TW201918990A - Personalized health recommendation method - Google Patents

Abstract

A personalized health recommendation method includes receiving a plurality of physiological parameters, calculating the body mass index parameter according to height parameter and weight parameter of the physiological parameters, analyzing other parameter of the physiological parameters according to at least one physiological condition to obtain at least one first analysis result, analyzing the body mass index parameter to according to a somatotype condition to obtain a second analysis results, searching a plurality of health recommendation modules according to the least one first analysis result and the second analysis result to obtain at least one health recommendation information, and generating a health recommendation report according to the health recommendation information. The health recommendation report has the physiological parameters, the body mass index parameter and the health recommendation information.

Description

Personalized health advice method

The present invention relates to a technique for health management, and more particularly to a method for personalizing a health suggestion method.

In recent years, more and more people have begun to pay attention to physical health. In general, most of you go to a hospital or health check-up clinic for health checkups, and measure various physiological values so that you can understand your health status from your personal health check report.

However, due to the health check at the hospital or health check-up clinic, it is often necessary to make an appointment in advance and it is costly. In addition, it is time-consuming and laborious to perform a general health check only to easily understand physiological values such as blood pressure, blood sugar, and body fat percentage.

In an embodiment, a personalized health suggesting method includes: receiving a plurality of physiological parameters including a height parameter, a body weight parameter, and at least one other parameter, calculating a body mass index parameter according to the height parameter and the body weight parameter, according to at least A physiological condition analyzes other physiological parameters to obtain at least a first analysis result, analyzes a body mass index parameter according to the body condition to obtain a second analysis result, and searches for a complex array health suggestion module according to the first analysis result and the second analysis result to obtain At least one health advice information and a personalized health advice form based on at least one health advice information. The personalized health suggestion form has such physiological parameters, body mass index parameters, and at least one health suggestion information.

In summary, the personalized health suggestion method of the embodiment of the present invention can automatically analyze the physiological parameters of the subject and further provide a personalized health suggestion table for the corresponding health assessment.

The personalized health suggestion method according to the present invention can be implemented on a host having the processing module by executing a firmware or software algorithm by a processing module. In some embodiments, the personalized health suggestion method in accordance with the present invention can be implemented by a computer program product such that the personalized health suggestion method in accordance with any of the embodiments of the present invention can be completed when the host loads the program and executes it. In one embodiment, the computer program product can be a computer readable recording medium, and the computer readable recording medium stores a program for loading by the host. In another embodiment, the computer program product may also be the program itself for loading by the host and transmitted to the host via wired or wireless means.

FIG. 1 is a schematic diagram of a personalized health management system according to an embodiment of the present invention. 2 is a functional block diagram of one embodiment of the host of FIG. 1. FIG. 3 is a schematic diagram of a personalized health management system according to another embodiment of the present invention. 4 is a functional block diagram of one embodiment of the host of FIG. Referring to FIGS. 1 through 4, the personalized health management system 10 includes a host 110 and a multimedia platform 130. The host 110 can connect to the multimedia platform 130 via a network signal in a wired or wireless manner.

The host 110 includes a user interface 111, a processing module 112, and a network module 113. The processing module 112 is coupled to the user interface 111 and the network module 113. The processing module 112 is connected to an external component (eg, the multimedia platform 130) through the network module 113. The processing module 112 is adapted to perform a health assessment of the subject and generate a corresponding personalized health advice form.

The user interface 111 is adapted to allow the user (subject or operator) to set various parameters. For example, the user interface 111 can be used by a user to input or set physiological parameters and/or personal parameters. The physiological parameters obtained through the user interface 111 may be, but are not limited to, a height parameter and/or a weight parameter. The personal parameters obtained via the user interface 111 may be, but are not limited to, name parameters, address parameters, phone parameters, user accounts, and the like.

In some embodiments, the personalized health management system 10 can further include one or more gauges, and the processing module 112 can obtain one or more physiological parameters (eg, height parameters, weight parameters, Other physiological parameters or any combination thereof). In one embodiment, the measuring instrument can be an external measuring instrument 120, and the external measuring instrument 120 is connected to the processing module 112 signal, as shown in FIG. 1 and FIG. The external measuring instrument 120 can be used to obtain the physiological parameters of the subject by measuring the physiological state (and direct input) of the subject. The external measuring instrument 120 can directly measure the physiological state of the subject to generate corresponding physiological parameters. In addition, the external measuring instrument 120 may also be based on at least one physiological parameter of the subject input to the external measuring instrument 120 (for example, inputting a gender parameter, an age parameter, a height parameter, a weight parameter, and the like) of the subject. Measuring the physiological state of the subject produces another physiological parameter (eg, body fat rate parameter, etc.). In one embodiment, the processing module 112 can be connected to the external measuring instrument 120 via the network module 113 (not shown). In another embodiment, the host 110 can further include a connection unit 114, and the processing module 112 is coupled to the connection unit 114. The processing module 112 receives one or more physiological parameters obtained by the external measuring instrument 120 through the connecting unit 114. The physiological parameter obtained by the external measuring instrument 120 can be measured by the external measuring instrument 120. The connection unit 114 can be a connection port or another network module. It should be understood that although the drawing is illustrated by an external measuring instrument 120 (and a connecting unit 114), in practice, a plurality of external measuring instruments can be provided according to different physiological parameters to be measured. 120, and the external measuring instruments 120 are connected to the processing module 112 through one or more connecting units 114.

