KR20090000942A - Fitting exercise for each users recommendation service - Google Patents
Fitting exercise for each users recommendation service Download PDFInfo
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- KR20090000942A KR20090000942A KR1020070064899A KR20070064899A KR20090000942A KR 20090000942 A KR20090000942 A KR 20090000942A KR 1020070064899 A KR1020070064899 A KR 1020070064899A KR 20070064899 A KR20070064899 A KR 20070064899A KR 20090000942 A KR20090000942 A KR 20090000942A
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- 230000003862 health status Effects 0.000 claims abstract description 10
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 10
- 201000010099 disease Diseases 0.000 claims description 9
- 230000000386 athletic effect Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 abstract description 7
- 230000036772 blood pressure Effects 0.000 abstract description 5
- 235000005911 diet Nutrition 0.000 description 11
- 230000037213 diet Effects 0.000 description 11
- 238000000034 method Methods 0.000 description 10
- 235000013305 food Nutrition 0.000 description 6
- 235000012046 side dish Nutrition 0.000 description 4
- 239000008280 blood Substances 0.000 description 3
- 210000004369 blood Anatomy 0.000 description 3
- 238000000605 extraction Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000037323 metabolic rate Effects 0.000 description 3
- 206010020772 Hypertension Diseases 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 208000001871 Tachycardia Diseases 0.000 description 1
- 210000000577 adipose tissue Anatomy 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 206010012601 diabetes mellitus Diseases 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 235000020803 food preference Nutrition 0.000 description 1
- 230000002503 metabolic effect Effects 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 238000013077 scoring method Methods 0.000 description 1
- 235000014347 soups Nutrition 0.000 description 1
- 230000006794 tachycardia Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Social work or social welfare, e.g. community support activities or counselling services
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- 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
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
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- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Marketing (AREA)
- Human Resources & Organizations (AREA)
- Biophysics (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
- Medical Informatics (AREA)
- Child & Adolescent Psychology (AREA)
- Physical Education & Sports Medicine (AREA)
- Public Health (AREA)
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- General Business, Economics & Management (AREA)
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- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
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Abstract
The system of the present invention encourages a user to exercise, and recommends a specific sport in consideration of the user's health condition, exercise preference, general activity amount, and the like. In other words, the user's health status is recommended through the biometric index measuring devices such as blood pressure monitors and pulse rate monitors, and the exercise amount and preference through the existing patient health status database. Reflect. Through this, a user customized exercise recommendation service that combines the user's health status, exercise performance history, and preferences is possible.
Description
La exercise recommendation algorithm according to the present invention
1b illustrates an exercise recommendation algorithm according to the present invention
Prescribing exercise through professional health care professionals, health care professionals, or programs has been around for a long time, and several methods are currently in use.
First, prescribing general exercise constraints through specialists such as medical professionals
Second, a method of prescribing a specific exercise set in a database
Third, the method recommended in the preset list after measuring the user's exercise ability through the device
First, exercise prescriptions through general exercise constraints prescribed by a medical professional prescribe exercise constraints to be avoided by a doctor's judgment of a patient's health condition. This is a passive exercise prescription method that allows the user to choose basic exercise activities by providing constraints related to the disease, rather than prescribing based on the exercise ability of each patient. This has the disadvantage of not eliciting the active athletic activity of the user.
The method of prescribing a specific exercise set in a database is a method of recommending to a user by making a list of exercise lists recommended by existing medical staff or exercise specialists. This does not take into account the diversity of the user, and in general, by giving a prescription to exercise the user, there is a limit that can not provide a customized service.
Finally, the recommended method in the preset list after measuring the user's exercise ability through the device, after evaluating the user's exercise ability through the existing exercise capacity measuring machine to select the appropriate exercise sequence from the database exercise list Recommend and inform the user. It is a system to which the user's exercise ability is applied, and it is possible to provide customized exercise recommendation service than the existing system, but it does not consider the user's short-term exercise ability status, performance level, or preference for various exercises, so that the user can There is a lack of active recommendations.
