users of autonomous health management are users themselves. The report lacks a friendly and friendly interface.

That is to say, the analysis data provided by the traditional heart rate variability detection has two obstacles for the self-management users, such as application difficulties and interpretation errors. Only standard procedures for integrated interpretation and friendly imaging are proposed. The interface can meet the needs of autonomy.

For example, when autonomic nerve activity is normal, the heartbeat interval has considerable variability, rather than a rigid interval; conversely, a rigid heart rate interval, which represents a decline in heart regulation.

However, the traditional time-domain analysis method often uses the SDNN value to express the autonomic nerve activity. The high activity is young and healthy. However, in fact, professional interpretation must consider other factors, such as pressure, which may increase the SDNN value. Consider other data, such as activity divided into sympathetic nervous system activity and parasympathetic nervous system activity, while self-managed users usually do not have comprehensive analytical capabilities.

In addition, the information provided by the frequency domain analysis method can not be simply interpreted. It will calculate the Power Spectral Density (PSD) by Fourier transform with the Fourier transform, and then analyze the selection of each frequency band in the spectral density. Values representing various meanings, such as LF (Low Frequency) represents the activity of the sympathetic nervous system and the parasympathetic nervous system, HF (High Frequency) represents the activity of the parasympathetic nervous system, and the sum of energy TP (Total) Power) represents the value of the aforementioned LF plus HF, and the above is the detection result of the frequency domain analysis method.

When the subject is under stress, the endocrine hormones such as Epinephrine or Norrepinephrine increase Cortisol and increase the excitability of the sympathetic nervous system, while staying up late or insomnia It often causes hyperactivity in the sympathetic nervous system. Similarly, drug stimulation such as caffeine or stimulants can also produce the same physiological response to the sympathetic nervous system. In this case, if the frequency domain analysis method is used to interpret the energy state of the subject, It will receive a very high energy information (Total Power, TP), which is a fierce representation of the interaction between the sympathetic nervous system and the parasympathetic nervous system, rather than an adequate energy performance. In other words, the interpretation of energy should also consider other data.

Conversely, when the user is tired, cold, or infected with bacteria, the immune mechanism may be activated in the body, and the parasympathetic nervous system is so excited. If the frequency domain analysis method is also used at this time, the TP value may be caused by the high HF value. The wrong indication of the subject's full energy.

In fact, regardless of the data provided by the frequency domain analysis method or the time domain analysis method, a set of standard procedures for integrated interpretation is needed to reduce the risk of interpretation errors, and also requires an imaged interface to solve the obstacles of self-application. More need for personalized testing benchmarks as a personal health reference.

Traditional analysis reports are easily plagued by individual interpretations or lack of systematic judgment, whether in medical or autonomous health management applications. This is the biggest blind spot in the current report of autonomic nerve detection technology.

In view of the problems mentioned in the prior art, the present invention provides a physiological monitoring feedback system and a method for operating the same, wherein the physiological monitoring feedback system includes a detection module, A logic computing platform, a user data collection module, and a push module.

The detection module is configured to detect a detection result, the detection result includes a balance information, an activity information, and an energy information, and the logic operation platform includes a user database, an analysis system, wherein the user database and the detection The module is connected, and the user database is used to receive the detection result and stored in a newly opened or corresponding one of the user accounts.

The analysis system is connected to the user database, and the data analyzed by the analysis system is the detection result, wherein one of the first reference or the second reference system used in the analysis of the detection result is taken from a user reference and a recognized reference respectively. a homogenous user benchmark or a combination thereof, and the first benchmark adjusts the recognized benchmark and the homogenous user benchmark by a first ratio; and the second benchmark adjusts the user benchmark by a second ratio, This accepted benchmark and the homogenous user benchmark are combined.

The user benchmark, the recognized benchmark, and the homogenous user benchmark are generated by the analysis system by analyzing the qualified test result and a user basic data.

The user data collection module is connected to the user database, the user data collection module is configured to collect the user basic data and a self-assessment data, and the push module includes an early warning module and a report generation module. The group, the early warning module and the report generation module are respectively connected to the analysis system.

The invention further provides a method for operating a physiological monitoring feedback system, comprising the following steps.

First, step (a) is performed. A detection module detects a user, obtains a detection result, and then performs step (b). If the user is a new user, executes a new user mode, and if not, executes an old user mode.

The new user mode includes two steps. First, step (c1) is performed. A user data collection module collects a user basic data, and then performs step (d1). The analysis system analyzes the detection by using a first benchmark. result.

The old user mode also includes two steps. First, step (c2) is performed. A user data collection module collects a user basic data, and then performs step (d2). The analysis system analyzes the detection by using a second reference. result.

After performing the above steps (d1) or (d2), performing step (e), creating an interactive report, and then performing step (f), the user returns a self-rating data to the user data collecting module according to the interactive report. .

Then, step (g) is executed, the detection result and the self-assessment data are stored in a user database, and finally step (h) is performed. If the detection result is significantly changed compared with the previous detection result of the user, then The alert module notifies the user, a relative of the user, or a combination thereof.