In another embodiment, the gauge can be directly integrated with the host 110. In other words, the host 110 can further include a built-in measuring instrument 120', and the built-in measuring instrument 120' is connected to the processing module 112 signal, as shown in FIG. 3 and FIG. The built-in measuring instrument 120' can be used to obtain (produce) corresponding physiological parameters by measuring the physiological state of the subject.

In yet another embodiment, the host 110 can have a built-in built-in gauge 120' and an external external gauge 120 (not shown).

Here, although the graph measuring instrument (the external measuring instrument 120 of FIG. 1 and the built-in measuring instrument 120' of FIG. 2) is an example of a blood pressure meter, the measuring instruments may be but not Limited to body fat meter, height gauge, weight scale or a combination thereof. In one embodiment, the body fat meter, the height gauge, the weight scale, and the like can be integrated into an external measurement system to simultaneously obtain a plurality of physiological parameters required.

FIG. 5 is a flowchart of a personalized health suggestion method according to an embodiment of the present invention.

Please refer to Figure 1 to Figure 5. In the evaluation process, first, the processing module 112 receives a plurality of physiological parameters of the subject (step S201). Among them, the physiological parameters include the height parameter, the body weight parameter and other parameters of the subject. Other parameters may be gender parameters, age parameters, blood pressure parameters, body fat rate parameters, or a combination thereof. Here, the physiological parameters may be from the user interface 111, the external measuring instrument 120, the built-in measuring instrument 120' or a combination thereof. In one embodiment, the processing module 112 can only receive physiological parameters transmitted by the user interface 111. In another embodiment, the processing module 112 can be a physiological parameter transmitted only by the measuring instrument (the external measuring instrument 120 and/or the built-in measuring instrument 120'). In another embodiment, the processing module 112 may also be a part of the physiological parameters transmitted by the receiving measuring instrument (the external measuring instrument 120 and/or the built-in measuring instrument 120') and via the user interface 111. Receive another part of the physiological parameters.

Next, the processing module 112 calculates a body mass index (BMI) parameter according to the height parameter and the body weight parameter (step S202). In an embodiment of step S202, the processing module 112 can calculate the body mass index parameter according to the following formula 1.

BMI=W/H2 Equation 1.

Among them, BMI stands for body mass index parameter, W stands for weight parameter and H stands for height parameter. Here, the unit of BMI is kg/m2.

Moreover, the processing module 112 analyzes the corresponding other parameters according to the at least one physiological condition to obtain the first analysis result T1 (step S203). Wherein, each physiological condition is a physiological threshold preset by the corresponding physiological parameter. In one embodiment, the physiological threshold may include one or more thresholds, such as a single value, a combination of an upper threshold (upper limit) and a lower threshold (lower limit), or a combination of three or more thresholds, and the like. In an embodiment, when the physiological threshold can also be a single value, the first analysis result T1 can be a judgment result that the corresponding physiological parameter is in a normal state (the physiological parameter is less than or equal to a single value), or the physiological parameter is in the The judgment result of the abnormal state (physiological parameter is larger than a single value). In another embodiment, when the physiological threshold is a combination of an upper threshold and a lower threshold, the first analysis result T1 may be that the physiological parameter is in a normal state (the physiological parameter falls within a range formed by the upper threshold and the lower threshold) The judgment result and the physiological parameter are judged as being too high (the physiological parameter is greater than the upper threshold) or the physiological parameter is too low (the physiological parameter is less than the lower threshold). In still another embodiment, when the physiological threshold is a combination of an upper threshold and a lower threshold, the first analysis result T1 may be a judgment result that the physiological parameter system is in a normal state (the physiological parameter is less than a lower threshold), and the physiological parameter is in a The judgment result of the unqualified state (the physiological parameter falls within the interval formed by the upper threshold and the lower threshold) or the judgment result that the physiological parameter is in the warning state (the physiological parameter is greater than the upper threshold). In still another embodiment, when the physiological threshold is a combination of three or more thresholds, the first analysis result T1 may be a judgment result that the physiological parameter system is in a normal state, the physiological parameter is in a state of being unqualified, or The physiological parameter is the result of the judgment of the warning state. For example, when the other parameters received by the processing module 112 are blood pressure parameters, the physiological conditions corresponding to the blood pressure parameters are systolic blood pressures of 120, 139, and 140 mmHg (mmHg) and diastolic blood pressures of 80, 89, and 90 mm. Mercury column. When the systolic blood pressure parameter of the user is less than 120 mmHg (mmHg) and the diastolic pressure is less than 80 mm Hg, the first analysis result T1 is that the blood pressure parameter is in a normal state. When the user's blood pressure parameter has a systolic blood pressure of between 120 and 139 mm Hg or a diastolic blood pressure of between 80 and 89 mm Hg, the first analysis result T1 is a warning state of the blood pressure parameter. When the systolic blood pressure parameter of the user is greater than or equal to 140 mmHg or the diastolic pressure is greater than or equal to 90 mmHg, the first analysis result T1 is that the blood pressure parameter is in an unqualified state.

