CN111524576B - Food weight estimation learning system for weight control - Google Patents
Food weight estimation learning system for weight control Download PDFInfo
- Publication number
- CN111524576B CN111524576B CN202010382045.5A CN202010382045A CN111524576B CN 111524576 B CN111524576 B CN 111524576B CN 202010382045 A CN202010382045 A CN 202010382045A CN 111524576 B CN111524576 B CN 111524576B
- Authority
- CN
- China
- Prior art keywords
- food
- weight
- module
- learner
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
-
- G—PHYSICS
- G09—EDUCATION; CRYPTOGRAPHY; DISPLAY; ADVERTISING; SEALS
- G09B—EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS
- G09B7/00—Electrically-operated teaching apparatus or devices working with questions and answers
- G09B7/02—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student
- G09B7/04—Electrically-operated teaching apparatus or devices working with questions and answers of the type wherein the student is expected to construct an answer to the question which is presented or wherein the machine gives an answer to the question presented by a student characterised by modifying the teaching programme in response to a wrong answer, e.g. repeating the question, supplying a further explanation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Epidemiology (AREA)
- Public Health (AREA)
- Nutrition Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Educational Technology (AREA)
- General Physics & Mathematics (AREA)
- Medical Treatment And Welfare Office Work (AREA)
Abstract
The invention relates to the field of learning systems, and discloses a food weight estimation learning system for weight control. The invention comprises the following steps: the storage module is used for receiving food pictures uploaded by an administrator and storing the food pictures in a classified mode according to the difficulty level; and the weight estimation learning module is used for acquiring food pictures from the storage module according to the current level of the user, issuing weight measurement test questions containing the food pictures to the learner, judging the test question answers after the weight estimation learning module receives the test question answers sent by the learner, and simultaneously giving answer analysis, wherein the answer analysis comprises weight estimation auxiliary information, nutrition information and eating advice. The invention is suitable for users who need to control the weight.
Description
Technical Field
The invention relates to the field of learning systems, in particular to a food weight estimation learning system for weight control.
Background
In recent years, the rapid development of social economy in China makes the physical living standard of people unprecedented, and changes of diet behavior and life style make the occurrence rate of overweight obesity appear in the burst of well-spraying. Despite the greatly increased weight management and demand, effective weight control remains a major difficulty in health management. Since 2010, the application of m-Health taking a smart phone, a wearable device and the like as carriers in weight control becomes a research hotspot, and a great deal of literature evidence shows that the modification of bad life behavior patterns can be promoted and the weight control efficiency can be improved by performing diet and exercise intervention through m-Health APP. However, at the same time, since the application of m-Health in the field of weight control is still in the development stage, there are some problems and challenges such as similar functions, lack of innovativeness, and no consideration of national or regional food culture differences, etc., so m-Health APP has considerable development space and prospect in the field of weight control.
The applicant has found that the current m-Health APP for weight control has difficulty in achieving control of food intake, and as one of the decisive factors influencing meal energy, controlling food intake as precisely as possible during weight loss is a necessary requirement to ensure weight loss effectiveness and scientificity. However, based on the current life situation of people, the method for weighing by adopting the food balance is neither feasible nor necessary, so that an effective way is found to improve the accuracy of estimating the weight of the food by people, and the method is the direction in which people can explore and try. In the nutritional community, there are many research bases aimed at improving the efficiency of estimating the weight of food, and the methods involved mainly include: diet investigation patterns, simulated food models, standardized tableware, measuring tools, timeliness image analysis and the like are commonly used in retrospective diet investigation, nutrition education and diet guidance of chronic patients or special groups (pregnant women and old people), and researches prove that the method can effectively improve the accuracy of food weight estimation. However, since the feasibility of such methods in people's daily lives is limited by means of standard auxiliary tools, the invention assumes that the auxiliary tools are digitized and virtualized, and m-Health APP or WeChat applet is used as a carrier, or the practicability of the method in people can be improved, and the method plays a role in the nutritional intervention fields such as weight control and the like.