1‧‧‧Test module

2‧‧‧Logical Computing Platform

21‧‧‧ User Database

22‧‧‧Analysis system

3‧‧‧ User Data Collection Module

4‧‧‧Pushing module

41‧‧‧Warning module

42‧‧‧Report generation module

51‧‧‧ Balance image

511‧‧‧Yang block

512‧‧‧Yan block

52‧‧‧Active images

521‧‧‧ Activity values

53‧‧‧Energy image

531‧‧‧ Energy values

ER‧‧‧Maximum Tai Chi Diameter

RR‧‧‧ Actual Tai Chi Diameter

Y‧‧‧ Tai Chi Yang

WR‧‧‧ Tai Chi Yang Diameter

X‧‧‧ Tai Chi Yin

BR‧‧‧ Tai Chi Diameter

P1‧‧‧

P2‧‧‧ Yang Duan

Q‧‧‧Yin and Yang balance

N‧‧‧Normal interval

T‧‧‧ time

Z‧‧‧ arbitrary test indicators

UCL‧‧‧ upper limit

DCL‧‧‧ lower limit

P‧‧‧ actual test value

(a)~(h)‧‧‧ steps

1 is a system structural diagram of a physiological monitoring feedback system of the present invention.

2(a) is a schematic diagram of a balanced information interval of the physiological monitoring feedback system of the present invention.

Figure 2 (b) is a schematic diagram of the interactive report of the physiological monitoring feedback system of the present invention.

Fig. 2(c) is another schematic diagram of the interactive report of the physiological monitoring feedback system of the present invention.

Fig. 3(a) is a schematic diagram of a Taiji diagram in the interactive report of the physiological monitoring feedback system of the present invention.

Fig. 3(b) is a schematic diagram showing the total diameter of the Taiji diagram in the interactive report of the physiological monitoring feedback system of the present invention.

Figure 3 (c) is a schematic diagram of the path of Taiji Yin and Taiji Yang in the interactive report of the physiological monitoring feedback system of the present invention. Figure.

Fig. 3(d) is a schematic diagram showing the depth of the Taiji diagram in the interactive report of the physiological monitoring feedback system of the present invention.

4 is a schematic diagram of a normal interval of an early warning module of the physiological monitoring feedback system of the present invention.

Figure 5 is a flow chart showing the operation method of the physiological monitoring feedback system of the present invention.

In order to understand the technical features and practical effects of the present invention, and can be implemented in accordance with the contents of the specification, the following is a detailed description of the preferred embodiment as shown in the following: Referring to FIG. 1, FIG. 1 is the present invention. The system structure diagram of the physiological monitoring feedback system. As shown in FIG. 1 , the physiological monitoring feedback system of the present invention comprises a detection module 1 , a logic operation platform 2 , a user data collection module 3 , and a push module 4 .

The detection module 1 is configured to detect a detection result, where the detection result includes a balance information, an activity information, and an energy information, and the logic operation platform 2 includes a user database 21 and an analysis system 22, wherein the user database 21 and The detection module 1 is connected, and the user database 21 is configured to receive the detection result and store it in a newly opened or corresponding user account.

The analysis system 22 is connected to the user database 21, and the data analyzed by the analysis system 22 is the detection result, wherein one of the first reference or the second reference system used in the analysis of the detection result is taken from a user reference, a recognized benchmark, a homogenous user benchmark, or a combination thereof, and the first benchmark adjusts the recognized benchmark and the homogenous user benchmark by a first ratio; and the second benchmark adjusts the user by a second ratio Baseline, this accepted benchmark, and a comprehensive analysis of this homogeneous user benchmark.

The user reference, the recognized reference, and the homogenous user reference are generated by the analysis system 22 by analyzing the qualified test result and the user basic data.

The user data collection module 3 is connected to the user database 21, the user data collection module 3 is configured to collect the user basic data and a self-assessment data, and the push module 4 includes an early warning module 41 and a The report generation module 42 is connected to the analysis system 22 by the early warning module 41 and the report generation module 42.

In this embodiment, the manner in which the physiological monitoring feedback system is mainly used may be determined according to whether the user is a new user or an old user, and the newly opened user account refers to the use of less than 30 samples of the detection result in the user. If the user is a new user, the user profile data is retrieved from the user through the user data collection module 3.

On the other hand, if the user is an old user but the user's basic information needs to be updated or changed, the updated user basic data can be retrieved from the user through the user data collection module 3, and then updated and stored in the corresponding data. In the user account.

The user data collection module 3 can provide a new or updated user basic data by using a paper questionnaire, an electronic questionnaire, a sample analysis instrument or a combination thereof in a hard or non-hard way. The provider can also use the search system in the user data collection module 3 to quickly check the user's dictation, filling or input data to determine whether the user has previously used the physiological monitoring feedback system.

The content of the user basic information may include the user's gender, age, occupation, blood type, weight, BMI, residence address, race, medical history, medication history, ribonucleic acid sequence, deoxyribonucleic acid sequence or a combination thereof. The type of data to be retrieved may be adjusted according to the service provider, and the invention is not limited thereto.

It must be emphasized here that the physiological monitoring feedback system provided in the present embodiment is selected to solve the many defects of the autonomic nerve detecting technology mentioned in the prior art.

First, the detection module 1 can be any device or instrument that can measure the heartbeat or pulse time interval, or more accurately, can output an electrocardiogram (ECG) or pulse that meets the conditional time data (in milliseconds). Photoplethysmography (PPG) devices, such as electrocardiographs, pulsers, and the like.

In the embodiment, the detecting module 1 uses the above device to record the heartbeat of the user in a quiet and awake state for 3 to 5 minutes, thereby observing the rate and change of the heart beat and obtaining the heartbeat of the user. Interval (RR interval, RRI).

The measurement result is analyzed by the analysis system 22 by heart rate variability (HRV), that is, time domain analysis and frequency domain analysis. The analysis results include balance information, activity information, and energy information.

