WO2020235028A1 - Système de gestion d'informations de santé, dispositif de terminal mobile, procédé de gestion d'informations de santé et programme de gestion d'informations de santé pour dispositif de terminal mobile - Google Patents

Système de gestion d'informations de santé, dispositif de terminal mobile, procédé de gestion d'informations de santé et programme de gestion d'informations de santé pour dispositif de terminal mobile Download PDF

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WO2020235028A1
WO2020235028A1 PCT/JP2019/020197 JP2019020197W WO2020235028A1 WO 2020235028 A1 WO2020235028 A1 WO 2020235028A1 JP 2019020197 W JP2019020197 W JP 2019020197W WO 2020235028 A1 WO2020235028 A1 WO 2020235028A1
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amount
data
meal
mobile terminal
user
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PCT/JP2019/020197
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English (en)
Japanese (ja)
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圭二 大久保
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Okubo Keiji
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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
    • G16H20/60—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

Definitions

  • the present invention is the world's first "My Health Anatomy (MHA) My Anatomy” application that makes it possible for users to understand what is the strongest KPI.
  • KPIs Key Performance Indicators
  • wearables including devices, chips, etc.
  • smartphones and KPIs are displayed on smartphones and the like. It is not a medical device, but is intended to support self-determination.
  • the obese population has already exceeded 2.1 billion, accounting for more than one in three human beings, and because it is expanding rapidly, it is called the Global Obesity War. War destroys everything. The same is true for obesity. It is widely known that obesity is associated with many diseases such as hypertension, diabetes, aspiration syndrome, kidney disease, myocardial infarction, cerebral infarction, cancer, dementia, psychiatric disorder, orthopedic disorder, dialysis, and blindness, and is accompanied by enormous sacrifice. Has been done. Obesity progresses chronically. Earth history states that there are no drugs with dramatic effects and that the efforts of obese people are often unrewarded. And it is accelerating not only individual financial collapse but also social collapse. The above diseases are the main efforts of WHO and the Japan Society for the Study of Obesity.
  • References 5 Nutter, RL, Gridley, DS, Kettering, JD, et al., “Modification of a transplantable colon tumor and immune responses in mice fed different sources of protein, fat and carbohydrate.” Cancer Letters, 18 (1) , 1983, pages 49-62.
  • Reference paper 6 Sensi, M., Pricci, F., Andreani, D., et al., “Advanced Nonenzymatic Glycation Endproducts (AGE): Their Relevance to Aging and the Pathogenesis of Late Diabetic Complications.” Diabetes Research, 16 ( 1), 1991, pages 1-9.
  • Reference Article 7 Gererts, SO, Leoffice, V., Charpentier, J., et al.
  • insulin resistance in skeletal muscle plays a major role in the development of type 2 diabetes and may be causally related to an increase in intramuscular fatty acid metabolites.
  • FATP1 deletion protected KO mice from fat-induced insulin resistance and intramuscular accumulation of fatty acyl-CoA without altering systemic hyperlipidemia.
  • Fatty acid transport protein 1 (FATP1) is an acyl-CoA synthetase highly expressed in skeletal muscle and modulates fatty acid uptake and metabolism by converting fatty acids into fatty acyl-CoA.
  • HbA 1c Comparing one or two people with dyslipidemia at risk of high cardiovascular disease (CVD), HbA 1c was 7.3, 7.5, and 7.9%, triglycerides 150, 210, and 381 mg / dl, and HDL 54, 40 and 37 mg 1 / dl suggest an association between poor glycemic control and dyslipidemia in type 2 diabetes, respectively.
  • Pladevall etc. (Summary 948) A managed care database of 9,642 people, with 6,751 people followed between 1997 and 2001, was used to describe trends in the management of dyslipidemia in diabetic patients. Lipid tests were performed at 37, 44, 51, 55, and 6% in 1997, 1998, 1999, 2000, and 2001, respectively.
  • NCEP National Cholesterol Education Program
  • ATP III Adult Treatment Panel III
  • a low-saturated fat diet combined with exercise and 2) Dietary supplements, namely fish oil, oats, or plant sterol supplementation and treatment of exercise dyslipidemia.
  • Combination therapy is particularly advantageous because diet and exercise elicit complementary effects on lipid profiles. More specifically, with a few exceptions, diets have low total (TC) and LDL cholesterol (LDL-C) levels, whereas exercise interventions have HDL cholesterol (HDL-C). While increasing, it decreases triglyceride (TG) levels.