Disclosure of Invention
The invention aims to solve the technical problems that: the food weight estimation learning system for weight control is provided, and nutrition and health beliefs and cognition of a user are established by transmitting and infusing nutrition related knowledge about food weight estimation, nutrition ingredients, food collocation and the like in a learning test process, so that the formation of healthy eating behaviors is promoted, and finally the purpose of weight control is achieved.
In order to achieve the above purpose, the invention adopts the following technical scheme: a food weight estimation learning system for weight control comprises a picture management module, a storage module, a weight estimation learning module and a user information management module;
the storage module is used for receiving food pictures uploaded by an administrator, wherein the food pictures comprise the following information: identifying a difficulty level, designating a food image with a weight specification, assisting in evaluating a reference object image, and classifying and storing the food image according to the identified difficulty level;
the image management module is used for performing manager operations on the food images in the storage module, wherein the manager operations comprise adding, deleting and replacing;
the weight estimation learning module is used for acquiring a certain number of food pictures from the storage module according to the current level of the learner, sequentially issuing weight measurement test questions containing the food pictures to the learner through the human-computer interaction interface, judging the test question answers after the weight estimation learning module receives the test question answers sent by the learner each time, and simultaneously giving answer analysis, wherein the answer analysis comprises nutrition information, eating advice and weight estimation auxiliary information based on reference object images in the food pictures; after the valuation learning module receives all test question answers sent by a learner, summarizing the judgment of the test question answers, giving answer scores, and updating the level grade of the learner according to the answer scores;
the user information management module is used for managing basic information and learning information of learners.
In particular, the references may include tableware and common living goods (e.g., a stylus or an identification card, etc.).
Furthermore, in order to avoid the conversion of the food raw-to-cooked ratio in daily life, the foods in the food picture are counted by the cooked weight except fruits and other raw-edible foods.
Specifically, the basic information of the learner may include a nickname, age, sex, weight, body fat rate, and body circumference.
Specifically, the learning information of the learner may include an answer record and a level.
Further, in order to better promote the formation of healthy diet and finally achieve the purpose of weight control, the system of the invention can further comprise a recording module, wherein the recording module is used for recording diet information and exercise information of the learner, and before the weight estimation learning module acquires the food pictures from the storage module, firstly, according to the diet information and exercise information recorded by the recording module, whether the learner has excessive food intake recently is analyzed, and if so, the weight estimation learning module acquires a part of food pictures containing the excessive food intake when the storage module acquires the food pictures. For example, a learner has more beef recently, but has less recent exercise, and the exercise is insufficient to consume so much beef, so that exercise and intake are not proportional, and the weight learning module will intentionally issue a weight measurement test question containing a beef picture to the learner, so that the learner has an excessive recent beef intake by answer analysis, and needs to control the beef intake. In addition, in order to enhance the aforementioned implication effect, the number of the pictures of the excessive intake of the food obtained by the weight learning module may be proportional to the excessive degree of the food, and taking beef excess as an example, if the beef excess is greater, the more beef pictures are obtained, the more weight test questions including the beef pictures are issued to the learner. In order to further enhance the aforementioned suggestion effect, the number of the large-weight-specification food pictures can be proportional to the excessive degree of the food, and in the case of beef, the more the beef is excessive, the more the large-weight-specification beef pictures are obtained, and the more weight test questions containing the large-weight-specification beef pictures are issued to the intended learner.
Further, the invention can also comprise an evaluation module for evaluating the diet record and the exercise record of the learner.
Further, in order to facilitate the checking and exporting of the learner information, the system of the present invention may further include an information record query module, where the information record query module is used for checking and exporting the learner information.