In this embodiment, the time domain analysis method may use indicators such as SDN (Standard Deviation of all Normal to Normal intervals), SDANN index, SDNN index, and pNN50 (NN50 count divided by the total number of all NN intervals) for evaluation. The user's ability to adjust the heart.

The aforementioned frequency domain analysis method obtains the Power Spectral Density (PSD) by using a Fast Fourier Transform (FFT).

The power spectral density obtained by the above frequency domain analysis method can be regarded as the power of the segment (the activity of the sympathetic nervous system or the parasympathetic nervous system) in various frequency segments. For the standard adopted in this embodiment, see Table 1 below. .

Therefore, the balance information of the foregoing detection results includes LF, HF, and the activity information includes the results of the SDNN, SDANN index, SDNN index, and pNN50, and the energy information is the TP value, and then the information is transmitted to the analysis system 22.

The detection module 1 transmits the information to the analysis system 22 in the logic operation platform 2, and the analysis system 22 transmits the data in the standard JSON (JavaScript Object Notation) format, and the analysis system 22 can obtain the above by providing an API (Application Programming Interface). data.

Since the analysis system 22 is a Big Data analysis system, the personalization benchmark analysis can be performed in accordance with the balance information, activity information, and energy information in each user test result.

The foregoing personalization benchmark can be divided into a first benchmark and a second benchmark according to the difference between the old and new users. The two benchmarks are mainly composed of three elements, which are a user benchmark, a recognized benchmark, and a homogenous user benchmark.

The user benchmark is sampled from the user account of the same old user in the user database 21, and based on the comprehensive analysis of the test results of more than 30 samples in the past, the number of samples to be actually taken can be served by the service. The provider is set by himself, and the invention is not limited thereto.

The recognized benchmarks can be referenced to the benchmarks provided by government-funded government units, domestic or international journals, clinical research papers, etc., and then service providers are adjusted according to their professional judgments.

Finally, the calculation of the homogenous user benchmark is based on other user accounts in the user database 21 that have the same or similar user profile as the tested user, such as gender, age range, occupation, blood type, weight, BMI, a range of residential addresses, races, medical history, medication history, ribonucleic acid sequences, specific DNA sequences, or a combination thereof, and the homogeneity user benchmark is derived.

In this embodiment, the sample system for calculating the user reference and the homogenous user reference is not randomly selected, and the service provider can adjust and set various sampling conditions according to the needs thereof, such as setting the standard deviation range, and sampling the detection result. The limit value of the LF and HF in the test result is abnormal, or only the sampled user returns a good or very good test result in the self-assessment data, and the present invention is not limited thereto.

In addition, whether the user is a new user or not will also affect the setting of the personalization benchmark by the analysis system 22.

When the user is a new user, because the newly created user account does not have a sufficient number of samples, the analysis benchmark used for the new user's balance information, activity information, and energy information analysis is called The first reference, the first reference system analysis system 22 samples the balance information, the activity information, and the individual recognized benchmarks of the energy information in the user database 21. And the homogenous user benchmark is obtained by weighted averaging in a first ratio, wherein the first ratio is 50% of the accepted benchmark (including individual recognized benchmarks for balance, activity, energy information) and 50% of the homogenous user benchmark ( Contains individual homogeneity user benchmarks for balance, activity, and energy information).

On the other hand, if the user is an old user, the analysis criterion of the detection result is referred to as a second reference, and the second reference system analysis system 22 samples the balance information, the activity information, and the energy information individually recognized in the user database 21. The benchmark and homogenous user benchmarks are obtained by weighted average in a second ratio of 20% of the user benchmark (including individual user benchmarks for balance, activity, and energy information) and 40% of the accepted benchmark (including balance) , an individual recognized benchmark for activity, energy information, and 40% of this homogenous user benchmark (including homogenous user benchmarks for balance, activity, and energy information).

However, the first ratio of the first reference and the second ratio of the second reference are only selected according to the requirements of the embodiment, but may actually be changed according to the needs of the service provider according to the needs and the situation, and only reasonable. The invention is not limited thereto.

The balance information, activity information, and energy information in the detection results are respectively shown in Table 2 below the user criteria, the recognized benchmarks, and the homogeneity user benchmarks calculated for the analysis system 22.

Balance information (LF, HF)

When it is necessary to interpret the balance information in the user test result, it is defined as LF and HF in this embodiment.

First, we must confirm the accepted benchmarks for balanced information. We can refer to the benchmarks and standard deviations provided by research institutions with credibility at the time, domestic or international journals, clinical studies, etc., for the convenience of explanation. In this embodiment, we will balance the information. The accepted benchmarks are defined as LF1 and HF1.

The homogenous user benchmark for balancing information will be sampled by homogenous users who have the same or similar user profile (eg gender, age, etc.), and can also set various sampling conditions, such as time, self. The evaluation in the evaluation data, etc., the invention is not limited thereto.

In this embodiment, the sampling conditions used for the homogeneity user benchmark for balancing information are within 3 to 5 years for the homogenous user, and the self-assessment data itself is evaluated as a very good test result as a sample, and the number of samples must be More than or equal to 30 strokes, then take 95% of the data in the center of the normal distribution. Other parts are regarded as abnormal information and are not included in the reference. It should be noted that since LF and HF are in a pair relationship, both LF and HF must be Do an outlier check. When there is one of the exceptions, both values must be discarded.

Therefore, in this embodiment, the homogeneity user reference for balancing information is defined as LF2 and HF2, and the standard deviations are defined as S _LF2 and S _{HF2 , respectively} .