  • TC total
  • LDL-C LDL cholesterol
  • TG triglyceride
  • Measurements of outcomes before and after treatment included glucose treatment with a hyperinsulin normoglycemic clamp and computed tomography scans of abdominal AT and central femoral skeletal muscle. Results-Glucose infusion rate was significantly increased in the Ae + RT group (P ⁇ 0.05). Both exercise groups reduced subcutaneous and visceral AT in the abdomen and increased muscle density. The Ae + RT training group showed a significantly greater increase in muscle density than the Ae-only group. Improved glucose treatment was independently associated with changes in subcutaneous AT, visceral AT, and muscle density. After controlling abdominal AT, muscle density remained associated with glucose processing. In conclusion, the addition of resistance training to aerobic training enhanced glucose treatment in postmenopausal women with type 2 diabetes.
  • MetS hyperglycemia
  • MetS and its main components can be powerful predictors of long-term ischemic stroke. Therefore, it is necessary to focus on the identification and proper management of MetS components to prevent the occurrence of stroke.
  • Metabolic syndrome and all causes and mortality from cardiovascular disease A prospective (JPHC) study based on the Public Health Center. Between 1990 and 2005, a Japanese health center-based prospective (JPHC) study found metabolic risk in 12,412 and 21,639 men aged 40-69 years with no history of cardiovascular disease (CVD) or cancer. Baseline measurements of the factors were performed. To clarify the role of obesity adopted by the definition of MetS in Japan, we investigated the clustering of risk factors in data grouped according to overweight status. During the 12.3 year follow-up, there were 2,040 deaths, of which 947 died of cancer and 304 died of CVD. MetS significantly increased the hazard ratios for all-cause mortality in women and CVD mortality in men. Non-overweight with two or more risk factors had similar effects on all-cause mortality and CVD mortality. Clustering of metabolic factors caused a linear increase in risk relative to mortality. CONCLUSIONS: MetS moderately increased all-cause mortality and CVD mortality.
  • Metabolic syndrome is defined as at least three clusters of five clinical risk factors: abdominal (visceral) obesity, hypertension, elevated serum triglycerides, low serum high density lipoprotein (HDL) and insulin resistance. It is estimated that more than 20% of the world's adult population is affected. Abdominal (visceral) obesity is considered to be a major risk factor for metabolic syndrome, and it is estimated that by 2030, 50% of adults will be classified as obese. According to health and economic evidence, regular and consistent exercise reduces abdominal obesity and results in favorable changes in body composition.
  • FIG. 1 is a schematic diagram showing the entire system to which the MHA application according to an embodiment of the present invention is applied. Further, FIG. 2 is a diagram showing an image of a screen that the user can actually see. This is just an example, and can be changed by the user and customization at the time of manufacture and sale.
  • This system is composed of a device A worn by the user, an information communication terminal B such as a smartphone carried by the user, and a cloud server C.
  • the device A mainly collects exercise data and transmits it to the information communication terminal B.
  • the information / communication terminal B mainly photographs foods to be ingested, uploads images together with exercise data to cloud server C, downloads PFC data from the cloud server C, and displays various information.
  • the cloud server C uses the uploaded image, exercise data, etc., analyzes what the user has ingested by the AI engine, generates PFC data based on the analysis result, and downloads it to the information communication terminal B.
  • the user can obtain the PFC of the meal or beverage simply by automatically reading (sometimes taking a picture) the identification code such as the voice, photo, and bar code of the meal and uploading it. As a result, it becomes easy to recognize which factor of the PFC factor should be lowered or increased. The user can adjust the main factors of the diet by himself.
  • PFC is used as basic data to improve the eating habits of users, but exercise and sleep collected by devices such as smart watches shown in 1a, which are collectively called devices including healthcare devices.
  • devices such as smart watches shown in 1a, which are collectively called devices including healthcare devices.
  • blood pressure and heart rate data By adding blood pressure and heart rate data, it becomes an integrated integrated data, which enables integrated analysis of not only PFC but also exercise to support people's choices and decisions.
  • the data collected by healthcare devices is mainly exercise data such as steps, distance, and calories burned, but in recent years, devices that can collect health support data such as heart rate and sleep records have appeared.