The beneficial effects of the invention are as follows: the invention refers to the national institute of overweight/obese medical nutrition therapist (2016 edition) and takes the cognition-behavior theory (CBT) as the psychological theoretical basis of the weight control system. CBT is recognized as a person's cognitive process is affected by emotion and behavior, and by changing cognition and behavioral techniques, people can recognize bad behaviors and correct them. In the system, an administrator learns and evaluates bad cognition and behaviors of a user in nutrition and weight control through communication with the user, pays attention to psychological change in the weight control process, corrects the bad cognition in a mode of taking implicit teaching as a main part and taking nutritional consultation as an auxiliary part, and guides the user to make positive behavior change. In the invention, the system for estimating and learning the weight of the food sets the nutrition education for estimating the weight of the food as explicit education, selects common food and the food consumption range thereof in the life of Chinese residents, takes the well-known articles in the daily life as visual reference objects, and provides the user with the food weight estimation and learning in the form of pictures to help establish the connection between the visual impression of the appearance of the common food and the corresponding weight thereof, thereby realizing the accurate estimation of the weight of the food through visual inspection. Meanwhile, nutrition and health knowledge such as food nutrition components, recommended intake, food collocation skills, health diet principles and the like are set as implicit education, and are nested in introduction and analysis of food weight estimation learning, so that potential and defaulting nutrition education and diet guidance are performed on a user in an unconscious state.
Drawings
FIG. 1 is a diagram of a weight control concept provided by the invention;
fig. 2 is a block diagram of a food weight estimation learning system for weight control according to an embodiment.
Detailed Description
The invention takes food weight estimation study as the core, and food nutrition related knowledge is used as assistance to provide nutrition education for users. By improving the ability to accurately estimate food weight and the level of knowledge related to nutrition, the important role of controlling food intake in weight control is recognized, facilitating self-management of food intake in a daily diet. Meanwhile, the learning effect of food weight estimation, the behavioral and psychological changes in the weight control process, the weight control effect and the like of the user are evaluated and fed back by monitoring diet, movement, weight change and the like of the user, and the overall design thinking is shown in figure 1.
Based on the above concept, the embodiment provides a food weight estimation learning system for weight control, which can be implemented by using the current m-Health APP or WeChat applet as a carrier, as shown in fig. 2, and specifically includes a picture management module, a storage module, a weight estimation learning module, a user information management module, a recording module, an evaluation module, and an information recording query module, and the functions of each module are described in detail below.
1. Memory module
The storage module is used for receiving food pictures uploaded by an administrator, wherein the food pictures comprise the following information: the method comprises the steps of identifying difficulty level, designating food images with weight specification, assisting in evaluating reference object images, and classifying and storing food images according to the difficulty level.
When an administrator shoots pictures, the administrator can place food in a dinner plate with the diameter of 18cm, place common living articles such as a common neutral pen or an identity card beside the dinner plate as a reference object, store picture information through shooting and upload the picture information into the storage module.
When the weight specification is selected, the weight of various foods can be determined by combining the common eating amount range in daily life based on the recommended intake of the Chinese resident balance diet guide (2016). As shown in Table 1, the weight of the food is calculated primarily on a cooked weight basis and the weight of the fruit or other raw edible food is calculated on a raw weight basis.
Table 1 part of common food weight settings
When the administrator sets the level of difficulty in recognition, the difficulty in estimating and learning the weight of the food can be classified into a primary level, a medium level and a high level according to the form, cooking mode, common degree and the like of the food, for example: chicken breast meat is primary, chicken legs (with bones) are medium-grade, and chicken blocks (with bones) are high-grade; the Chinese cabbage is primary and the purple cabbage is medium. The user starts learning from the beginning, and when the learning score exceeds 80 points (fully divided into 100 points), the user is regarded as having the capability of accurately estimating the weight of the food under the difficulty level, and can learn the next difficulty level, and the capability of visually estimating the weight of the food is gradually enhanced in the process.