The user benchmark portion of the balanced information (if the user being tested is a new user can be skipped) is the same as the homogenous user benchmark of the balanced information described above, and various sampling conditions can be set, such as time and self-assessment data. The evaluation and the like, the invention is not limited thereto.

The sampling conditions used by the user benchmark for balancing information in this embodiment are measured by the user within 3 to 5 years, and the self-evaluation data itself is evaluated as a very good detection result as a sample, and the number of samples must be greater than or equal to Thirty strokes, after taking 95% of the data in the normal distribution, other parts are regarded as abnormal information, not included in the reference. It should be noted that since LF and HF are in a pair relationship, both LF and HF must be outliers. Verification, when there is one of the exceptions, both values must be discarded.

Therefore, the user base of the resulting balance information is defined as LF3 and HF3 in this embodiment, and the standard deviations are defined as S _LF3 and S _{HF3 , respectively} .

Next, the first reference or the second reference of the balance information interpretation reference is analyzed in the definitions in Table 3 below and the formulas 1-1, 1-2, 1-3 and 1-4.

It can be seen from Table 3 and Equations 1-1, 1-2, 1-3, and 1-4 that W1, W2, and W3 are the adjustment parameters in the first ratio and the second ratio, and the second ratio is For example, the user benchmark of 20% balanced information means (W3 x LF3 and W3 x HF3, W3=20%), and the recognized benchmark of 40% balance information (W1 x LF1 and W1 x HF1, W1=40%) And a homogenous user benchmark of 40% balanced information (W2 x LF2 and W2 x HF2, W2 = 40%).

In addition, the standard deviation of balanced information, user benchmarks and homogenous user benchmarks, the standard deviation is added to the standard deviation HFs or LFs calculation formula, and E2 and E3 are their control coefficients, which can also be adjusted by the service provider according to their professional judgment. Standard deviation to a reasonable value for specific interval size control.

That is, when the test result of the user is input to the analysis system 22, the analysis system 22 performs big data analysis in the manner described above to create a personalized first or second reference for "balanced information." (Using the first benchmark or the second benchmark is determined by whether the above is a new user), that is, LFa and HFa, and a plurality of unequal intervals are established by the values of the two standard deviations of LFs and HFs. Used to analyze the balance information in the test results.

In this embodiment, the sections are divided into four sections. Please refer to Table 4 and Table 5 below.

In addition to Table 4 and Table 5, please refer to FIG. 2(a) at the same time, and FIG. 2(a) is a schematic diagram of the balance information interval of the physiological monitoring feedback system of the present invention. The balance information of the LF and HF is obtained by the balance information in the test results falling in the intervals in Tables 4 and 5, and the balance information in the current test result of the user can be judged by referring to FIG. 2(a). situation.

In this embodiment, the interpretation of the balance information is judged by the yin and yang method. When the measured balance information (LF, HF) approaches the yin and yang balance Q, the balance information needs to be displayed as the yin and yang balance. The closer to P1, the more vaginal, and the closer to P2, the more impotence.

Activity information (S)

The objective of the interpretation is the activity information in the test results of the user, in this implementation The example is defined as S, which represents the SDNN measured by the user.

For the recognized benchmarks of active information, the method of obtaining the same is the same as the accepted benchmark of the above-mentioned balanced information. With reference to the benchmarks provided by the credible government units, domestic or international journals, clinical research papers, and the recognized benchmarks for the obtained active information. It is defined as S1 in this embodiment.

Then, the homogeneity user benchmark of the active information is sampled in the same manner as the homogenous user benchmark of the balanced information described above, and is performed for other homogenous users who have the same or similar user basic information (eg, age, gender, etc.) and the tested user. Sampling, various sampling conditions can be set, such as time, evaluation in self-assessment data, etc., and the invention is not limited thereto.

In this embodiment, the homogeneity user benchmark of the active information is evaluated by the homogenous user within 3 to 5 years, and the self-evaluation data itself is evaluated as a very good test result as a sample, and the number of samples must be greater than or It is equal to thirty strokes, and then takes 95% of the data in the center of the normal distribution. The other parts are regarded as abnormal information and are not included in the reference. Therefore, in this embodiment, the homogeneity user benchmark for active information is defined as S2, and the standard deviation is defined. For S _S2 .

Finally, the user benchmark of the active information is sampled in the same manner as the user benchmark of the balanced information. If the user being tested is a new user, the sampling method can be set to various sampling conditions, such as time and self-evaluation. The evaluation in the quantity data, etc., the invention is not limited thereto.

In this embodiment, the user reference of the active information is taken as a sample by the user within 3 to 5 years, and the self-assessment data is evaluated as a very good test result, and the number of samples must be greater than or equal to thirty. After the pen, 95% of the data in the center of the normal distribution is taken, and other parts are regarded as abnormal information, which is not included in the reference. Therefore, in this embodiment, the user reference of the active information is defined as S3, and the standard deviation is defined as S _S3.

Next, the first reference or the second reference of the activity information is analyzed in the definitions in Table 3 below and in Equations 2-1 and 2-2.

With respect to the first reference and the second reference of the balance information, the active information portion can be seen from Table 6 and Equations 2-1 and 2-2, and the adjustments of the W1, W2, and W3 systems are the first ratio and the second ratio. The parameters, and the same reason, the standard deviation of the active information interpretation is more accurate, and the regulation is added. The calculation formula of the standard deviation Sa, and E2 and E3 are their control coefficients, which can also be adjusted to a reasonable value by the service provider according to their professional judgment.

Therefore, the active information in the user test result can be interpreted in the interval by the first reference or the second reference of the active information.