  • Sphygmomanometers, blood glucose meters, weight scales / body composition analyzers, activity meters, etc. equipped with communication functions have also been commercialized, and data collected from these devices can also be integrated and utilized with PFC.
  • the exercise data of 1 is generally accumulated in the built-in storage of the smartphone paired with the healthcare device via Bluetooth or the like. This data can be freely obtained from external systems and applications at any time via the library provided by the OS.
  • Recent smartphones are equipped with a motion coprocessor that processes sensor information such as gyro sensors, accelerometers, and compasses, which is less accurate than dedicated healthcare devices, but the smartphone itself can also be used as a pedometer. It is possible.
  • Meal photography 2a is a smartphone application (hereinafter referred to as MHA application) to which the present invention is applied.
  • MHA application a smartphone application
  • the first step of the MHA app is to take pictures of food, beverages, and sometimes medicines that enter the body and read codes, but before that, I will briefly explain the work required.
  • the photos / codes, PFC, exercise data / health supplement data, etc. uploaded by the user will be integrated and further accumulated.
  • the 6a and 7a servers are built on the cloud platform.
  • Meals, beverages, drug images, cosmetics, skin, blood vessels Upload Meal photos taken with the MHA app are uploaded to storage on the 7a cloud server via the Internet, unless the user deletes the account thereafter. Stay in the cloud. If the upload is successful, the user may delete the photo from their smartphone.
  • the upload of 3 is managed by linking it to the database as a user-specific history, and it is also used for learning AI. Uploads can be treated as anonymous data, and it is possible to propose PFC, which is a dietary factor, with numerical values and photographs. When registering the account described in 2, specify the purpose of use and ask for consent for secondary use by the MHA app.
  • Image input The process of recognizing 3 included in the photograph uses the external image recognition AI service described in 4a.
  • Image recognition technology has been created by major IT companies based on many years of research, and in recent years it has been released in a form that can be used by general users. In other words, this technology already has sufficient practicality, and it is not the kind of technology that will be newly developed and competed in the future. Needless to say, it is AI learning that improves the recognition accuracy of uploads. Since the development of blueprints and theories has been completed, improvement in recognition accuracy cannot be denied if learning is performed according to individual characteristics.
  • a trained model is prepared for such an image recognition AI service, and for the present invention, "a recognition model for something that enters or touches the body" is selected, or another model is used. If you enter something that touches the body uploaded in 3 to this AI, all the objects contained in the image will be recognized. All means that one is recognized as one, and three is recognized as three.
  • the accuracy of the present invention is increased by continuous use. In other words, it is the learning effect of AI. I want to clearly define the following behavior. In addition, since it is difficult to calculate the amount of food and drink with AI at the beginning, if 4 to 7 are completed normally, there is a degree of freedom to have the user correct the amount before 8 or at the same timing as 8. Yes (numerical correction by human, voice correction) is possible. AI will relearn this correction as well.
  • the collation PFC engine works in conjunction with the database, as shown in 6a. Let's take breakfast of toast, fried egg, and bacon as an example.
  • the PFC of each food is P: 4.9g, F: 2.6g, C: 30.3g per toast, P: 6.7g, F: 7.6g, C: 0.2g, bacon per fried egg.
  • the primary assumption is P: 2.58g, F: 7.82g, C: 0.06g per sheet.
  • the PFC engine plays a role of collation by registering those PFC grams in the database and matching them with the correction of 5.
  • 8a is the entrance to correct, input, and select PFC by yourself. If you include a few normal patterns that capture and upload what goes into your body, there are four main patterns to get PFC.
  • the first is a pattern that accepts the result of 8 as it is.
  • the second is a pattern that manually corrects the result of 8 after performing 2 to 3
  • the third is a pattern that inputs PFC from the beginning in the second stage
  • the fourth is the one that the user himself uploaded in the past.
  • It is a pattern (reuse of PFC) to select the one that is close to the meal that you ingested from the ones uploaded by other users.
  • PFC can be calculated automatically, and can be input manually, by voice, barcode, etc., and the automatically calculated result can be corrected. We can meet all your needs.
  • the data accumulated through the MHA app is accumulated in the cloud.
  • This data can be provided externally by installing an API on the PFC server of 7a, and in the future, cooperation with medical, national, corporate and school systems, external application systems and services, anonymous big
  • the range of applications, such as sales as data, is endless. So far, we have explained the connections related to KPIs of MHA applications, and at the same time, explained the significance and novelty of integrated display.