2. Picture management module
The picture management module is used for performing manager operations on the food pictures in the storage module, wherein the manager operations comprise adding, deleting and replacing.
3. Weight learning module
The weight estimation learning module is a core functional module of the system, mainly presents picture resources issued by an administrator under a food weight estimation learning theme, and simultaneously sets analysis auxiliary users to improve the accuracy of estimating the weight of food, strengthen the ability of estimating the weight of the food through visual inspection, potentially transmit nutrition knowledge information and carry out nutrition education on the users from multiple angles.
When a learner performs the weight estimation learning, the weight estimation learning module can randomly acquire a certain number of food pictures from the storage module according to the current level of the learner, sequentially issue weight measurement test questions containing the food pictures to the learner through the human-computer interaction interface, judge the test question answers after the weight estimation learning module receives the test question answers sent by the learner each time, and simultaneously give answer analysis, wherein the answer analysis comprises nutrition information, eating advice and weight estimation auxiliary information based on reference object images in the food pictures, and taking 25g beef as an example, the analysis can be set as follows:
(1) Weight estimation auxiliary information: as shown in the food pictures, the thickness of the beef is about 0.3cm, and the size of the beef is equivalent to that of a Chinese resident identification card.
(2) Nutritional information and eating advice: the beef has high protein content and relatively low fat content, is rich in B vitamins, magnesium, iron and other microelements, is one of sources of high-quality proteins in daily life, and can be matched with vegetables such as Chinese cabbage, white radish and the like for eating. The weight of beef in the picture is about 25g, and 2-3 parts per meal and 2-4 times per week are recommended.
After the valuation learning module receives all test question answers sent by a learner, summarizing the judgment of the test question answers, giving answer scores, and updating the level grade of the user according to the answer scores.
4. User information management module
The user information management module is used for managing basic information of a learner and learning information, wherein the basic information of the learner comprises nicknames, ages, sexes, weights, body fat rates, body circumferences and the like; the learning information of the learner comprises answer records, level grades and the like.
5. Recording module
The recording module is used for recording diet information and exercise information of the learner. The athletic information may include padding items including (1) athletic type, (2) athletic time (minutes/hour); the heat consumed by the exercise is an option, because the system can calculate the heat consumed by the exercise according to the information of the necessary padding. The manager can monitor the physical activity of the user through the background management system and serve as one of the basis of nutrition guidance.
The embodiment can better promote the formation of healthy diet behaviors through the recording module and finally achieve the purpose of weight control, because before the weight learning module acquires the food pictures from the storage module, whether food intake is excessive recently can be analyzed according to diet information and motion information recorded by the recording module, and if so, the weight learning module acquires a part of food pictures containing the excessive intake when acquiring the food pictures from the storage module. For example, a learner has more beef recently, but has less recent exercise, and the exercise is insufficient to consume so much beef, so that exercise and intake are not proportional, and the weight learning module will intentionally issue a weight measurement test question containing a beef picture to the learner, so that the learner has an excessive recent beef intake by answer analysis, and needs to control the beef intake. In addition, in order to enhance the aforementioned implication effect, the number of the excessive intake food pictures obtained by the weight learning module may be proportional to the excessive intake degree of the food, and taking beef excessive as an example, if the beef excessive is greater, the more beef pictures are obtained, the more weight test questions including the beef pictures are issued to the learner. In order to further enhance the aforementioned suggestion effect, the number of the large-weight-specification food pictures can be proportional to the excessive degree of the food, and in the case of beef, the more the beef is excessive, the more the large-weight-specification beef pictures are obtained, and the more weight test questions containing the large-weight-specification beef pictures are issued to the intended learner.
6. Evaluation module
The evaluation module may be used to evaluate the learner's diet and exercise records.
7. Information record inquiry module
The information record query module can be used for viewing and exporting the learner information by an administrator.