The interval table of the activity information S in this embodiment is shown in Table 7 below.

That is to say, for the activity information in the test results measured by the user, Sa can be used as a reference, and the Ss of plus or minus Ss or positive and negative plus or minus 1.5 times can be determined as the interval of six levels of I+~VI+ to judge the current measurement of the user. The activity information S is a grade. Among them, I+ and I, IV+ and IV have the same properties, which are only used for warning and are not shown in Figure 2(b).

Energy Information (TP)

The target of the interpretation is the energy information in the test result of the user, which is defined as TP in this embodiment, and represents the TP measured by the user.

Recognized benchmarks for energy information, such as the aforementioned balance and activity information recognition The benchmarks are the same. Referring to the benchmarks provided by the government agencies at the time, domestic or international journals, clinical research papers, the accepted benchmark for energy information is defined as TP1 in this embodiment.

Similarly, the homogenous user benchmark for energy information is sampled in the same way as the homogenous user benchmark for balance and activity information, and has the same or similar user profile (eg age, gender, etc.) for other users. The homogeneity user performs sampling, and various sampling conditions, such as time, evaluation in self-assessment data, and the like, can be set, and the present invention is not limited thereto.

In this embodiment, the homogeneity user benchmark of the energy information is used by the homogenous user within 3 to 5 years, and the self-evaluation data itself is evaluated as a very good detection result as a sample, and the number of samples must be greater than or It is equal to thirty strokes, and then takes 95% of the data in the center of the normal distribution. The other parts are regarded as abnormal information and are not included in the reference. Therefore, in this embodiment, the homogeneity user benchmark for active information is defined as TP2, and the standard deviation is defined. For S _TP2 .

Finally, the user reference of the energy information is sampled in the same manner as the user benchmark of the balance and activity information. If the user being tested is a new user, the sampling method can be set, for example, time, The evaluation in the self-assessment data, etc., the invention is not limited thereto.

In this embodiment, the user reference of the energy information is taken as a sample by the user within 3 to 5 years, and the self-evaluation data itself is evaluated as a very good test result, and the number of samples must be greater than or equal to thirty Then, 95% of the data in the center of the normal distribution is taken, and other parts are regarded as abnormal information, and are not included in the reference. Therefore, in this embodiment, the user reference of the active information is defined as TP3, and the standard deviation is defined as S _TP3 .

Next, the first reference or the second reference of the energy information is analyzed in the definitions in Table 3 below and in Equations 3-1 and 3-2.

The interpretation interval table for interpreting the energy information interval of the current user test result is shown in Table 9 below.

Similarly, for the energy information TP of the currently measured detection result, the standard deviation TPs of each two levels is added and subtracted as the reference level of the energy information interval by using TPa as a reference.

All of the standard deviations used to determine the respective reference intervals can be obtained by means of table lookup or computer calculation, and the present invention is not limited thereto.

In summary, the new and old users in this embodiment, the adoption criteria, the reference content, and the adoption ratio are arranged in the following Table 10 to facilitate the public to better understand the meaning of the interpretation test result of the present invention.

However, the reference contents in Table 10 are selected for convenience of description in the present embodiment, and the present invention is not limited thereto, and any statistical or interpretation interval concept that is the same as the present invention belongs to the scope of the present invention. .

Therefore, after the analysis system 22 has judged the balance information, the activity information, and the energy information in the detection result, the analysis system 22 transmits the analyzed detection results to the user database 21 at the same time, and reports generation. Module 42 and early warning module 41.

The user database 21 is configured to store the analysis result of the analysis, and the report generation module 42 can generate an interactive report of the user, and then through the push module 4 The user is pushed to the user electronically or in writing, wherein the interactive report includes a balanced image 51, an active image 52, and an energy image 53, the balanced image 51, the active image 52, and the energy image 53. Refer to Figure 2(b) for the performance. Figure 2(b) is a schematic diagram of the interactive report of the physiological monitoring feedback system of the present invention.

Balanced image

As shown in Fig. 2(b), the balance image 51 is expressed by the yin and yang symbol, and the content thereof reflects the interval interpretation result of the balance information in the user detection result (refer to Table 4, Table 5, and Fig. 2(a) The closer to Q, the positive block 511 in the balance image 51 is equivalent to the area of the female block, and the closer to P1, the smaller the positive block 511 in the balanced image 51 is smaller than the negative block 512. The closer to P2, the larger the positive block 511 in the balanced image 51 is over the negative block 512.

In this embodiment, in order to facilitate the user to quickly and clearly understand the status of the balance information in the self-test result, the interactive report can further add the judgment interval in the vicinity of the balance image 51 by means of a watermark text or the like, and other blank spaces can be Indicate the meaning of each section.

For example, the interval of the balanced image 51 in this embodiment is as shown in Table 11 below.

As explained in Fig. 2(a), the user's test results LF and HF are regarded as the X coordinate and the Y coordinate. The closer the drop point is to Q, the more the yin and yang balance is, and the closer to P1, the degree (standard deviation) can be Low to high is divided into yang and yin and yin, and the closer to P2, the lower (to standard deviation) is divided into yin and yang.

Assuming that the balance information of the user test result is interpreted as "shadow" today, the portion of the negative block 512 in the balance image 51 occupies a considerable area ratio of the entire balance image 51.

However, the manner in which the above-mentioned Table 11 is interpreted for the balanced image 51 is only used in the present embodiment, and the present invention is not limited thereto, and any genre and grading in which the yin and yang balance is replaced by the sympathetic nervous system or the parasympathetic nervous system is balanced. Institutional or image descriptions are within the scope of the invention.