  • FIG. 2 a display example on the information communication terminal B is shown on the left side, and an image recognition AI service is displayed on the right side.
  • the information and communication terminal B S-phone Wave
  • Protein P: protein
  • Fat F: lipid
  • Carbohydrates C: carbohydrate
  • Al Alcohol ingested next.
  • Alcohol 5" Alcohol 5".
  • the degree of sleep quality is shown as "SleepQ good”
  • the degree of exercise quality is shown as “Exercise perfect”.
  • HR / BP good the degree of heart rate and blood pressure quality
  • BMI index the height-to-weight ratio
  • Waist size fine the waist size
  • the change status of the dosage is shown as "Drug reductions”
  • the blood is shown as "Blood great”. In this way, the status of blood in general may be shown, or individual measured values such as blood glucose level may be displayed.
  • the indications for heart rate and blood pressure quality, height-to-weight ratio (BMI index), waist size, dosage changes, and blood glucose levels are just examples.
  • the display can be changed as appropriate, such as displaying graphically.
  • PFC and Al can be captured and accumulated. Also, 1. 1. 2. Capture and collect electronic signals such as barcodes (supermarkets, drug stores, convenience stores, etc.) with smartphones. Capture, integration, and correction with a voice input system 3. AI captures and accumulates PFC + Al by photographs 4. Improve the accuracy of captured data by manual correction (input)
  • Integrated display using AI is an invention that can be displayed on the screen of a device such as a wearable, a smartphone, a PC, a VR, or the like.
  • the user is the first invention in the world to be able to self-determine what and how much to change.
  • the American Sleep Education Society (sleep education.com) recommends the following to the public:
  • BMI body mass index
  • EE factor scores were calculated using confirmatory factor analysis and dietary patterns were identified using principal component analysis.
  • the association of EE with health behavior and BMI z-score was analyzed using a multilevel model that included age, gender, and household income as covariates.
  • the positive link between EE and physical activity and watching television was inconsistent between sites. Results tended to be similar for boys and girls.
  • EE is independent of BMI, but prospective studies are needed to determine whether higher EE in children predicts unwanted dietary patterns and the development of obesity over time.
  • MHA collects data from devices such as wearables and smartphones for a long period of time and returns it to the user, so that the user can work on improvement for good sleep for a long period of time.
  • MHA is an invention that allows patients to always discuss and support better medical care with their doctors.
  • H) A smartphone camera captures skin data and AI accumulates. It is an invention that allows users to compare and refer to past data and the present at any time and find the true value of cosmetics and esthetics. In reality, even in cases such as psoriasis in dermatology, case photo recording with a smartphone camera is useful, and it is an invention that can be integrated and examined with other KPIs possessed by MHA. Users can think deeply like never before.
  • FIG. 3 is a block diagram of a smartphone corresponding to the information communication terminal B
  • FIG. 4 is a block diagram of a wearable terminal corresponding to the device A.
  • the cloud server C is not shown, it will be described as a computer system equipped with the AI engine described above.
  • CPU 21, ROM 22, RAM 23 and the like are connected to each other via a bus 24 as main control elements.
  • a basic program or data for the smartphone 20 to function is recorded in the ROM 22, and the CPU 21 expands the data or program in the memory area of the RAM 23 to perform predetermined control.
  • the RAM 23 is mainly a volatile device
  • the storage 25 is a non-volatile area
  • the storage 25 is a variety of application programs that add functions in addition to the basic program, image data, audio data, and the like.
  • the display 26 displays information based on control by the CPU 21.
  • the display 26 has a touch function, and provides an input function such as a software keyboard by displaying an image and detecting touch information.
  • the smartphone 20 can perform voice call communication and data communication via the network interface 27. Data communication supports both WAN and LAN. With this data communication function, the smartphone 20 performs data communication with the cloud server C. By uploading and downloading the predetermined data, the smartphone 20 and the cloud server C share the functions and the MHA application of the present invention is executed.
  • Various devices are connected to the smartphone 20 via the interface 28.
  • a camera 29, a microphone 31, a Bluetooth interface (BT) 32, a barcode reader 33, and the like are connected.
  • the barcode reader 33 can also be realized by software processing using the camera 28 as an image pickup element.