The processing module 112 also analyzes the body mass index parameter according to the body condition to obtain the second analysis result T2 (step S204). Wherein, the body condition refers to the upper limit threshold and/or the lower limit threshold of the body shape corresponding to the body mass index parameter, and the second analysis result T2 refers to the body condition index is determined by the body condition to be in a large body (body mass) The index parameter is greater than the upper limit of the body size, the moderate body type (the body mass index parameter falls within the interval between the upper limit threshold and the lower limit threshold), or the judgment result of the small body type (the body mass index parameter is smaller than the lower limit threshold). Among them, the general type may cover an overweight body type, an obese body type, or a combination thereof. Among them, the small body type can cover the Underweight body type.

Then, the processing module 112 searches the complex array health suggestion module M according to the first analysis result T1 and the second analysis result T2 to obtain at least one health suggestion information N (step S205). Here, the first analysis result T1 and the second analysis result T2 obtained by each analysis are associated with a set of health suggestion modules M, and the health suggestion module M includes at least one health suggestion information N. In one embodiment, the complex array health suggestion module M can be recorded in a health suggestion lookup table T, and each set of health suggestion modules M includes one or more health suggestion information N, as shown in FIG. The health suggestion lookup table T may include a complex array health suggestion module M, and each set of health suggestion modules M has at least one first analysis result T1, one second analysis result T2, and one or more suggestion categories T3 associated with each other. Health advice information N. Among them, the types of these suggested categories T3 are, for example, but not limited to, calorie suggestions, activity suggestions, medical advice, and dietary advice. In other words, the processing module 112 can search the complex array health suggestion module M with the first analysis result T1 and the second analysis result T2 to find a set of health suggestion modules having both the first analysis result T1 and the second analysis result T2. M, and read the health suggestion information N of the health suggestion module M. In one embodiment, the health suggestion lookup table T can be stored in the built-in storage module 115 of the host 110. In other words, the host 110 can further include a storage module 115, and the storage module 115 is coupled to the processing module 112.

Then, the processing module 112 generates a personalized health suggestion table P having the health suggestion information N, the physiological parameter and the body mass index parameter according to the obtained health suggestion information N (step S206). In an embodiment, as shown in FIG. 7, the personalized health suggestion table P can have information such as height parameters, body weight parameters, other parameters, body mass index parameters, health advice information N, etc. depending on the layout design. In another embodiment, as shown in FIG. 7, the personalized health suggestion table P can also have more information such as personal parameters and/or user accounts depending on the layout design. In still another embodiment, when the first analysis result T1 is that the physiological parameter is in an abnormal state (eg, a warning state, a failure state, an excessive state, an excessive state, an abnormal state, etc.), or is the second Analysis Results When the T2 body mass index parameter is of an abnormal body type (eg, large or small body type), the personalized health suggestion form P may indicate a reminder mark E at the corresponding physiological parameter or body mass index parameter (eg, special Symbol, text, or present in a special color, a larger font, a thicker font, or a combination thereof.) Here, the reminder mark E can mark an abnormal state (eg, alert state, unqualified state, excessively high state, too low state, abnormal state, etc.) and/or an abnormal body type (eg, a large size or a small body type). In addition, in some implementation manners, normal parameters (such as normal state, moderate body shape) and abnormal parameters (such as warning state, failure state, excessive state, too low state, and abnormal state) can be respectively marked with different reminder marks E. , large or small). In some implementations, the personalized health suggestion table P may further include a field of the specific item Is to indicate the name of the parameter corresponding to the reminder mark E, and the like. In some implementations, the layout size of the personalized health suggestion table P can be A4 or other settings depending on the print settings or layout settings.

In some embodiments, the processing module 112 can transmit the generated personalized health suggestion table P to the multimedia platform 130 through the network module 113 (step S207), and then receive the multimedia platform 130 through the network module 113. A sequence of printed numbers is transmitted (step S208). The print serial number is associated with the personalized health suggestion form P. In an implementation manner, when the multimedia platform 130 receives the personalized health suggestion table P transmitted by the host 110 via the network, the multimedia platform 130 generates a serial number and receives the personalized health suggestion table P and the generated This print serial number is stored together in an association relationship. At the same time, the multimedia platform 130 will transmit the generated print serial number to the host 110 via the network. In an implementation manner, the multimedia platform 130 can be a multimedia device (Multiple Media Kiosk, MMK). In another implementation, the multimedia platform 130 can be a combination of a multimedia machine and a server connected via a network communication. In yet another embodiment, the multimedia platform 130 can be a remote server.