[Table 1] Comparison of existing exercise recommendation method and invented recommendation method
Therefore, the present invention is to solve the disadvantages and problems of the prior art as described above, and is a user customized exercise recommendation service that combines the user's health status, exercise performance history and preferences.
The present invention is a system that encourages a user to exercise, and recommends a specific sport in consideration of the user's health condition, exercise preference, general activity amount, and the like. In other words, the user's health status is best determined through biometric index measurement devices such as blood pressure monitors and pulse rate monitors, health status measurement through the existing patient health status database, real-time activity amount through activity meter, and exercise amount and preference through user's exercise performance history. Recommend an appropriate exercise, evaluate the performance, and reflect the content in future recommendations. Through this, a user customized exercise recommendation service that combines the user's health status, exercise performance history, and preferences is possible.
The algorithm configuration of the present invention is composed of three parts as follows.
① Necessary momentum extraction algorithm
② recommended exercise scoring algorithm
③ Algorithm setting preference by inputting user exercise history
La illustrates the exercise recommendation algorithm according to the present invention.
● required momentum extraction algorithm
The user health index measurement algorithm is an algorithm that collects the user's restrictions and extracts the amount of exercise that the user should spend on exercise. To do this, the disease management state, which can know the user's existing health state, is identified through the stored data, and this is included in the constraint. At this time, the user's disease state is stored in the DB name and the extent. This information can be updated by the user via the web.
Blood pressure, pulse rate, blood sugar, weight, and body fat can be acquired from the sensor in order to understand the current state of health of the user. This information can be input via wired / wireless through heterogeneous sensors or directly through the user interface. The input blood pressure, pulse rate, blood sugar, etc. are converted into the index of hypertension, tachycardia and diabetes diseases and considered as user restrictions.
In addition, BMR (Basal Metabolic Rate) is extracted in consideration of the user's weight and the previously input gender and age. BMR is converted by the following formula.
Formula by Harris and Benedict:
● man h = 66.4730 + (13.7516ω) + (5.0033.s)-(6.7550α)
● women h = 655.0955 + (9.5634 · ω) + (1.8496 · s)-(4.6756 · α)
h = calorie output (for 24 hours, KCal), w = weight (kg), s = height (cm), a = age
In addition, the amount of exercise required decreases depending on the diet.
Required amount of exercise = energy intake-basic metabolic rate-amount of activity + amount of exercise required for diet
At this time, the amount of exercise required for diet,
The amount of exercise required for the diet = (current weight-target weight) * 7000 / diet days. For this, a weight of 1 kg = 7000 KCal was used.
● Recommended exercise scoring algorithm
The recommended exercise scoring algorithm classifies food into 6 categories, such as rice, soup, side dish 1, side dish 2, side dish 3, side dish 4, and generates a diet list through the combination of recommended foods from each category. Score the resulting diet. At this time, the following formula is used as a scoring method.
S Meal = (1-sqrt ([(Exercise-Recommend) / Required] 2 )) * 100 * IF Preference
S Mea : Score of Recommended Food
IF Preference : The food's preference Impact Factor (0 ～ 1)
After sorting by the exercise of the highest score among the scored exercise, the exercise that does not match with the health constraints is extracted and recommended to the user.
At this time, the constraints are recorded in the exercise database and found through comparison with the user's personal health information.
● Preference setting algorithm by inputting user's exercise history
The exercise information recommended to the user is updated by the user's preference through the selection. If the user chooses to perform the recommended exercise, the user's preference for each exercise is increased to reflect the contents, and if the user selects the non-preferred exercise, the user lowers the preference to give a lower score in the subsequent exercise scoring. .
In case of a preferred exercise, + δ is used. When a non-preferred exercise is selected, -δ is used to update the preference. Preference is an index between 0 and 1, and the user's preference has the greatest influence in calculating the recommended food score.
1. Collect User Restrictions
-The exercise recommendation agent receives the necessary exercise restrictions in consideration of the current state of health using the user's disease database and biometric information, diet setting, and user basic metabolic rate.
2. Disease Management Situation
-The user's disease database is used to receive information about the disease currently possessed by the user, and the agent receives the type of exercise or intensity of exercise to be avoided.
3, user Vital Sign
-Current basic bio signals such as blood pressure and blood sugar of the user are used to find out whether the disease is being managed well and to limit the intensity and type of exercise according to the contents.
4. Diet and Content
If the user is on a diet, tell the agent to lower the total calories needed accordingly. At this time, the agent adjusts the amount of calories burned.
5. BMR calculation using user information
-Calculate BMR (Basal Metabolic Rate) using your age, gender, weight and height. The BMR calculated using the Harris-Benedict Equation is used to derive the calories needed by the user. The Harris-Benedict Equation is shown below.
-Man: 66.4730 + 13.7516 * w + 5.0033 * s-6.7550 * a
Woman: 655.0955 + 9.5634 * w + 1.8496 * s-4.6756 * a
w = weight, s = stature, a = age
6. Need momentum extraction
-Prescribe the amount of exercise required by the user using the necessary nutrients and restrictions from items 2, 3, 4 and 5.
7. Preferred Exercise
-Unlike food, exercise requires a lot of user preference. In addition, it is recommended to increase the preference of recent exercise so that the same type of exercise can be performed steadily.
8. Exercise Recommendations
-Recommend the user can choose the exercise with the amount and intensity according to the calculated exercise needs and the user's exercise preferences.
9. Whether to choose a recommended exercise
-The user selects the desired type of exercise from the agent and informs the agent. If none of the recommended workouts are desired, the agent is notified and the agent recommends another workout. At this time, the user's preferred exercise history is updated.
10. Confirm Recommended Diet
-The user confirms the exercise history selected by the user, and the prescribed exercise history is transmitted to the user through the user's mobile phone.
11. Preferred / Unpreferred Food Selection
-Update the user's exercise preferences in the database, and use it to recommend the user's high preference exercise in the future prescription.
12. Enter your workout history
-Enter the user's exercise history. At this time, the recommended exercise history can be selected in advance, and when the exercise of other contents is performed, the contents can be corrected. Through this, the agent manages the user's workout preferences.
13. Update your favorite workout list
-Update the preferred exercise list based on the result of the preference analysis extracted by the agent from the exercise preference / unfavorite list and the exercise history manually input by the user.
Figure 1b shows an exercise recommendation algorithm according to the present invention.
Accordingly, the present invention is a user customized exercise recommendation service that combines the user's health status, exercise performance history, and preferences, and can prevent accidents due to the user's exercise activity enhancement and the existing wrong exercise method.
Claims (4)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020070064899A KR20090000942A (en) | 2007-06-29 | 2007-06-29 | Fitting exercise for each users recommendation service |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020070064899A KR20090000942A (en) | 2007-06-29 | 2007-06-29 | Fitting exercise for each users recommendation service |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| KR20090000942A true KR20090000942A (en) | 2009-01-08 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| KR1020070064899A KR20090000942A (en) | 2007-06-29 | 2007-06-29 | Fitting exercise for each users recommendation service |
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| KR (1) | KR20090000942A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015064837A1 (en) * | 2013-10-30 | 2015-05-07 | 박종준 | Method for analysing sports-compatibility |
| KR102115153B1 (en) | 2019-11-19 | 2020-05-26 | 황상필 | Slide Locking Device with Multi Handles for Windows |
| WO2023146348A1 (en) * | 2022-01-28 | 2023-08-03 | 삼성전자 주식회사 | Electronic device for recommending exercise route, and control method therefor |
-
2007
- 2007-06-29 KR KR1020070064899A patent/KR20090000942A/en not_active Application Discontinuation
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015064837A1 (en) * | 2013-10-30 | 2015-05-07 | 박종준 | Method for analysing sports-compatibility |
| KR101532644B1 (en) * | 2013-10-30 | 2015-06-30 | 박종준 | Method for analysys of sport suitability |
| KR102115153B1 (en) | 2019-11-19 | 2020-05-26 | 황상필 | Slide Locking Device with Multi Handles for Windows |
| WO2023146348A1 (en) * | 2022-01-28 | 2023-08-03 | 삼성전자 주식회사 | Electronic device for recommending exercise route, and control method therefor |
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