  • a power button (not shown), a volume up / down button, and the like are also connected via the interface 28.
  • the smartphone 20 communicates with the wearable terminal 40 via the Bluetooth interface 32.
  • a control unit 41 including a CPU and the like and a display 42 with a touch function are connected via a bus 43.
  • An acceleration sensor 44, a heart rate sensor 45, a GPS sensor 46, and the like are further connected to the bus 43, and the control unit 41 obtains the detection signals of these sensors and obtains predetermined motion data.
  • a Bluetooth interface 47 is connected to the bus 43, and the control unit 41 can communicate with the smartphone 20 via the Bluetooth interface 47.
  • the wearable terminal 40 is supposed to be worn by the user on a daily basis, and the control unit 41 accumulates motion data based on the detection signal of the sensor and transmits the motion data to the smartphone 20 at a predetermined timing.
  • 5 to 7 are flowcharts of programs executed by the smartphone 20, the wearable terminal 40, and the cloud server C, respectively. Although the processing of each step does not match the execution instruction of the CPU 21 or the control unit 41, it is functionally expressed.
  • each step includes the step shown in the step.
  • the process of step S100 is a process of a higher concept including the process of steps S102 to S114, and in the process of step S100, the processes of steps S102 and S110 are executed, and the process of step S102.
  • the processes of steps S104 to S108 are sequentially performed, and in the process of step S110, the processes of steps S112 and S114 are sequentially performed.
  • step S100 the CPU 21 requests the wearable terminal to transmit data in step S102 and requests the non-wearable terminal to transmit data in step S110 in order to acquire the exercise data. Since it communicates with the other party that sends data, processing is performed for each device on the other party.
  • step S102 the wearable terminal that makes the data transmission request performs the following processing.
  • the control unit 41 collects exercise data in step S200, and if there is a request for transmission of exercise data in step S208, the collected exercise data is transmitted in step S210. Normally, there is no request for transmission of exercise data, and exercise data is collected as an original function of the wearable terminal 40 in step S200.
  • the wearable terminal 40 of this embodiment includes an acceleration sensor 44, a heart rate sensor 45, and a GPS sensor 46, and counts a pedometer using the detection result of the acceleration sensor 44, and the acceleration sensor 44 and the GPS sensor 45 The movement amount is measured using the detection result, and the heart rate is measured using the detection signal of the heart rate sensor 45.
  • data based on the physical characteristics of the user is set in advance as a setting. For example, data such as height, weight, gender, age, and stride length are available.
  • step S202 the control unit 41 collects exercise data as a pedometer, in step S204, collects exercise data as a movement amount, and in step S206, collects heart rate information associated with exercise as exercise data. Do. In the wearable terminal 40, when motion is detected, the detection result of each sensor may be used.
  • the partner device with which communication is performed in step S110 is a non-wearable terminal.
  • the device that collects exercise data does not refer to direct exercise, whereas the wearable terminal 40 collects exercise data indicating the amount of activity, but the data of body weight and blood pressure have important meaning. And these are more accurate data to use with non-wearable instruments than to collect with wearables. Therefore, in this embodiment, the non-wearable data transmission request is made. Specifically, in step S112, the body weight data is acquired from the non-wearable weight scale, and in step S114, the blood pressure data is acquired from the non-wearable sphygmomanometer.
  • the number of devices that can communicate with the smartphone 20 is increasing due to Bluetooth for weight scales and blood pressure monitors, and by pairing in advance, when communication with the smartphone 20 becomes possible, it will be detected and described above.
  • Many weight scales have a function as a body composition meter, and by transmitting the measured body composition meter data together, exercise is performed when comparing with PFC data and exercise data including energy consumption. You will also be able to judge the efficiency of. For example, it becomes easy to determine whether exercise contributes to a decrease in body fat percentage.
  • the smartphone 20 makes a data transmission request via communication in Bluetooth at a predetermined timing, and when the data transmission request is made to the wearable terminal 40 in step S102, the wearable terminal 40 steps.
  • the transmission request is received in S208, and exercise data including pedometer data, movement amount data, and heart rate data is transmitted in step S210.
  • the smartphone 20 acquires the exercise data and saves it in the storage 25.
  • the CPU 21 acquires ingestion information in step S116.
  • the ingestion is all meals, snacks, etc. that the user ingests.
  • image data of a meal bar code data purchased, image data of a receipt of a meal, image data of a beverage, and a beverage Use the image data of the receipt you ordered.
  • the smartphone 20 acquires these data as much as possible. Specifically, when the user has a meal, the smartphone 20 is operated so that the CPU 21 takes a picture of the meal and saves it as image data in step S118. When the foodstuff is purchased, the barcode is read and the barcode data is saved in step S120. Similarly, in step S122, the meal receipt is read and the order contents and purchase contents are saved, in step S124, the beverage is photographed and saved as image data, and in step S126, the receipt related to the beverage is read and the order contents are saved. And save your purchase. By repeating this on a daily basis, the smartphone 20 performs a process of acquiring ingestion information in step S116.
  • the CPU 21 makes it possible to correct the information of the ingested food, and accepts the correction of the ingested amount by the user. As a result, accurate PFC data can be obtained.
  • the CPU 21 of the smartphone 20 After collecting the ingested data, the CPU 21 of the smartphone 20 uploads the acquired information in step S130.
  • the other party to upload the information is the cloud server C, and the CPU 21 starts communication with the cloud server C using a predetermined protocol. Then, the CPU 21 uploads the image data in step S132, the command in step S134, the audio data in step S136, and the correction data in step S138.
  • the command means data that represents the content such as "same as the previous day's meal” or "usually eaten meal 1" instead of taking a picture of the meal and sending the image data.
  • a command may be used in which a meal whose PFC data can be specified is registered in advance, and instructions such as meal 1 and meal 2 are given.
  • the voice data is such that the user verbally explains the contents of the meal and the contents of the beverage, and the voice data is uploaded to the cloud server C.
  • the cloud server C waits for upload from the smartphone 20 in step S300, and when uploaded, acquires the uploaded data in steps S302 to S308.
  • image data is acquired
  • step S304 a command is acquired
  • step S306 audio data is acquired
  • step S308, correction data is acquired.
  • the cloud server C After acquiring the data, the cloud server C identifies the image in step S310.
  • the content of the meal is identified by using the AI engine for material determination based on the image data. The results are mainly the materials used and their amounts.
  • a PFC weighing AI engine is used based on the identified material. Primarily, once the material and amount are determined, the amount of protein, fat and carbohydrate contained in it can be identified. By adding other information included in the content of the meal, such as the name of the dish, the amount of protein, fat, and carbohydrate can be identified more accurately.
  • the main purpose of using this cloud server C is to measure the amount of protein, fat, and carbohydrate ingested by the user as accurately as possible. Therefore, as one means, the ingested meal is photographed, and the material and amount used are determined from the image. It is assumed that the amount of protein, fat, and carbohydrate contained in a predetermined unit weight for each material is digitized. Such data conversion is generally already completed. Therefore, if you know the ingredients and amount, you can calculate the amount of protein, fat, and carbohydrate ingested by the user.
  • the image data not only the image of the meal but also the image of the receipt, the barcode, etc. can be used.
  • the contents of that dish should be stored in a database in advance, or it should be associated with an image taken by another user. Then, you will be able to specify the contents of the meal.
  • the command, voice data, and correction data are identified in step S316. If there is any processing required for the image data based on the audio data and correction data, execute it. For example, the material and amount may be specified while specifying the meal based on the command and voice data.
  • a command a command "same as what I ate yesterday" or a command "standard breakfast 1" in which a standard breakfast is registered can be assumed. Designate a meal that is so-called a meal that has already been registered.
  • voice data the content of the meal is expressed by voice, and the amount of protein, fat, and carbohydrate ingested by the user is calculated based on the reproduced meal.
  • the PFC data is corrected in step S318 based on the correction data. For example, if two people eat a meal, each value in the PFC data is halved.
  • the PFC data is downloaded in step S320. Downloading is a process of transmitting the generated PFC data to the smartphone 20.
  • the CPU 21 downloads the PFC data in step S140 and stores the PFC data in the storage 25.
  • Exercise data is also stored in the storage 25.
  • the smartphone 20 displays information in step S142. As shown in FIG. 2, the display is performed. Specifically, the PFC data is displayed in step S144, and the exercise data is displayed in step S146.
  • the above-mentioned PFC data is displayed as the user's health management, and the user's exercise data corresponding to the above-mentioned PFC data can mainly determine the energy consumption and the content of the muscle training. Those are suitable.
  • the energy consumption reflects the content of the exercise performed at the fitness gym in addition to the measured number of steps and the amount of movement. Since various exercise devices are equipped with a function that can display the energy consumption, the energy consumption is communicated by Bluetooth and transferred to the smartphone 20, or the exercise device converts the energy consumption into a QR code and displays it on the smartphone. 20 may take a picture and read the energy consumption.
  • the smartphone 20 may use the display 26 of the smartphone 20 to input information such as the type, load, number of times, and interval of muscle training as well as information on the time zone during exercise.
  • the fact that the amount of protein intake and the time zone correspond to the content and time of muscle training is one of the elements of health management judgment based on PRC data.
  • a health information management system that includes a mobile terminal device and a server device that can communicate with each other via a network.
  • the server device receives the information indicating the content and amount of the meal, it identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and returns the data representing the amount of this protein, fat and carbohydrate. It is possible and The mobile terminal device can collect exercise data representing the content and amount of exercise of a predetermined user. Information indicating the content of the meal ingested by the user is transmitted to the server device, The server device returns and receives data representing the amount of protein, fat and carbohydrate ingested by the user.
  • a health information management system that displays the exercise data for a predetermined period and data representing the amount of protein, fat, and carbohydrate ingested by the user.
  • the mobile terminal device is provided with a camera, images the meal ingested by the user to obtain image data, and transmits the image data to the server device as information representing the contents of the meal ingested by the user.
  • the server device is a health information management system that receives image data of images of meals ingested by the user as information representing the contents and amount of meals.
  • the mobile terminal device is equipped with a microphone, and the user transmits a voice explaining the content and amount of the ingested meal as voice data to the server device.
  • the server device identifies the audio data, and if the audio data represents the content and amount of the ingested meal, identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and this A health information management system that returns data representing the amount of protein, fat and carbohydrates.
  • the mobile terminal device captures a two-dimensional image showing the content and amount of the meal ingested by the user, and transmits the two-dimensional image data to the server device. If the two-dimensional image data represents the content and amount of the ingested meal, the server device identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and the protein, fat and carbohydrate.
  • a health information management system that returns data representing the amount of.
  • the mobile terminal device can communicate with a wearable terminal worn by the user, and the wearable terminal collects data on the content and amount of exercise of the user and provides health information by communicating with the mobile terminal device. Management system.
  • the server device accumulates and analyzes information representing the contents of meals transmitted from the plurality of mobile terminal devices, and autonomously identifies the amount of protein, fat, and carbohydrate contained in the contents and amount of the meal.
  • a health information management system that has the function of improving the accuracy of meals. It can be connected via a network, and when it receives information indicating the content and amount of a meal, it identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and determines the amount of this protein, fat and carbohydrate.
  • a mobile terminal device capable of communicating with a server device capable of returning the representative data via the above network. The mobile terminal device can collect exercise data representing the content and amount of exercise of a predetermined user.
  • Information indicating the content of the meal ingested by the user is transmitted to the server device, Receives data representing the amount of protein, fat and carbohydrate ingested by the user, returned by the server device, A portable terminal device that displays the exercise data for a predetermined period and data representing the amount of protein, fat, and carbohydrate ingested by the user.
  • the mobile terminal device can communicate with a wearable terminal worn by the user, and the wearable terminal collects data on the content and amount of exercise of the user and communicates with the mobile terminal device to provide the mobile terminal.
  • the server device accumulates and analyzes information representing the contents of meals transmitted from the plurality of mobile terminal devices, and autonomously identifies the amount of protein, fat, and carbohydrate contained in the contents and amount of the meal.
  • a mobile terminal device having a function of improving the accuracy of the operation.
  • the mobile terminal device collects exercise data representing the content and amount of exercise of a predetermined user, and collects exercise data.
  • the mobile terminal device transmits information representing the contents of the meal ingested by the user to the server device, and the mobile terminal device transmits the information to the server device.
  • the server device receives information indicating the content and amount of the meal, identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and outputs data representing the amount of this protein, fat and carbohydrate.
  • the mobile terminal device receives data indicating the amount of protein, fat, and carbohydrate ingested by the user, which is returned by the server device.