In an embodiment, after receiving the print serial number (step S208), the processing module 112 displays the received print serial number on the user interface 111 (step S209) for the subject to record. Subsequently, the testee can record the serial number to any multimedia machine to view and/or print the subject's personalized health suggestion form P.

In another embodiment, after receiving the print serial number (step S208), the processing module 112 can control the print module 116 to output the received print serial number (step S209). In other words, the host 110 can further include a print module 116, and the print module 116 is coupled to the processing module 112. Here, the printing module 116 prints a document having a printing serial number to provide to the subject. The subject can view and/or print the personalized health recommendation form P of the subject by printing the serial number recorded on the document to any multimedia machine.

In one embodiment, the processing module 112 can store the physiological parameters, the body mass index parameters, and the personalized health suggestion table P obtained during the evaluation process in the built-in storage module 115 and/or the remote servo. In the 140. In other words, the personalized health management system 10 can further include the server 140, and the processing module 112 is connected to the server 140 through the network module 113. In an implementation, the server 140 can be the same as the server to which the multimedia platform 130 belongs. In another implementation, the server 140 can be independent of the server to which the multimedia platform 130 belongs. In yet another embodiment, the server 140 can be integrated with a server to which the multimedia platform 130 belongs to form a server module.

In some implementations, the physiological parameters, the body mass index parameters, and the personalized health suggestion table P obtained during the evaluation process can be stored corresponding to the user account to which the subject belongs.

FIG. 8 is a flowchart of a personalized health suggestion method according to another embodiment of the present invention. Please refer to Figures 1 to 4 and Figure 8. In some embodiments, the processing module 112 can first receive the login information of the subject via the user interface 111 (step S301). The login information includes the user account of the subject. In addition, the login information may further include the user password of the subject. The login information is transmitted to the server 140 via the network for authentication. After the server 140 returns the successful authentication message to the host 110, the host 110 can access the history record of the user account in the server 140. In other words, the processing module 112 stores the physiological parameters, the body mass index parameters, and the personalized health suggestion table P obtained during the evaluation process as a history record in the server 140 via the network module 113.

Moreover, the processing module 112 can search the historical record of the user account from the database to obtain at least one historical physiological parameter (ie, the physiological parameter obtained in the previous evaluation process) and the historical body mass index parameter of the previous storage from the historical record ( That is, the body mass index parameter obtained by the previous evaluation process) (step S302). After step S302, the processing module 112 compares each historical physiological parameter with the corresponding physiological parameter of the physiological parameter obtained this time to obtain a physiological evaluation result (step S303) and the comparison historical body mass index parameter. The body mass index parameter currently calculated is obtained to obtain a body mass rating result (step S304). At this time, in step S206, the processing module 112 generates the health suggestion information N, the physiological parameter, the body mass index parameter, the physiological evaluation result and the body according to the obtained health suggestion information N, the physiological evaluation result and the body quality evaluation result. Personalized Health Recommendation Form P for quality assessment results. In other words, the personalized health suggestion form P has a physiological evaluation result and a body quality evaluation result Ic. In addition, in step S206, the processing module 112 may further generate health suggestion information N, physiological parameters, body mass index parameters, at least one historical physiological parameter and historical body mass index parameters, physiological evaluation results, and body quality evaluation results Ic. Personalized Health Recommendation Form P.

FIG. 9 is a flowchart of a personalized health suggestion method according to still another embodiment of the present invention. Please refer to Figure 9. In another embodiment, the processing module 112 may generate a heat recommendation intake amount according to a plurality of physiological parameters (step S401).

In one embodiment, the processing module 112 can generate a calorie recommended intake based on the second analysis result T2, the body weight parameter, and the age parameter.

For example, when the body mass index parameter is less than the body lower limit threshold or between the body upper limit threshold and the body lower limit threshold, the processing module 112 multiplies the body weight parameter by 30 kcal to obtain the calorie recommended intake (steps) S401a). For example, the second analysis result T2 is that the body mass index parameter is a small body type or a moderate body type, and the processing module 112 multiplies the body weight parameter by 30 kcal to calculate the calorie recommended intake amount.

When the body mass index parameter is greater than the body size upper threshold and the age parameter is greater than the age threshold, the processing module 112 multiplies the body weight parameter by 30 kcal to obtain the calorie recommended intake (step S401b). For example, the upper age threshold of the corresponding age parameter is 64 years old, and when the second analysis result T2 is that the body mass index parameter is of a large type and the first analysis result T1 of the corresponding age parameter is greater than the upper age threshold, the processing module 112 Calculate the calorie recommended intake by multiplying the weight parameter by 30 kcal.