Active image

The interpretation and expression of the active image 52 is simpler and easier to understand. In the balance image 51, the active image 52 can also be filled with a watermark text or the like in the vicinity thereof, and other blank spaces can indicate the meaning represented by each interval.

Table 12 below is the meaning of the active image 52.

Please refer to Table 7 for the interpretation method in Table 12, and the activity value 521 exhibited by the active image 52 in Fig. 2(b) of the present embodiment will only show the image difference of the four levels (I~IV) in the middle of Table 7. The sample data that did not show excessive differences were the active information interpretation results in Table 12, which were too high overall activity, moderate overall activity, low overall activity and low overall activity, and were stained with gray dots in image expression. Whether the activity value 521 is filled with the image of the active battery is simple and clear, and the user is convenient for reviewing the activity information of the self.

Energy image

The energy image 53 is similar to the image representation of the active image 52 described above, and is represented in the form of a fuel tank in which the energy value 531 dyed with the gray dots points will present four equal image differences to fill the energy tank.

Refer to Table 13 below for the meaning of the energy image 53.

Similarly, the interpretation results in Table 13 only show the interpretation results (I~IV) in the middle four levels of Table 9, and do not show the sample data with excessive differences.

However, Table 12 and Table 13 can only express a single message. The present invention emphasizes that the interpretation of heart rate variability (HRV) analysis requires a standard procedure for integrated interpretation.

Since the interlocking image 51, the active image 52, and the energy image 53 are interlocked to affect the authenticity of the interpretation, the balance image 51, the active image 52, and the energy image in the interactive report of this embodiment The order of interpretation of 53 is to first interpret the balanced image 51, then to interpret the active image 52, and finally to interpret the energy image 53.

If the standard procedure for interpreting the detection data is presented in FIG. 2(b), the first step is to interpret the balance information by the balance image 51 presented in FIG. 2(b); and then interpret the active image 52 according to the balance information. Represents the meaning of active information; finally, the meaning of the first two interpretations of energy information (TP) is the energy image 53.

An example of the application of the above information interpretation program: when the yin and yang balance, the active image 52 is presented at a relatively high level, indicating that the subject has a younger neurological activity and vice versa. However, when the yin and yang are out of balance, the active image 52 indicates that the subject is unhealthy. When presented at a relatively high level, the subject may be under stress, or other factors, excessive heart rhythm, muscle tension or even arrhythmia. It is not related to activity; otherwise, it indicates that the subject has insufficient ability to mediate the heart rhythm, and the compression and strain are insufficient.

For the application of the above information interpretation program, for example, when the yin and yang are balanced, the active image 52 exhibits a subject with good activity. If the energy image 53 exhibits a relatively high level, it can be inferred that it still has high energy under static conditions. On the contrary, the energy is lower.

However, when the yin and yang are out of balance, the active image 52 exhibits a pressure-responsive subject. If the energy image 53 exhibits a relatively high level, it indicates that the subject operates the rhythm with high energy under static conditions, which is impotence. Physiological evidence, on the contrary, indicates that the subject is experiencing a false fire and insufficient energy. In Chinese medicine theory, the phenomenon of yin and yang is both loss, or yin and yang cannot be mutually independent.

In summary, the interactive report in this embodiment allows the user to quickly and clearly grasp their own balance information, activity information and energy information through the way of image expression and the standard procedure of integrated interpretation.

In addition, in this embodiment, the balance image 51, the active image 52, and the energy image 53 can be combined and displayed in the same taiji diagram.

Referring to FIG. 2(c), FIG. 2(c) is another schematic diagram of the interactive report of the physiological monitoring feedback system of the present invention. In addition to the information shown in FIG. 2(b), the information in FIG. 2(c) can also be displayed in the interactive report in this embodiment, and the left column can be seen in the above-mentioned user detection result in addition to the numerical value or numerical value. In addition to the measured values of the balance information (LF, HF), active information SDNN (S) and energy information (TP), it can also be seen that other service providers use the above balance information (LF, HF), active information (S) (this example is obtained by using SDNN) and energy information (TP) calculated by some information and suggestions. In addition, the fourth column from the left is a long strip of values (gradient dots) Part of it) Marks the level of the measured value. This graphical display makes it easier for the user to see the values in the test results at a glance.

It should be emphasized here that some of the interactive reports shown in FIG. 2(c) are only used in the present embodiment, and all the expressions are similar or close to the representation of FIG. 2(c), and all belong to the present invention. Within the scope of this.

Next, please refer to FIG. 3(a) to 3(d) at the same time, FIG. 3(a) is a schematic diagram of the Taiji diagram in the interactive report of the physiological monitoring feedback system of the present invention; FIG. 3(b) is an interactive report of the physiological monitoring feedback system of the present invention. Schematic diagram of the total diameter of the Taiji diagram; Fig. 3(c) is a schematic diagram of the yin and yang of the Taiji diagram in the interactive report of the physiological monitoring feedback system of the present invention; Fig. 3(d) is the depth of the Taiji diagram in the interactive report of the physiological monitoring feedback system of the present invention. Degree diagram.

As shown in Fig. 3(a), the interactive report can display the balance information, activity information and energy information in a taiji diagram, wherein the overall size of the taiji diagram, the image size of the Taijiyang Y, and the image of the Taiji Yin X The size, individual color or shade is determined by the balance information, activity information and energy information.

Next, as shown in FIG. 3(b), the maximum Taiji diameter ER and the actual Taiji diameter RR are shown in FIG. 3(b), wherein the maximum Taiji diameter ER indicates that the user is in a homogenous user and the age is in adolescence. The activity information, while the actual Taiji diameter RR is the activity information (S) measured by the current user.

Figure 3 (c) contains the Tai Chi Yang y tai yang diameter WR, and the Tai Chi Yin X taiji yin diameter BR, wherein the ratio of the Tai Chi Yang diameter WR and the Tai Chi Yin diameter BR represents the current In the balance information of the user test result, LF and HF are divided by LF by a fixed ratio (S=1~2, empirical value), and then divided by the ratio of HF. If the ratio is greater than 1, the taihe diameter WR The proportion in the Taiji diagram will increase. Conversely, if the ratio is less than 1, the proportion of the Taiji cathode diameter BR in the Taiji diagram will increase, meaning WR=LF/S, BR=HF.

Figure 3 (d) shows the gradual features of Figure 3 (b) and Figure 3 (c) above, so that the user's balance information (LF, HF), activity information (S) changes, and finally The energy information (TP) is revealed by the depth and depth. The deeper the taiji color from top to bottom in Figure 3(d), the higher the energy information (TP) measured by the user.

If the Taiji diagram at the bottom of Figure 3(d) is used as an interpretation, it can be judged that the balance information of the user shows that the Yin is greater than the yang, and the actual Taiji diameter RR and the depth of the two Taiji diagrams are compared. The activity information (S) and energy information (TP) measured at the bottom of the Taiji diagram are larger than the above two Taiji diagrams.

The above-mentioned Taiji diagram is not strictly defined in an interval. Actually, according to which interval, the overall size, the image size of Taiji Yang Y, the image size of Taiji Yin X, the depth or color are determined by the service provider. It is decided that the present invention is not limited thereto, and any one that can simultaneously present the overall size of the Taiji diagram, the image size of the Tai Chi Yang Y, the image size of the Tai Chi Yin X, the depth or the color are all within the scope of the present invention.

When the push module 4 pushes the interactive report to the user, a self-assessment is pushed to the user, and the user can feedback and evaluate whether the interactive report conforms to the current situation in the self-assessment, for example. It can be divided into bad, acceptable, ordinary, good and very good, etc., the invention is not limited thereto, and after the user completes the paper or electronic method, a self-assessment data is formed by the user. After the data collection module 3 collects, it is stored in the user database 21 The user account should be appropriate for future analysis by the system 22 sampling.

The self-rating data is fed back to the analysis system 22 as a method for analyzing sample sampling conditions, so that the accuracy of the analysis in each analysis system 22 is greatly increased, and the more self-assessment data fed back by the user is collected as a correction analysis. The basis of the homogenous user benchmark and the user benchmark in the system 22, the more the user measures the number of feedback, the more self-assessment data is returned, and the established interactive report is more in line with its own situation.

In this embodiment, the interactive report and self-evaluation pushed by the push module 4 can be integrated and implemented by using the HTML5 technology, so that the user can browse and feedback the user's self-assessment data through a mobile device such as a computer or a smart phone.

Regarding the part of the early warning module 41, the analysis system 22 firstly calculates the LF, HF, and SDNN values in the previous detection result of the user, and adds the VLF (Very Low Frequency) to the statistical test. For the meaning of the user's normal interval N, VLF, please refer to Table 1 above, which is the feedback control of the autonomic nervous system, and the frequency of the power spectral density is 0.0003~0.04Hz.

In the present embodiment, the analysis system 22 estimates the normal state of the autonomic nervous system of the user according to the detection result of the current user, and calculates the normal interval N of the user. If the user is a new user, the homogenous user is sampled as the standard for its normal interval N.

Please refer to FIG. 4 first, which is a schematic diagram of the normal interval of the early warning module of the physiological monitoring feedback system of the present invention. As shown in FIG. 4, the horizontal axis is time T, and the vertical axis is any detection index Z, wherein any detection index Z may be the aforementioned LF, HF, SDNN, VLF or a combination thereof, and the actual detection value P represents the user. The value of any index Z obtained at different times T.

The upper limit value UCL and the lower limit value DCL are obtained by statistically determining the arbitrary index Z according to the positive and negative standard deviations set by the service provider.

Therefore, the normal interval N refers to a range from the upper limit value UCL to the lower limit value DCL (part of the gray halftone dot in Fig. 4).

However, the normal interval N can be calculated in any statistical manner. The present invention is not limited thereto. Once the analysis system 22 determines that the detection result does not fall within the normal interval N, the analysis system 22 Notifying the early warning module 41, the early warning module 41 notifies the user, the relatives of the user or a combination thereof through the push module 4 via the newsletter, the social networking website, the communication software, the email or the APP message, and pay attention to the health condition thereof. .

In this embodiment, the data in the user database 21 can be combined with a social website or an APP, so that users can authorize each other to view or compare the detection results of themselves and others at different times.

Finally, please refer to FIG. 5. FIG. 5 is a flow chart of a method for operating the physiological monitoring feedback system of the present invention. As shown in FIG. 5, step (a) is first performed, the detecting module 1 detects the user, obtains the detection result, and then performs step (b). If the user is a new user, a new user mode is executed, if otherwise, a new user mode is executed. Old user mode.

The new user mode includes two steps. First, step (c1) is performed. The user data collection module 3 collects a user basic data, and then performs step (d1). The analysis system 22 analyzes the detection result by using the first reference. .

The old user mode also includes two steps. First, step (c2) is executed. The user data collection module 3 collects a user basic data, and then performs step (d2). The analysis system 22 analyzes the detection result by using the second reference. .