  • the mobile terminal device is a health information management method that displays the exercise data in a predetermined period and data representing the amount of protein, fat, and carbohydrate ingested by the user. It can be connected via a network, and when it receives information indicating the content and amount of a meal, it identifies the amount of protein, fat and carbohydrate contained in the content and amount of the meal, and determines the amount of this protein, fat and carbohydrate. It is a health information management program of a mobile terminal device that can communicate with a server device that can return the data to be represented via the above network.
  • the health information management program is applied to the mobile terminal device.
  • a function to collect exercise data showing the content and amount of exercise of a given user A function to send information indicating the contents of the meal ingested by the user to the server device, and The function to receive the data representing the amount of protein, fat and carbohydrate ingested by the user, which is returned by the server device, and
  • a health information management program for a mobile terminal device that realizes a function of displaying the exercise data in a predetermined period and data representing the amount of protein, fat, and carbohydrate ingested by the user.
  • the present invention is not limited to the above examples. Needless to say, those skilled in the art -Applying the mutually replaceable members and configurations disclosed in the above-mentioned Examples by appropriately changing the combination thereof-Although not disclosed in the above-mentioned Examples, it is a known technique and the above-mentioned Examples.
  • the members and configurations that can be mutually replaced with the members and configurations disclosed in the above are appropriately replaced, and the combinations thereof are changed and applied.-Although not disclosed in the above examples, known techniques and the like It is an embodiment of the present invention to appropriately replace the members and configurations that can be assumed as substitutes for the members and configurations disclosed by those skilled in the art based on the above, and to change and apply the combinations thereof. It is disclosed as.
  • BT Bluetooth interface

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  • Health & Medical Sciences (AREA)
  • Nutrition Science (AREA)
  • Engineering & Computer Science (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

Il n'a pas été possible d'afficher les protéines (P), la graisse (F) et les glucides (C) ingérés, ni la quantité d'exercice.　Un terminal portable (40) collecte des données d'exercice à l'étape S200, et, lorsqu'il est déterminé qu'une requête d'émission de données d'exercice est effectuée à l'étape S208, transmet les données d'exercice à l'étape S210. Un téléphone intelligent (20) acquiert les données d'exercice à l'étape S100, acquiert des informations sur les substances ingérées à l'étape S116, corrige les informations sur les substances ingérées à l'étape S128, et téléverse les informations acquises à l'étape S130. Un serveur en nuage (C) se tient prêt au téléversement depuis le téléphone intelligent (20) à l'étape S300, identifie une image en utilisant la technologie IA à l'étape S310 sur la base des données téléversées, calcule les PFC ingérés sur la base de l'image et corrige les PFC ingérés, puis entraîne le téléchargement données de PFC par le téléphone intelligent à l'étape S320. Le téléphone intelligent (20) affiche les données PFC téléchargées à l'étape S144, et affiche les données d'exercice à l'étape S146.
PCT/JP2019/020197 2019-05-22 2019-05-22 Système de gestion d'informations de santé, dispositif de terminal mobile, procédé de gestion d'informations de santé et programme de gestion d'informations de santé pour dispositif de terminal mobile WO2020235028A1 (fr)

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CN114613504A (zh) * 2022-03-09 2022-06-10 北京无极慧通科技有限公司 基于ai技术的医疗健康智能管理方法及服务平台

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JP2011221637A (ja) * 2010-04-06 2011-11-04 Sony Corp 情報処理装置、情報出力方法及びプログラム
JP2015534686A (ja) * 2012-09-25 2015-12-03 セラノス， インコーポレイテッド 回答の較正のためのシステムおよび方法
JP2015225460A (ja) * 2014-05-27 2015-12-14 京セラ株式会社 食事管理方法、食事管理システム及び食事管理端末
JP2016173658A (ja) * 2015-03-16 2016-09-29 株式会社ブイカム 健康管理システム、健康管理方法、プログラム、及び記録媒体
WO2017022017A1 (fr) * 2015-07-31 2017-02-09 株式会社ＦｉＮＣ Serveur de gestion de santé, procédé de commande de serveur de gestion de santé et programme de gestion de santé

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JP2011221637A (ja) * 2010-04-06 2011-11-04 Sony Corp 情報処理装置、情報出力方法及びプログラム
JP2015534686A (ja) * 2012-09-25 2015-12-03 セラノス， インコーポレイテッド 回答の較正のためのシステムおよび方法
JP2015225460A (ja) * 2014-05-27 2015-12-14 京セラ株式会社 食事管理方法、食事管理システム及び食事管理端末
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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