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KR-102963489-B1 - PERSONALIZED WEIGT MANAGEMENT

KR102963489B1KR 102963489 B1KR102963489 B1KR 102963489B1KR-102963489-B1

Abstract

The present invention relates to a device capable of managing an individual's weight and a personalized weight management method using the same, which can operate in an Internet of Things environment through a 5G communication network and, specifically, can effectively manage an individual's weight. The present invention learns an individual's expected weight calculated based on the individual's weight received on the current day, the type and amount of food consumed by the individual, and exercise information performed by the individual, and predicts information on food to be consumed and exercise information to be performed for the target weight received by the individual based on the learned individual's expected weight.

Inventors

  • 장서정

Assignees

  • 엘지전자 주식회사

Dates

Publication Date
20260513
Application Date
20191018

Claims (18)

  1. As a personalized weight management method, At least one processor receives an individual’s weight information, information on food consumed by the individual, and information on exercise performed on the current day; The above-mentioned at least one processor calculates the individual's expected weight for the next day by applying a pre-generated prediction model to the consumed food information and executed exercise information to predict weight gain or loss based on the consumed food information and executed exercise information; The above-mentioned at least one processor receives actual weight information for the next day; The above at least one processor calculates the difference between the expected weight for the next day and the actual weight for the next day; The above-mentioned at least one processor updates the prediction model, which learns the predicted weight according to weight increase/decrease information during a certain period based on the difference value between the predicted weight and the actual weight of the next day during a certain period and the previous difference value stored before receiving the actual weight information of the next day, into a personal prediction model; The above-mentioned at least one processor includes the step of comparing an expected weight predicted using the personal prediction model with a target weight received from the individual to generate exercise information including food information to be consumed by the individual, a type of exercise to be performed, and an exercise time to be performed according to the type of exercise. The step of receiving information on food consumed and exercise performed by the above individual is: The method includes the step of receiving the food intake time of the individual and the exercise execution time of the individual. The step of generating the above-mentioned food information to be consumed and exercise information to be performed is, A step of predicting the food information to be consumed based on the information of food and ingredients stored in the refrigerator used by the individual; and A step comprising generating a time for the individual to consume food and a time to execute exercise based on the received food intake time and the individual's exercise execution time. Personalized weight management methods.
  2. In paragraph 1, The above-mentioned information on consumed food includes at least one of the information on the type of food consumed or the amount of food consumed, and The exercise information performed above includes at least one of the type of exercise performed or the time of the exercise performed. Personalized weight management methods.
  3. In paragraph 2, The step of calculating the above individual's expected weight for the next day is, A step of calculating the calories obtained by the individual by applying average food calorie information to the type and amount of food consumed by the individual; A step of calculating the calories consumed by the individual by applying average exercise calorie information to the type of exercise and exercise time performed by the individual; and A method comprising the step of predicting the individual's weight for the following day based on the calories acquired by the individual and the calories consumed by the individual. Personalized weight management methods.
  4. ◈Claim 4 was waived upon payment of the establishment registration fee.◈ In paragraph 3, The step of updating to the above-mentioned personal prediction model is, It includes the step of generating the individual's food calorie table and the individual's exercise calorie table based on the difference value between the expected weight for the next day and the actual weight for the next day over a certain period, The above individual's food calorie table includes calorie information presumed to be obtained by the said individual according to the type and amount of food, and The above individual’s exercise calorie table includes calorie information estimated to be consumed by the individual according to the type and amount of exercise, Personalized weight management methods.
  5. ◈Claim 5 was waived upon payment of the establishment registration fee.◈ In paragraph 3, The step of generating the above-mentioned food information to be consumed and exercise information to be performed is, A step of receiving the type of exercise performed by an individual and the time of the exercise performed; A step of receiving the type of food consumed by an individual and the amount of food consumed; and A step comprising suggesting additional exercise to be performed or additional food to be consumed according to the personal prediction model to achieve the target weight based on the type of exercise performed and the time of the exercise performed, and the type and amount of food consumed. Personalized weight management methods.
  6. In paragraph 1, The above at least one processor further comprises the step of generating, within the above period, food information to be consumed and exercise information to be performed to achieve a target weight received from the individual based on the average calorie intake per food and the average calorie expenditure per exercise according to the individual's age and gender. Personalized weight management methods.
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  9. A computer-readable recording medium storing a computer program for executing any one of the methods of paragraphs 1 through 6 using a computer.
  10. As a personalized weight management device, A first data collector that receives an individual's weight information, information on food consumed by the individual, and information on exercise performed on the current day; A first data generator that calculates the individual's expected weight for the next day by applying a pre-generated prediction model to the information on food consumed and exercise performed to predict weight gain or loss based on the information on food consumed and exercise performed; A second data collector that receives actual weight information the next day; A second data generator that calculates the difference between the predicted weight for the next day and the actual weight for the next day; and A processor comprising: a predictor model that learns an expected weight based on weight increase/decrease information during a certain period, based on the difference between the expected weight and the actual weight of the next day during a certain period and a previous difference value stored prior to receiving the actual weight information of the next day, and a personal predictor model that compares the expected weight predicted using the personal predictor model with the target weight received from the individual, and generates exercise information including food information to be consumed by the individual, the type of exercise to be performed, and the exercise time to be performed according to the type of exercise. The above-mentioned first data collector is, Further receiving the above individual's food intake time and the above individual's exercise execution time, and The above processor is, Predicting the information on the food to be consumed based on the information on the food and ingredients stored in the refrigerator used by the aforementioned individual, and Based on the received food intake time of the individual and the exercise execution time of the individual, generating additional time for the individual to consume food and execute exercise, Personalized weight management device.
  11. In Paragraph 10, The above-mentioned information on consumed food includes at least one of the information on the type of food consumed or the amount of food consumed, and The exercise information performed above includes at least one of the type of exercise performed or the time of the exercise performed. Personalized weight management device.
  12. In Paragraph 11, The above processor is, Calculate the calories obtained by the said individual by applying average food calorie information to the type and amount of food consumed by the said individual, and Calculate the calories burned by the individual by applying average exercise calorie information to the type and duration of the exercise performed by the individual, and A further configured to predict the individual's weight for the following day based on the calories acquired and calories consumed by the individual. Personalized weight management device.
  13. ◈Claim 13 was waived upon payment of the establishment registration fee.◈ In Paragraph 12, The above processor is, It is further configured to generate the individual's food calorie table and the individual's exercise calorie table based on the difference value between the expected weight for the next day and the actual weight for the next day over a certain period, and The above individual's food calorie table includes calorie information estimated to be obtained by the individual based on the type and amount of food, and the above individual's exercise calorie table includes calorie information estimated to be consumed by the individual based on the type and amount of exercise. Personalized weight management device.
  14. ◈Claim 14 was waived upon payment of the establishment registration fee.◈ In Paragraph 12, The above processor is, A further configured to receive the type of exercise performed by an individual and the time of the exercise performed, and to receive the type of food consumed by an individual and the amount of food consumed, and to generate the food information to be consumed and the exercise information to be performed. Personalized weight management device.
  15. In Paragraph 10, The above processor is, Within the above specified period, additionally configured to generate food information to be consumed and exercise information to be performed to achieve a target weight received from the individual, based on the average calorie intake by food and the average calorie expenditure by exercise according to the individual's age and gender. Personalized weight management device.
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  18. As a personalized weight management device, One or more processors; and It includes memory connected to the above processor, and The above memory is, A command is stored to receive an individual’s weight information, information on food consumed by the individual, and information on exercise performed for the current day, and to apply a pre-generated prediction model to the information on food consumed and exercise performed to predict weight gain or loss based on the information on food consumed and exercise performed to calculate the individual’s expected weight for the next day, receive information on the actual weight for the next day, calculate the difference between the expected weight for the next day and the actual weight for the next day, update the prediction model as a personal prediction model that learns the expected weight based on the weight gain or loss information for a certain period, based on the difference value between the expected weight for the next day and the actual weight for the next day over a certain period and the previous difference value stored before receiving the information on the actual weight for the next day, and compare the expected weight predicted using the personal prediction model with the target weight received from the individual to generate exercise information including information on food to be consumed by the individual, the type of exercise to be performed, and the exercise time to be performed according to the type of exercise. The above memory is, Further receiving the individual’s food intake time and the individual’s exercise execution time, predicting the food information to be consumed based on information about food and ingredients stored in the refrigerator used by the individual, and further storing a command to generate the individual’s food intake time and exercise execution time based on the received individual’s food intake time and the individual’s exercise execution time. Personalized weight management device.

Description

Personalized Weight Management The present invention relates to a device capable of effectively managing an individual's weight and a personalized weight management method using the same. The contents described below are provided solely for the purpose of providing background information related to embodiments of the present invention, and the contents described do not automatically constitute prior art. Obesity refers to the excessive accumulation of body fat and occurs when an energy imbalance persists over a long period, in which energy intake exceeds energy expenditure. In particular, the prevalence of obesity has increased significantly over the past 30 years due to factors such as increased consumption of animal products, lack of exercise, and stress. Obesity, defined as a condition in which adipose tissue is excessively accumulated in the body, is emerging as a global problem to the extent that the World Health Organization warns it is a disease requiring long-term treatment. Moreover, obesity is not merely an aesthetic issue; it acts as a direct cause of metabolic syndrome, leading to various diseases such as cancer, heart disease, diabetes, hypertension, stroke, and osteoarthritis. In fact, it is reported that obese individuals have a 28% higher mortality rate than those in a normal state, and face a 2.9-fold higher risk of developing diabetes and a 5.6-fold higher risk of developing hypertension. As such, as the perception that obesity itself is a fatal disease spreads, interest and effort in weight management are increasing across society, and measures include self-management through diet and exercise, consumption of diet foods, and fat breakdown and liposuction procedures. Meanwhile, in the case of self-management that requires continuously measuring weight and identifying and recording changes in weight using a scale, there were problems such as it being inconvenient and cumbersome due to the busy lifestyles of modern people, and the inability to receive systematic counseling from weight management experts. Consequently, willpower and motivation declined, and in most cases, management was neglected, leading to failure. Furthermore, regarding the consumption of diet foods, there are countless varieties made from various medicinal plants, making it difficult to identify and consume foods suitable for one's constitution. Additionally, there was the problem of serious side effects occurring if unverified foods were consumed, and the administration of drugs and procedures were relatively expensive, resulting in a financial burden. Accordingly, there is a demand for technology that learns an individual's current weight, food intake, and exercise data to effectively manage their weight, and can predict weight fluctuations for the following day. In particular, there is a need for technology that analyzes how an individual's weight changes based on the type and amount of food consumed. In this regard, a technology for managing an individual's weight in relation to obesity is disclosed by prior art 1 and prior art 2. Specifically, Korean registered patent No. 10-1987620 (Prior Art 1), 'Method for predicting final weight loss using initial weight and weight loss and computer-readable storage medium,' discloses a technology that can analyze factors affecting weight loss in the intermediate stage and final weight loss of obesity treatment more scientifically and objectively by using result data of patients who have received specific obesity treatment, such as taking herbal medicine for obesity treatment. In particular, if factors influencing weight loss results are discovered, the relationship between these factors and weight loss is scientifically and objectively analyzed, and based on the analysis results, a reliable predicted value of weight loss in the next or final stage of obesity treatment is provided to the patient, thereby enabling effective obesity treatment by allowing the patient to set appropriate weight loss goals and treatment periods. However, the above-mentioned 'method for predicting final weight loss using initial weight and weight loss and computer-readable storage medium' merely describes a technology that enables patients to achieve appropriate weight loss by utilizing result data of patients receiving herbal medicine treatment; it does not disclose a technology that learns an individual's weight, information on food consumed, and information on exercise performed on the current day, and can predict weight gain or loss for the following day in advance. In addition, the ‘weight management method’ of Korean registered patent No. 10-1740516 (Prior Art 2) discloses a technology that obtains the energy consumed by an individual in a day and predicts a target weight achievable through the obtained consumed energy. However, the aforementioned prior art 2 does not describe a technology for analyzing how an individual's weight changes depending on the type and amount of food consumed by the ind