The innovative analysis of the present invention is as follows:
at present, research and development of m-Health APP used in the field of weight control at home and abroad are well-developed, and the invention provides a food weight estimation learning system for weight control, which has a plurality of unique advantages compared with the existing weight reduction APP in the software market:
(1) the nutrition education of food intake is used as the core of weight control for the first time and is realized through a food weight estimation learning system, and the purpose of weight control is realized by guiding people to recognize daily food intake and self-correct bad eating behaviors. Research indicates that under the social environment that fast food culture is popular, takeaway service industry is mature and resident dining phenomenon is common nowadays, a large quantity of single meal, especially a large quantity of dinner, high energy density diet is closely related to overweight and obesity. Visual impact from a large volume of food can affect the judgment of the eater, and overeating the appropriate intake can result in excessive eating. In the nutrition intervention in the current weight control field, there is a method for education and guidance of food intake, and in view of the important role of food intake in total energy control, the invention takes the food intake as an entry point, and the nutrition education for food intake estimation is realized through a food weight estimation learning system, and meanwhile, related nutrition knowledge such as food nutrition ingredients, diet collocation, recommended intake and the like is infused, so that the accuracy of estimating daily food intake of a user is improved, and meanwhile, the behavior of excessive diet is corrected, the user is guided to self-adjust the intake proportion of various foods, and correct and reasonable diet habit is cultivated, thereby achieving the purpose of effectively controlling weight. The novel concept is provided for weight control, and the function of the m-Health APP in weight control is expanded.
(2) The nutrition education adopts a mode of combining explicit education and implicit education. Research has shown that implicit education helps to stimulate the mobility of the educated person and helps to create a more permanent, profound effect. Therefore, the research does not adopt a 'teaching' nutrition education method in the design stage, and the concept of nutrition knowledge learning is reduced by nesting nutrition knowledge in the food intake estimation learning/education process, so that the user is subjected to the acquainted influence and guidance.
(3) The metering mode of the food intake is based on the common food maturation state in daily diet, so that the process of converting the maturation weight of most of foods in daily diet is avoided, and the method is convenient and practical. In the field of nutrition research, in order to accurately control food calories, the recommended intake amount of related food or the weight of food is usually selected as a metering mode when a recipe is made, but from the aspects of nutrition science popularization and practice, the weight of food cannot reflect the corresponding cooked food amount, is disjointed from the daily eating habit of people (especially young people with the age of 18-44 years and frequent choice of external meals), and lacks popularity, intuitiveness and operability for the audience. Therefore, the invention simplifies the operation procedure of food metering, refers to the food maturity ratio calculated by the scholars such as Fang Yuewei, completes the conversion of the weight of food maturity and the corresponding heat in the design stage, and guides the diet of the user by the weight of food maturity. Through the nutrition education of the food weight estimation learning system, a user can directly estimate the food intake on the dining table more accurately and is applied to self-control and management of the food intake. For people who have dinner outside more and more currently, the food weight estimation learning system can help to accurately control the food intake under the condition of no weighing, closely fit the food habits of people in the modern society, and greatly enhance the practicability in daily life.
Claims (9)
1. The food weight estimation learning system for weight control is characterized by comprising a picture management module, a storage module, a weight estimation learning module, a recording module and a user information management module;
the storage module is used for receiving food pictures uploaded by an administrator, wherein the food pictures comprise the following information: identifying a difficulty level, designating a food image with a weight specification, assisting in evaluating a reference object image, and classifying and storing the food image according to the identified difficulty level;
the image management module is used for performing manager operation on the food images in the storage module;
the weight estimation learning module is used for acquiring a certain number of food pictures from the storage module according to the current level of the learner, sequentially issuing weight measurement test questions containing the food pictures to the learner through the human-computer interaction interface, judging the test question answers after the weight estimation learning module receives the test question answers sent by the learner each time, and simultaneously giving answer analysis, wherein the answer analysis comprises nutrition information, eating advice and weight estimation auxiliary information based on reference object images in the food pictures; after the valuation learning module receives all test question answers sent by a learner, summarizing the judgment of the test question answers, giving answer scores, and updating the level grade of the learner according to the answer scores;
the user information management module is used for managing basic information and learning information of a learner;
the recording module is used for recording diet information and movement information of a learner, the weight estimation learning module firstly analyzes whether the learner has excessive food intake recently according to the diet information and movement information recorded by the recording module before the weight estimation learning module obtains the food picture from the storage module, and if so, the weight estimation learning module obtains a part of food picture containing the excessive food intake when obtaining the food picture from the storage module.