When the body mass index parameter is greater than the body upper limit threshold and the age parameter is between the upper age threshold and the lower age threshold, the processing module 112 multiplies the weight parameter by 30 kcal and then deducts 500 kcal to obtain the calorie recommended intake ( Step S401c). For example, the upper age threshold for the age parameter is 64 years old and the lower age threshold is 18 years old. When the second analysis result T2 is the body mass index parameter is a large type and the first analysis result corresponding to the age parameter is T1. When between the upper age threshold and the lower age threshold, the processing module 112 multiplies the weight parameter by 30 kcal and deducts 500 kcal to obtain the calorie recommended intake to calculate the calorie recommended intake.

Furthermore, in step S206, the processing module 112 generates a personalized health suggestion table P based on the obtained health suggestion information N and the calorie recommended intake amount. In other words, the personalized health suggestion form P has health suggestion information N, physiological parameters, body mass index parameters and calorie recommended intake (and having at least one historical physiological parameter and historical body mass index parameters, and/or having various physiological evaluation results and/or Body quality evaluation results Ic). In one embodiment, the amount of calorie intake recommended can be directly recorded in the personalized health recommendation form P. In another embodiment, the calorie recommended intake may also be recorded in the health suggestion information table N in the health suggestion lookup table T in the health suggestion information N in the health suggestion information N, and then recorded in the health suggestion information N Personalized Health Recommendation Form P.

In an embodiment, after step S401, the processing module 112 can further generate food distribution information according to the calorie recommended intake amount (step S402). The food distribution information includes a plurality of different food categories F1 and a recommended number F2 of each food category F1. In other words, the calorie recommended intake is associated with a suggested number F2 of a plurality of different food categories F1. In one embodiment, the processing module 112 can search a food recommendation table F with the calculated calorie recommended intake amount to find a set of recommended number F2 associated with the recommended calorie intake amount. Here, the food recommendation table F includes a plurality of food categories F1 suggested corresponding to different calorie recommended intakes and a recommended number F2 corresponding to each food category F1, as shown in FIG. The food suggestion table F can be pre-stored in the built-in storage module 115 of the host 110.

Furthermore, in step S206, the processing module 112 generates a personalized health suggestion table P based on the obtained health suggestion information N and the recommended number of copies F2 of the plurality of different food categories F1. In other words, the personalized health suggestion form P has a health suggestion information N, a physiological parameter, a body mass index parameter, and a recommended number F2 of a plurality of different food categories (and having a calorie recommended intake, and/or having at least one historical physiological parameter and Historical body mass index parameters, and / or have various physiological evaluation results and body quality evaluation results Ic). Among them, the food category may be whole grain rhizomes (the recommended unit of F2 is the bowl), the peas meat and eggs (the recommended number of parts is F2), and the dairy category (the recommended unit of F2 is the cup) ), vegetables (the recommended number of units for F2 is a dish), fruits (the recommended number of units for F2 is a share), oils and fats (the recommended number of units for F2 is a share), nut seeds (recommendation) The unit of the number of parts F2 is a part) or any combination thereof. Here, the food category of the recommended number of copies F2 obtained by the processing module 112 may be five, six or seven in total.

In an embodiment, after step S402, the processing module 112 may further obtain at least one recommended food of the food category according to the recommended number of copies F2 of each food category. Wherein, the recommended number F2 of each food category may be associated with at least one recommended food. In one embodiment, the processing module 112 can search for a recommended food list S by using the recommended number of copies F2 of the plurality of different food categories to find the corresponding recommended food. Here, the recommended food list S includes suggested foods recommended by different recommended portions F2 corresponding to different food categories, as shown in FIG. For example, the recommended food category for whole grain roots can be rice or whole wheat toast. In one embodiment, the number of at least one recommended food for each food category obtained by the processing module 112 may be one. In another embodiment, the number of at least one recommended food for each food category obtained by the processing module 112 may be plural, for example, recommended food for breakfast, recommended food for Chinese food, and recommended food for dinner.

In another embodiment, after step S402, the processing module 112 may further obtain a photo of at least one recommended food and each recommended food of the food category according to the recommended number of copies F2 of each food category. In one embodiment, the processing module 112 can search for a recommended food list S by using the recommended number of copies F2 of the plurality of different food categories to find the corresponding recommended food and its photograph. In other words, the recommended food list S includes suggested foods and photographs (not shown) recommended by different recommended portions F2 corresponding to different food categories.

The recommended food list S can be pre-stored in the built-in storage module 115 of the host 110.

Furthermore, in step S206, the processing module 112 generates a personalized health suggestion table P according to the obtained health suggestion information N, the recommended number of copies F2 of the plurality of different food categories, and the recommended foods (and photos thereof) of the respective food categories. . In other words, the personalized health advice form P has health advice information N, physiological parameters, body mass index parameters, recommended number F2 of a plurality of different food categories, and recommended foods for each food category (and photos with recommended foods, and/or Or have a calorie recommended intake, and/or have at least one historical physiological parameter as well as a historical body mass index parameter, and/or have various physiological evaluation results and body quality evaluation results).

In an embodiment of step S204, as shown in FIG. 12, the processing module 112 compares the body mass index parameter and the body condition (step S204a) to determine whether the body mass index parameter satisfies the body condition. Here, the body condition includes a body upper limit threshold and a body lower limit threshold. When the body mass index parameter is greater than the body size upper threshold, the processing module 112 obtains the second analysis result T2 as the body mass index parameter is of a general type (step S204b). When the body mass index parameter is less than the body size lower limit threshold, the processing module 112 obtains the second analysis result T2 that the body mass index parameter belongs to a small body type (step S204c). When the body mass index parameter is between the body upper limit threshold and the body lower limit threshold, the processing module 112 obtains the second analysis result T2 that the body mass index parameter belongs to the moderate body type (step S204d).

In an embodiment of step S203, the processing module 112 may analyze the same physiological parameter according to a plurality of different physiological conditions to obtain a first analysis result T1 corresponding to the physiological parameter. In an embodiment, when the physiological parameters received by the processing module 112 are gender parameters, age parameters, and body fat rate parameters, the gender thresholds of the physiological conditions corresponding to the gender parameters are male and female, and physiological conditions corresponding to the age parameters. The age threshold is 30 years old, and the body fat rate threshold corresponding to the physiological conditions of the body fat rate parameter is 20%, 25%, 30%. When the gender parameter of the user is male, the age parameter is less than 30 years old, and the body fat percentage parameter is less than 20%, the first analysis result T1 corresponding to the body fat percentage parameter is normal; when the user's gender parameter is female, the age parameter is less than 30 years old and the body fat percentage parameter is less than 25%, the first analysis result T1 corresponding to the body fat percentage parameter is normal; when the user's gender parameter is male, the age parameter is greater than 30 years old and the body fat percentage parameter is less than 25%, corresponding The first analysis result T1 of the body fat percentage parameter is a normal state; when the gender parameter of the user is female, the age parameter is greater than 30 years old, and the body fat percentage parameter is less than 30%, the first analysis result T1 corresponding to the body fat percentage parameter is normal. status. When the gender parameter of the user is male, the age parameter is less than 30 years old, and the body fat percentage parameter is greater than or equal to 20%, the first analysis result T1 corresponding to the body fat percentage parameter is in an unqualified state; when the user's gender parameter is female, age The parameter is less than 30 years old and the body fat percentage parameter is greater than or equal to 25%, and the first analysis result T1 corresponding to the body fat percentage parameter is in an unqualified state; when the user's gender parameter is male, the age parameter is greater than 30 years old and the body fat percentage parameter is greater than Equal to 25%, the first analysis result T1 corresponding to the body fat percentage parameter is in an unqualified state; when the user's gender parameter is female, the age parameter is greater than 30 years old and the body fat percentage parameter is greater than or equal to 30%, corresponding to the body fat rate parameter The first analysis result T1 is in a failed state.

Taking an exemplary example as an example, the physiological parameters received by the processing module 112 are gender parameters, age parameters, blood pressure parameters, and body fat percentage parameters, wherein the age parameter is 20 years old, and the systolic blood pressure parameter is less than 120 mm Hg (mmHg). When the diastolic blood pressure is less than 80 mm Hg, and the gender parameter of the user is female, the age parameter is less than 30 years old and the body fat percentage parameter is less than 25%. According to this, the first analysis result T1 corresponding to the blood pressure parameter is the normal state, the first analysis result T1 corresponding to the body fat percentage parameter is the normal state, and the first analysis result T1 corresponding to the age parameter is the 18 to 64 year old interval. In addition, the second analysis result T2 of the body mass index parameter is a small body type. The processing module 112 finds a set of health suggestion modules M corresponding to the first analysis result T1 and the second analysis result T2 from the complex array health suggestion module M of the health suggestion lookup table, and the health suggestion module of the group The M has at least one health suggestion information N, for example, a dietary suggestion information, a calorie suggestion information, and/or an activity suggestion information corresponding to the user.

In an embodiment of step S206, referring to FIG. 13, the processing module 112 can obtain a suggestion table template W from the storage module 115 (step S206a), and then obtain the obtained physiological, body mass index parameters and health suggestion information N. (and such as results of physiological evaluation and body quality, at least one historical physiological parameter and historical body mass index parameter, calorie recommended intake, food distribution information, at least one recommended food for each food category, a photograph of each recommended food or The combination) is respectively nested to the corresponding position of the suggestion table template to generate a personalized health suggestion table P (step S206b). In an embodiment, as shown in FIG. 14 , the suggestion table template W includes a parameter suitable for presenting corresponding parameters, gender parameters, physiological parameters, height parameters, body weight parameters, body mass index parameters, health suggestion information N, A number of fields for items such as calorie recommended intake, food distribution information, recommended foods and their photos, physiological evaluation results, and body quality evaluation results Ic. Here, it is suggested that the item types and the number of the plurality of fields of the form template W can be determined according to the layout design of the personalized health suggestion table P.

In an embodiment, in step S206b, the processing module 112 receives the received personal parameters, physiological parameters, height parameters, weight parameters, and/or calculated body mass index parameters, calorie recommended intakes, and/or Or the food distribution information obtained, and/or the comparison of the physiological evaluation result or the body quality evaluation result are correspondingly inserted into the suggestion form template to generate the personalized health suggestion form P.

It should be noted that although the foregoing steps are described in order, the order is not limited by the present invention, and those skilled in the art should understand that the order of execution of the partial steps may be performed simultaneously or sequentially in a reasonable situation.

The processing module 112 can be implemented by one or more processing units, and each processing unit can be a microprocessor, a microcontroller, a digital signal processor, a microcomputer, a central processing unit, a field programming gate array, and a programmable logic device. , state, logic, analog circuits, digital circuits, and/or any device that operates on signals (analog and/or digits) based on operational instructions. The storage module 115 can be implemented by one or more storage units, and each storage unit can be non-permanent memory, permanent memory, static memory, dynamic memory, flash memory, and/or any information storing digital information. device. The network module 113 can be implemented in one or more network elements, and each network element can be a wired network chip, a wireless network chip, a Bluetooth chip, and/or any component suitable for connecting to a network. Each of the foregoing user interfaces may be a touch screen, a combination of a general display screen and a plurality of physical buttons, or a combination of a touch screen and one or more physical buttons. The aforementioned server can be composed of one to a plurality of computers.

In summary, the personalized health suggestion method of the embodiment of the present invention can automatically analyze the physiological parameters of the subject and further provide a personalized health suggestion table P of the corresponding health assessment.

Although the technical content of the present invention has been disclosed in the above preferred embodiments, it is not intended to limit the present invention, and any modifications and refinements made by those skilled in the art without departing from the spirit of the present invention are encompassed by the present invention. The scope of protection of the present invention is therefore defined by the scope of the appended claims.

10‧‧‧Personalized Health Management System

110‧‧‧Host

111‧‧‧User interface

112‧‧‧Processing module

113‧‧‧Network Module

114‧‧‧ Connection unit

115‧‧‧ storage module

116‧‧‧Printing module

120‧‧‧External measuring instrument

120'‧‧‧ Built-in measuring instrument

130‧‧‧Multimedia platform

140‧‧‧Server

T‧‧‧Health advice lookup table

T1‧‧‧ first analysis result

T2‧‧‧ second analysis result

T3‧‧‧ Suggested categories

M‧‧‧Health Recommendation Module

N‧‧‧Health advice information

F‧‧‧Food Suggestion Form

F1‧‧‧Food category

F2‧‧‧ Suggested number of copies

W‧‧‧ suggestion form template

P‧‧‧Personalized Health Recommendation Form

Is‧‧‧Specific items

Ic‧‧‧ physiological evaluation results and body quality results

S‧‧‧Recommended food list

E‧‧‧Reminder mark

S201~S209, S204a~S204d, S206a~S206b, S301, S302~S304, S401~S402, S401a~S401c‧‧‧ steps

FIG. 1 is a schematic diagram of a personalized health management system according to an embodiment of the present invention. 2 is a functional block diagram of one embodiment of the host of FIG. 1. FIG. 3 is a schematic diagram of a personalized health management system according to another embodiment of the present invention. 4 is a functional block diagram of one embodiment of the host of FIG. FIG. 5 is a flowchart of a personalized health suggestion method according to an embodiment of the present invention. 6 is a schematic diagram of one embodiment of a health advice lookup table. Figure 7 is a schematic illustration of one embodiment of a personalized health advice form. FIG. 8 is a flowchart of a personalized health suggestion method according to another embodiment of the present invention. FIG. 9 is a flowchart of a personalized health suggestion method according to still another embodiment of the present invention. Figure 10 is a schematic illustration of one embodiment of a food recommendation form. Figure 11 is a schematic illustration of one embodiment of a recommended food list. Figure 12 is a flow chart of an embodiment of step S204. Figure 13 is a flow chart of an embodiment of step S206. Figure 14 is a schematic illustration of one embodiment of a proposed table template.

Claims (14)

A personalized health suggesting method, comprising: receiving a plurality of physiological parameters, wherein the physiological parameters include a height parameter, a body weight parameter, and at least one other parameter; calculating a body mass index according to the height parameter and the body weight parameter (Body Mass) Index) parameter; analyzing the at least one other parameter according to at least one physiological condition to obtain at least one first analysis result; analyzing the body mass index parameter according to the integrated condition to obtain a second analysis result; according to the at least one first analysis result Searching the complex array health suggestion module with the second analysis result to obtain at least one health suggestion information; and generating a humanized health suggestion form according to the at least one health suggestion information, the personalized health suggestion form having the physiological parameters, the body The quality index parameter and the at least one health suggestion information. The personalized health suggestion method of claim 1, further comprising: transmitting the personalized health suggestion form to a multimedia platform; receiving a printed serial number returned by the multimedia platform, wherein the print serial number is associated with the personalized health Suggestion table; and output the print sequence number. The personalized health suggestion method of claim 1, wherein the at least one other parameter comprises a blood pressure parameter, an integrated fat rate parameter, and an age parameter, and searching for the first analysis result and the second analysis result according to the at least one first analysis result The step of re-arranging the health suggestion module to obtain the at least one health suggestion information comprises: finding the first analysis result corresponding to the blood pressure parameter and the first analysis of the body fat rate parameter from the complex array health suggestion module The result, the first analysis result of the age parameter, and the health assessment module of the second analysis result, wherein the set of health suggestion modules found has the at least one health suggestion information. The personalized health suggesting method according to claim 1, wherein the body type condition includes an integrated upper limit threshold and an integrated lower limit threshold, and the step of analyzing the body mass index parameter according to the body condition to obtain the second analysis result comprises: Comparing the body mass index parameter and the body shape condition; when the body mass index parameter is greater than the body size upper limit threshold, obtaining the second analysis result that the body mass index parameter belongs to a large body type; when the body mass index parameter is smaller than the body mass index parameter When the body lower limit threshold is obtained, the second analysis result is that the body mass index parameter belongs to a small body type; and when the body mass index parameter is between the body size upper limit threshold and the body size lower limit threshold, the second analysis result is obtained. The body mass index parameter belongs to a moderate body type. The personalized health suggestion method according to claim 4, further comprising: when the body mass index parameter belongs to the general type or the small body type, marking a reminder mark on the personalized health suggestion form. The personalized health suggesting method according to claim 1, wherein the at least one other parameter includes an age parameter, and the personalized health suggesting method further comprises: generating, according to the second analysis result, the weight parameter, and the age parameter Calorie recommended intake, where the personalized health advice form has this recommended calorie intake. The personalized health suggestion method according to claim 6, wherein the body condition includes an integrated upper limit threshold and an integrated lower limit threshold, the age parameter corresponding to one of at least one physiological condition, the physiological parameter corresponding to the physiological parameter includes An age threshold, and the step of generating the recommended amount of calorie intake comprises: multiplying the body weight parameter by 30 thousand when the body mass index parameter is less than the body size lower threshold or between the body size upper threshold and the body lower threshold Card to obtain the recommended amount of calorie intake; when the body mass index parameter is greater than the upper limit threshold of the body size and the age parameter is greater than the upper age threshold, multiply the weight parameter by 30 kcal to obtain the recommended calorie intake; and When the body mass index parameter is greater than the body size upper threshold and the age parameter is between the upper age threshold and the lower age threshold, the body weight parameter is multiplied by 30 kcal and then 500 kcal is deducted to obtain the calorie recommended intake. The personalized health suggestion method according to claim 6 or 7, further comprising: generating a food distribution information according to the calorie recommended intake amount, wherein the personalized health suggestion form further has the food distribution information. The personalized health suggestion method of claim 8, wherein the food distribution information includes a plurality of food categories and suggested number of copies corresponding to each of the food categories, and the personalized health suggestion method further comprises: according to each of the food categories The suggested number of copies of the at least one recommended food of the food category, wherein the personalized health advice form further has the at least one recommended food for each of the food categories. The personalized health suggestion method of claim 1, further comprising: receiving a login information, wherein the login information includes a user account; searching a historical record of the user account from a database to obtain the history record Storing at least one historical physiological parameter and a historical body mass index parameter at a time; comparing one of the historical physiological parameters and the corresponding one of the physiological parameters to obtain a physiological evaluation result; and comparing the historical body mass index parameter And the currently calculated body mass index parameter to obtain a body quality evaluation result, wherein the personalized health suggestion table has each of the physiological evaluation results and the body quality evaluation result. The personalized health suggestion method according to claim 1, wherein the at least one other parameter is a gender parameter, an age parameter, a blood pressure parameter, an integrated fat rate parameter, or a combination thereof. The personalized health suggesting method of claim 1, wherein the receiving the physiological parameters comprises: receiving the physiological parameters returned by at least one measuring instrument. The personalized health suggesting method of claim 1, wherein the receiving the physiological parameters comprises: receiving one of the physiological parameters returned by the at least one external measuring instrument; and receiving via a user interface Another part of these physiological parameters. The personalized health suggestion method of claim 1, wherein the step of generating the personalized health suggestion form according to the at least one health suggestion information comprises: obtaining a suggestion form template; and the physiological parameters, the body mass index parameter And the at least one health suggestion information is respectively inserted into a corresponding position of the suggestion form template to generate the personalized health suggestion form.