The analysis system can analyze the heart rate variability (HRV) spectrum analysis, and the first reference and the second reference are referred to Table 10.

After the step (d1) or (d2) is performed, the step (e) is executed to create an interactive report, and then the step (f) is executed, and the user returns the self-rating data to the user data collecting module 3 according to the interactive report.

Then, step (g) is performed, the detection result and the self-assessment data are stored in the user database 21, and finally step (h) is performed. If the detection result is significantly changed compared with the previous detection result of the user, The alert module 41 notifies the user, the relatives of the user, or a combination thereof by the push module 4 via a newsletter, a social networking site, a communication software, an email, or an APP message.

However, the above is only the preferred embodiment of the present invention, and the scope of the present invention is not limited thereto, that is, the simple equivalent changes and modifications according to the scope and description of the present invention remain It is within the scope of the present invention.

1‧‧‧Test module

2‧‧‧Logical Computing Platform

21‧‧‧ User Database

22‧‧‧Analysis system

3‧‧‧ User Data Collection Module

4‧‧‧Pushing module

41‧‧‧Warning module

42‧‧‧Report generation module

Claims (14)

A physiological monitoring feedback system includes: a detection module for detecting a detection result, the detection result comprising a balance information, an activity information and an energy information; a logic operation platform, the logic operation platform comprises: a user data a library connected to the detection module, the user database is configured to receive the detection result and stored in a newly opened user account; an analysis system is connected to the user database, and the analysis system analyzes the data as the detection Results; one of the first benchmarks used in the analysis of the test results includes a recognized benchmark and a homogenous user benchmark obtained by a first ratio comprehensive analysis, wherein the recognized benchmark and the homogenous user benchmark are analyzed by the analysis system. And the user data collection module is configured to collect the user basic data and a self-assessment data; and The push module includes an early warning module and a report generation module, and the early warning module generates the report module Group are respectively connected to the analysis system. A physiological monitoring feedback system includes: a detection module for detecting a detection result, the detection result comprising a balance information, an activity information and an energy information; a logic operation platform, the logic operation platform comprises: a user data a library connected to the detection module, the user database is configured to receive the detection The result is stored in a corresponding user account; an analysis system is connected to the user database, and the data analyzed by the analysis system is the detection result; and the second reference system used in the analysis of the detection result includes a user reference a recognized benchmark and a homogenous user benchmark are analyzed in a second ratio, wherein the user benchmark, the recognized benchmark, and the homogenous user benchmark are analyzed by the analysis system to meet the condition of the test result and a user basic Data generation; a user data collection module is coupled to the user database, the user data collection module is configured to collect the user basic data and a self-assessment data; and a push module includes an early warning module and A report generation module is connected to the analysis system and the report generation module. The physiological monitoring feedback system according to claim 1 or 2, wherein the detection module is a device having an electrocardiography (ECG) or a photoplethysmography (PPG) function. The physiological monitoring feedback system according to claim 1 or 2, wherein the user basic data includes the gender, age, occupation, blood type, body weight, BMI, residential address, ethnicity, medical history, medication history, and ribonucleic acid of the user. Sequence, deoxyribonucleic acid sequence or a combination thereof. The physiological monitoring feedback system of claim 1, wherein the first ratio is 40% to 60% of the recognized reference and 40% to 60% of the homogenous user reference. The physiological monitoring feedback system of claim 2, wherein the second ratio is 0% to 20% of the user benchmark, 40% to 50% of the recognized benchmark, and 40% to 50% of the homogenous user benchmark. The physiological monitoring feedback system according to claim 1 or 2, wherein the analysis system is big data (Big Data) analysis system. The physiological monitoring feedback system of claim 1 or 2, wherein the user data collection module provides a paper questionnaire, an electronic questionnaire, a sample analysis instrument, or a combination thereof. The physiological monitoring feedback system of claim 1 or 2, wherein the report generation module generates an interactive report and a self-assessment, the interactive report comprising a balanced image, an active image, an energy image or combination. The physiological monitoring feedback system according to claim 9, wherein the interaction report further comprises a Taiji diagram, the overall size of the Taiji diagram, the image size of the Taijiyang image, the image size of the Taiji Yin image, the shade of the Taijiyang and the Taiji Yin Or the color is determined by the balance information, the activity information, and the energy information. A method for operating a physiological monitoring feedback system, comprising: (a) detecting a user by a detection module to obtain a detection result; (b) executing a new user mode if the user is a new user, if otherwise performing an old User mode; the new user mode includes: (c1) a user data collection module collects a user basic data; (d1) the analysis system analyzes the detection result by a first reference; the old user mode includes: (c1) The user data collection module updates the user basic data; (d2) the analysis system analyzes the detection result by a second benchmark; (e) creates an interactive report; (f) the user returns a feedback according to the interactive report Self-assessment data to the user data collection model a group (g) storing the test result and the self-assessment data in a user database; and (h) an alert module notification if the test result is significantly different from the previous test result of the user The user, a relative of the user, or a combination thereof. The method for operating a physiological monitoring feedback system according to claim 11, wherein the analysis system analyzes the detection result by heart rate variability (HRV) analysis. The method for operating a physiological monitoring feedback system according to claim 11, wherein the interactive report in step (e) comprises a balanced image, an active image, an energy image, or a combination thereof. The physiological monitoring feedback system of claim 13, wherein the balanced image, the active image, and the energy image are interpreted in the order of reading the balanced image, and then the active image is interpreted, and finally the energy image is interpreted. image.