2. A food weight estimation learning system for weight control as claimed in claim 1, wherein the reference objects include tableware and common living goods.
3. The food weight estimation learning system for weight control of claim 1 wherein the food in the food picture, excluding fruits and other raw foods, is based on the weight of the meal.
4. A food weight estimation learning system for weight control as claimed in claim 1, wherein the basic information of the learner includes nickname, age, sex, weight, body fat rate and body circumference.
5. A food weight estimation learning system for weight control as claimed in claim 1, wherein the learner's learning information includes answer notes and level.
6. The food weight estimation learning system for weight control of claim 1, wherein the number of food pictures taken by the weight estimation learning module is proportional to the degree of food overdose.
7. The food weight estimation learning system for weight control of claim 6, wherein the number of food pictures of a large weight specification in taking excessive food pictures is proportional to the degree of excessive food.
8. The food weight estimation learning system for weight control of claim 1 further comprising an evaluation module for evaluating a learner's diet and exercise records.
9. The food weight estimation learning system for weight control of claim 1, further comprising an information record query module for review and export of learner information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010382045.5A CN111524576B (en) | 2020-05-08 | 2020-05-08 | Food weight estimation learning system for weight control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010382045.5A CN111524576B (en) | 2020-05-08 | 2020-05-08 | Food weight estimation learning system for weight control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111524576A CN111524576A (en) | 2020-08-11 |
| CN111524576B true CN111524576B (en) | 2023-07-07 |
Family
ID=71905165
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010382045.5A Active CN111524576B (en) | 2020-05-08 | 2020-05-08 | Food weight estimation learning system for weight control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111524576B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220399098A1 (en) * | 2020-12-25 | 2022-12-15 | Boe Technology Group Co., Ltd. | Diet recommendation method, device, storage medium and electronic device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106326683A (en) * | 2016-09-27 | 2017-01-11 | 成都脉安科健生物科技有限公司 | Food nutrition ingredient quantifying and tracing method and device |
| CN107153972A (en) * | 2017-05-10 | 2017-09-12 | 王鹏飞 | A kind of menu visualization point ordering system of user oriented evaluation of nutrition |
| CN107563115A (en) * | 2017-08-22 | 2018-01-09 | 北京清视野科技有限责任公司 | Attention bias image trainer and method |
| CN109841270A (en) * | 2019-02-02 | 2019-06-04 | 成都尚医信息科技有限公司 | Dietary nutrition health investigation and assessment system and its implementation based on smart machine |
| EP3582226A1 (en) * | 2018-06-15 | 2019-12-18 | Koninklijke Philips N.V. | An apparatus and method for providing context-based intervention |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060015016A1 (en) * | 2004-06-22 | 2006-01-19 | Thornton William E | Caloric balance weight control system and methods of making and using same |
| JP2006185317A (en) * | 2004-12-28 | 2006-07-13 | Ricoh Co Ltd | Personnel training support system and personnel training support method |
| EP1964011A2 (en) * | 2005-12-15 | 2008-09-03 | Koninklijke Philips Electronics N.V. | Modifying a person's eating and activity habits |
| AU2013323790A1 (en) * | 2012-09-25 | 2015-03-26 | Theranos Ip Company, Llc | Systems and methods for response calibration |
| CN107958696A (en) * | 2017-11-15 | 2018-04-24 | 四川大学 | One kind is used to mark the special food chart system of students in middle and primary schools' meals and mask method |
| GB2574435A (en) * | 2018-06-06 | 2019-12-11 | Guud Ltd | Personalised nutritional information system |
| CN109637666A (en) * | 2018-12-13 | 2019-04-16 | 重庆大学 | A kind of Weight management system |
| CN110021208A (en) * | 2019-05-16 | 2019-07-16 | 上海乂学教育科技有限公司 | For the efficient training method and system of English Reading topic |
| CN110634543A (en) * | 2019-08-16 | 2019-12-31 | 中食安泓(广东)健康产业有限公司 | Fat-reducing management platform and implementation method thereof |
-
2020
- 2020-05-08 CN CN202010382045.5A patent/CN111524576B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106326683A (en) * | 2016-09-27 | 2017-01-11 | 成都脉安科健生物科技有限公司 | Food nutrition ingredient quantifying and tracing method and device |
| CN107153972A (en) * | 2017-05-10 | 2017-09-12 | 王鹏飞 | A kind of menu visualization point ordering system of user oriented evaluation of nutrition |
| CN107563115A (en) * | 2017-08-22 | 2018-01-09 | 北京清视野科技有限责任公司 | Attention bias image trainer and method |
| EP3582226A1 (en) * | 2018-06-15 | 2019-12-18 | Koninklijke Philips N.V. | An apparatus and method for providing context-based intervention |
| CN109841270A (en) * | 2019-02-02 | 2019-06-04 | 成都尚医信息科技有限公司 | Dietary nutrition health investigation and assessment system and its implementation based on smart machine |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111524576A (en) | 2020-08-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Shifflett et al. | Understanding of athletes' nutritional needs among athletes, coaches, and athletic trainers | |
| CN107731278A (en) | A kind of food recognition methods, nutrient health analysis method, system and device | |
| Hawks et al. | Intuitive eating and the nutrition transition in Asia | |
| CN105260594A (en) | Individual nutrition evaluation system | |
| CN111161838B (en) | Reasonable diet method and guidance system suitable for students | |
| CN111261260B (en) | Diet recommendation system | |
| CN108288495A (en) | A kind of dietary management system and method | |
| Malpede et al. | Racial influences associated with weight-related beliefs in African American and Caucasian women | |
| CN111524576B (en) | Food weight estimation learning system for weight control | |
| CN115910284A (en) | Dining recommendation method and device, electronic equipment and storage medium | |
| CN110634543A (en) | Fat-reducing management platform and implementation method thereof | |
| CN106339574A (en) | Refrigerator food-based personalized catering method and system as well as refrigerator | |
| KR102033240B1 (en) | A System Providing AI Secretary Service for Improvement of Eating Habit based on MR through Customization Learning of Virtual Character | |
| CN114388101A (en) | Health recipe management system and method thereof | |
| CN111652044A (en) | Dietary nutrition analysis method based on convolutional neural network target detection | |
| CN108766535B (en) | Intelligent personal health management system | |
| CN116312964A (en) | Human nutrition evaluation management method and device for whole population | |
| Nakamoto et al. | Prediction of mental state from food images | |
| CN115410675A (en) | Student movement and health data management system | |
| CN114780837A (en) | Intelligent personalized life style recommendation system | |
| Minkow et al. | Cooking frequency predicts fruit and vegetable intake among college students | |
| Sommer | An investigation of food choice behaviour and dietary intake of children, teenagers and adults with food allergies | |
| Xiao et al. | Using photovoice to understand perceptions of obesity among African-American women | |
| CN117594194A (en) | Method for managing nutrition and health of catering | |
| Wali | A pilot program to evaluate the effect of training table-based nutrition education and menu modification on the nutrition knowledge and dietary intake of collegiate football players at a NCAA division I university |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |