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
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
• • 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
  • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
• • 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
  • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
  • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
• • 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
• • 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
  • G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
• • 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
  • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
  • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
  • G16H40/67—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for remote operation
• • 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
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
• • 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
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
• • 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
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
• • 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
  • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
  • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

• the present invention relates to a system and method for providing a health promotion program tailored to the health status of users, including home occupants.
• a smart house generally refers to a home that uses information technology (IT) to control household appliances that use electricity and gas, such as lighting fixtures, cooking utensils, and heating and cooling equipment, thereby optimizing energy consumption.
• IT information technology
• Patent Document 1 JP 2013-15271 A discloses an indoor environment control system that includes an environmental information detection unit, an occupant information detection unit, a control unit, and an operation unit, in order to control the indoor environment according to the comfort of each occupant with a simple system without increasing time and energy costs.
• the storage means of the control unit is provided with a database that records the relationship between occupant information and the comfort of the occupants.
• the calculation means of the control unit generates an adjustment signal to use the occupant information as a fluctuation index to control the operation unit so that the comfort of the occupants is optimized in the database based on the occupant information detected by the occupant information detection unit.
• Patent Document 2 discloses a safety confirmation system that accurately determines whether a resident is abnormal and responds more safely, reliably, and quickly when an abnormality occurs.
• This safety confirmation system is installed in the residence and includes a first sensor that detects at least the resident's heart rate and respiratory rate without contact, an abnormality determination unit that detects significant differences from normal in the heart rate and respiratory rate, a communication unit that communicates between the operator and the resident to confirm the safety, an arrival information presentation unit that notifies the operator of the arrival of emergency personnel at the residence in response to a dispatch request from the operator, and a remote unlocking unit that emergency unlocks the residence at the operator's command.
• Patent Document 1 The indoor environment control system of Patent Document 1 is limited to environmental control.
• the safety confirmation system of Patent Document 2 is limited to safety confirmation.
• the present invention aims to provide a system and method that can effectively utilize biometric data obtained in the daily lives of users, including residents, to maintain and improve the health of users.
• one aspect of the present invention is to provide a method for manufacturing a semiconductor device comprising the steps of:
• a system for providing a health promotion program for a user comprising: a first sensor installed in a first facility and configured to continuously collect biometric data of the user; a second sensor installed in a second facility and configured to continuously collect the same type of biometric data from the user as the first sensor; an analysis device configured to perform an analysis including a comparison of the biometric data collected by the first and second sensors to determine a health condition of the user; a program generation device configured to generate a health promotion program tailored to the user based on the health condition determination result;
• the present invention provides a system comprising:
• the biometric data includes not only biometric data but also dynamic data.
• the health promotion program includes not only exercise programs, but also various programs and various health information for maintaining and improving the mental and physical health of the user.
• the biological data is at least one type of data among facial expression, heart rate, oxygen saturation, carbon dioxide exhalation, surface body temperature, deep body temperature, skin protein analysis, body composition, autonomic nerves, HbA1c, body water content, foot pressure distribution, walking posture, walking speed, change in joint range of motion, fluctuation in center of gravity, activity level, electromyography, electrocardiography, electroencephalography, standing and sitting postures, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleep time, amount and time of excretion, blood components, urine components, saliva components, images of the oral cavity, and fecal components.
• the analysis device It is preferable to execute a procedure of using a person's biometric data including at least one type of data among a person's facial expression, heart rate, oxygen saturation, carbon dioxide exhalation, surface body temperature, deep body temperature, skin protein analysis, body composition, autonomic nerves, HbA1c, body water content, foot pressure distribution, walking posture, walking speed, change in range of joint motion, fluctuation of center of gravity, activity level, electromyography, electrocardiography, electroencephalography, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleep time, amount and time of excretion, blood components, urine components, saliva components, images of the oral cavity, and fecal components, as well as the person's health condition including the person's energy consumption and exercise effect as teacher data, generating a predictive model by machine learning, which takes the person's biometric data as input and outputs the person's health condition, and using the predictive model to output the user
• the program generation unit extracting biometric features of the user by comparing the collected biometric data with a predetermined evaluation index; It is preferable to use predetermined evaluation indicators and an evaluation value indicating the effectiveness of a health promotion program as teacher data, generate an evaluation model by machine learning, with biometric data as input and an evaluation of the health promotion program as output, and use the evaluation model to output a health promotion program to be presented to the user together with the user's biometric characteristics from the collected biometric data.
• a method for providing a wellness program for a user comprising: a computer having a processor and a memory, comprising: Continuously collecting biometric data of the user using a first sensor installed in a first facility; A second sensor installed in a second facility continuously collects the same type of biometric data from the user as the first sensor; performing an analysis, including a comparison of the biometric data collected by the first and second sensors, to determine a health status of the user; and a method for generating a health promotion program tailored to the user based on the health condition determination result.
• biometric data obtained from a user's daily life can be effectively utilized to maintain and improve the user's health.
• FIG. 1 is a schematic diagram showing an overview of an exemplary embodiment of the present invention.
• 1 is a block diagram showing the overall configuration of a system 1 for providing a health promotion program.
• 13 is a flowchart showing a processing procedure for providing a health promotion program.
• the term "facility” refers to any facility (also referred to as facility, institution, site, place, activity base, etc.) in which the system according to the present invention can be used.
• facilities include, but are not limited to, homes, workplaces, educational facilities such as schools, medical facilities such as hospitals and health checkup centers, nursing homes, care facilities, elderly care facilities, exercise facilities, entertainment facilities, meeting places, and accommodation facilities. Biometric data of users who spend their daily lives in these facilities is sensed by sensors placed in various places in the facilities.
• the workplace may include moving objects such as vehicles, ships, and aircraft.
• biometric data refers to all data that can be obtained from the user's body.
• Biometric data includes not only dynamic data such as walking posture, walking speed, changes in range of motion of joints, fluctuations in the center of gravity, and activity level, but also static data including time-series data such as electromyography, electrocardiography, and electroencephalography, standing and sitting posture, body temperature, blood pressure and blood flow, heart rate, breathing, sweating, eyeballs, sleep time, amount and time of excretion, blood components, urinary components such as blood glucose level, saliva components, images of the inside of the oral cavity (tongue, pharynx, etc.), and fecal components.
• a home refers to a building in which a person lives daily, and includes not only detached houses and apartment buildings, but also so-called nursing homes.
• the obtained biometric data is analyzed by computer to determine the health condition of the user. For example, by comparing the same type of biometric data sensed in different facilities, i.e., a first and a second facility, i.e., the first and second biometric data, the health condition of the user in different environments can be accurately evaluated.
• the user can be evaluated as being under excessive tension at the workplace.
• the measured values of a user's blood pressure, heart rate, respiratory rate, blood flow, and sweat rate at the exercise facility are greater than the corresponding values at the user's home by a predetermined tolerance, the user can be evaluated as performing moderate exercise.
• the user can be evaluated as being in a state of physical inactivity at the workplace.
• the measured blood pressure, heart rate, and respiratory rate at a user's accommodation and entertainment facility are lower than the corresponding values at the user's home by a set value or more, the user can be evaluated as being in a suitably relaxed state at the accommodation and entertainment facility.
• the EEG measurements of a user at an educational facility or workplace differ from the corresponding values at the user's home, the user may be assessed as lacking concentration.
• the health promotion programs created here include exercise programs, as well as various programs and health information for maintaining and improving the user's physical and mental health.
• the system may suggest measures to reduce the tension, such as improving work, taking time off, exercising moderately, getting enough sleep, and consulting a doctor.
• measures to reduce the tension such as improving work, taking time off, exercising moderately, getting enough sleep, and consulting a doctor.
• an exercise menu that is appropriate for the user's health condition may be presented.
• a suggestion may be made to him or her to do moderate exercise.
• Such health promotion programs are provided to users, and the users implement the programs.
• the user's health functions are improved.
• the collected biometric data may be collected in a data server or the like and presented to and used by medical professionals such as doctors with the user's consent.
• the generated health condition assessment results may also be presented to and used by medical professionals with the user's consent.
• the collected biometric data and the generated health status assessment results may be used by government and local communities to create more comfortable and livable communities, to manage the health of residents, and to enable people to continue living independently in their own homes after retirement.
• the system 1 has a sensing function that collects biometric data from users in the facility, an analysis and assessment function that analyzes the collected biometric data to assess the health status of the users, and a program generation function that generates a health promotion program based on the results of the health status assessment.
• the system 1 includes a sensor 10, an analysis device 20, a program generation device 30, an output device 40, and a storage device 50 (see FIG. 2). These components are communicatively connected via a wired or wireless communication network.
• the sensor 10, the analysis device 20, the program generation device 30, the output device 40, and the storage device 50 may be separate or may be configured integrally.
• the storage device 50 may be installed as a component of a data center (not shown), and the data center may store not only collected data but also collected samples (e.g., saliva and fecal samples). In this case, the data center may be called a biobank.
• the sensor 10 is a general term for various sensors that are installed in a facility 70 such as a house and continuously collect biometric data of a user H.
• the sensor 10 transmits and stores the collected biometric data together with the collection date and time in a storage device 50.
• An example of the storage device 50 is a data server, but is not limited to this.
• the sensor 10 may transmit and store the collected biometric data in a storage device (not shown) in the facility 70.
• Examples of available sensors 10 include, but are not limited to, infrared temperature cameras, hyperspectral imaging cameras, RFID sensors, pressure sensors, motion capture devices, temperature sensors, component analyzers, weight sensors, flow sensors, and position sensors (none of which are shown).
• the infrared temperature camera is installed in a room such as the living room, study, bedroom, and office of the facility 70, and can measure the facial expression, heart rate, oxygen saturation, carbon dioxide exhalation, surface body temperature, and deep body temperature of the user H.
• the hyperspectral imaging camera is installed, for example, at the entrance and its vicinity, and in addition to the personal authentication function of the user H, can perform skin protein analysis of the user H, measurement of body composition (e.g., composition ratio of fat, bone, lean soft tissue, etc., weight, body fat, basal metabolism, visceral fat, muscle mass, bone mass), and measurement of autonomic nerves (e.g., autonomic nerve fatigue level, balance of sympathetic nerves and parasympathetic nerves).
• body composition e.g., composition ratio of fat, bone, lean soft tissue, etc., weight, body fat, basal metabolism, visceral fat, muscle mass, bone mass
• autonomic nerves e.g., autonomic nerve fatigue level, balance of sympathetic nerve
• the hyperspectral imaging camera is installed at the dining table, kitchen, and their vicinity to measure glycohemoglobin (HbA1c).
• the RFID sensor is installed on the wall surface of the room and can measure the amount of water in the body of the user H.
• the pressure sensor is installed, for example, in the hallway and on the floor surface of the room, and can measure the foot pressure distribution of the user H.
• the motion capture device can be installed, for example, in a hallway inside a room to measure the walking posture, walking speed, changes in joint range of motion, fluctuations in the center of gravity, and activity level of user H.
• the sensing of the biometric data of the user H by the sensor 10 may be performed, for example, by constant measurement.
• the sensor 10 is preferably installed hidden from view so that the user H cannot immediately find it. This is to prevent the user H from becoming nervous upon noticing the presence of the sensor 10, and the sensed biometric data from deviating from the daily values of the user H. In other words, this is to know the user H in a natural state in daily life.
• a sensor for obtaining environmental data may be installed. That is, a thermometer for measuring room temperature, a hygrometer for measuring room humidity, a light meter for measuring room brightness, a carbon dioxide meter for measuring room CO2 concentration, etc. may be installed.
• the environmental data collected by these sensors is transmitted to and stored in the storage device 50. It is to be noted that the sensor 10 may include these sensing functions.
• the user's biometric data may be acquired using various sensors built into the wearable device and mobile information terminals such as smartphones. This type of biometric data is useful as an aid in determining the user's health condition.
• the sensor 10 may include these sensing functions.
• the collected biometric data, environmental data, and other data are transmitted to and stored in the storage device 50.
• the storage device 50 may also store the health condition assessment results and the generated health promotion program, which will be described later.
• the various data stored in the storage device 50 may be made available, with the permission of user H, to an external computer 80 used by the doctor and medical institution in charge of user H. Through such medical collaboration, various health records necessary for the diagnosis and treatment of user H can be shared between medical professionals in a timely manner. Of course, data collaboration may also target doctors, dentists, pharmacists, nutritionists, etc., and is not limited to the medical field.
• the analysis device 20 analyzes the biological data collected by the sensor 10 to determine the health condition of the user H.
• the analysis device 20 can suitably use AI analysis for data analysis. That is, the analysis device 20 uses the person's biometric data including at least one of the following data: facial expression, heart rate, oxygen saturation, carbon dioxide exhalation, surface body temperature, deep body temperature, skin protein analysis, body composition, autonomic nerves, HbA1c, body water content, foot pressure distribution, walking posture, walking speed, change in range of motion of joints, fluctuation of center of gravity, activity, electromyography, electrocardiography, electroencephalography, standing and sitting posture, body temperature, blood pressure, blood flow, heart rate, breathing, sweating, eyeballs, sleep time, amount and time of excretion, blood components, urine components, saliva components, images of the oral cavity, and fecal components, as well as the person's health condition including the person's energy consumption and exercise effect as teacher data, and generates a prediction model by machine learning in which the input is the person's biometric data and the output is the person's health condition.The analysis device 20 then uses the generated prediction model to output the user'
• the analysis device 20 can use at least one type of biological data such as a person's facial expression, heart rate, oxygen saturation, carbon dioxide exhalation, surface body temperature, deep body temperature, skin protein analysis, body composition, autonomic nerves, HbA1c, body water content, foot pressure distribution, walking posture, walking speed, joint range of motion change, center of gravity fluctuation, and activity level as a teacher variable (teacher data) as an existing index.
• the analysis device 20 can perform machine learning using the same type of biological data as the data group collected by the sensor 10 as explanatory variables to create an analysis program.
• the analysis device 20 can use basic information such as age, sex, height, and weight as parameters to be used, and can perform algorithmic analysis of motor function information such as facial expression, heart rate, oxygen saturation, carbon dioxide exhalation, surface body temperature, deep body temperature, skin protein analysis, body composition, autonomic nerves, HbA1c, body water content, and foot pressure distribution, joint angles during walking and their changes over time, lower limb muscle strength, center of gravity, or three-dimensional movement distance of an observation point correlated thereto, walking posture, walking speed, and activity level.
• the analysis device 20 applies the sensing data to the above model to obtain an objective evaluation including health function and motor function as the health condition of the user H.
• An example of machine learning that can be used in this embodiment includes, but is not limited to, deep learning.
• the analysis result 20 transmits and stores the assessment result of user H's health condition in the storage device 50. It goes without saying that such assessment result can be effectively used by related parties, including medical professionals, with user H's permission. Furthermore, by outputting the assessment result of user H's health condition to the output device 40 described below, user H and his family can detect user H's illness or poor health at an early stage, and can also use the result to manage user H's health. In other words, the assessment result can be effectively used to support user H and collaborate with related parties.
• the program generation device 30 generates a health promotion program customized for each user H based on the health condition assessment results obtained by the analysis device 20. That is, the program generation device 30 extracts the user's biometric characteristics by comparing the collected biometric data with a predetermined evaluation index. The program generation device 30 then uses the predetermined evaluation index and an evaluation value indicating the effect of the health promotion program as teacher data, and generates an evaluation model by machine learning, in which the input is the biometric data and the output is an evaluation of the health promotion program. The program generation device 30 then uses the evaluation model to output a health promotion program to be presented to the user from the collected biometric data, together with the user's biometric characteristics.
• health promotion programs are broadly divided into those related to health functions and those related to physical functions.
• the program generation device 30 clarifies the biometric characteristics of user H by comparing facial expressions, heart rate, oxygen saturation, carbon dioxide exhalation, surface body temperature, deep body temperature, skin protein analysis, body composition, autonomic nerves, HbA1c, body water content, and foot pressure distribution with the normal range for each individual and evaluation indices such as epidemiology and medical guidelines.
• the program generation device 30 uses AI analysis of the measured data, basic information such as age, sex, height, and weight, and user H's living environment data (for example, biometric data collected by sensor 10), to construct an algorithm related to each individual's lifestyle habits and present behavioral changes and the associated effects.
• the program generation device 30 clarifies the dynamic analysis characteristics of the user H by comparing the position and velocity of each joint, as well as the joint angle and angular velocity. Then, based on the measured data, the program generation device 30 can estimate the amount of energy consumed during exercise and the exercise effect, as well as construct a movement measurement algorithm, and present recommended exercises, the amount of energy consumed during exercise, and the exercise effect. Examples of AI analytics that can be used here include, but are not limited to, deep learning.
• the program generation device 30 transmits and stores the generated health promotion program in the storage device 50. Needless to say, such a program can be shared among medical professionals with the permission of user H. Furthermore, by outputting the program to the output device 40, user H and his/her family can clearly understand what they need to do to maintain, restore, and improve user H's health and motor functions, and can utilize this information in user H's health management.
• the analysis device 20 and the program generation device 30 can be configured as one or more computers including an arithmetic circuit such as a central processing unit (CPU) and memories such as random access memory (RAM) and read only memory (ROM).
• the functions of the analysis device 20 and the program generation device 30 described above can be realized by reading an execution program stored in the ROM into the RAM and executing it with the CPU.
• the analysis device 20 and the program generation device 30 use the biometric data collected continuously and at all times to determine the health condition and generate a health promotion program periodically or at a timing considered necessary for each individual.
• the output device 40 is a device that outputs the health condition assessment result and the health promotion program.
• the output device 40 includes a terminal of the user H (including a smartphone and a personal computer), a display, a printer, a projector, and a speaker.
• the output device 40 may output an alert to the terminals of the user H and those related to him/her.
• the output device 40 may also display other information related to life, such as information on energy and housing, and local information.
• step S1 the sensor 10 senses the biometric data of the user H and stores the data in the storage device 50 in association with the sensing time. Sensing is performed continuously at a predetermined time interval.
• step S2 the analysis device 20 analyzes the collected biometric data, judges the health condition of user H, and stores the judgment result in the storage device 50.
• the analysis device 20 generates a trained model in advance by machine learning, and obtains the health condition of user H by applying user H's biometric data to the trained model.
• Step S2 is executed periodically or at a timing considered necessary for each individual.
• step S3 the program generation device 30 generates a health promotion program for each user H based on the health status assessment result and stores the program in the storage device 50.
• the health condition assessment results and the health promotion program are displayed on the output device 40.
• User H can put the presented health promotion program into practice and utilize it in his/her own health management.
• the sensing data, health condition assessment results, and health promotion program accumulated in this manner are stored in the storage device 50 and, with user H's permission, are shared among medical professionals involved with user H. This is expected to enable doctors to provide more appropriate and prompt diagnosis and treatment. Such effects can be expected not only during face-to-face consultations, but also in remote consultations, realizing more comprehensive medical cooperation.
• the sensing data and the health condition judgment results can also be used as a monitoring system or an abnormality detection system for user H.
• the collected biometric data and the results of health status assessments can be used by government and local communities with the consent of User H, and can be reflected in policies for creating a more comfortable and livable community.
• the sensor 10 Since the sensor 10 is installed in the facility 70, there is no hassle of putting it on and taking it off, as there is with wearable sensors. Furthermore, since the sensor 10 is hidden from the eyes of the user H, the user H is not aware of the presence of the sensor 10. Therefore, it is possible to obtain the daily biometric data of the user H, and to grasp the health condition with a high degree of reliability.
• biometric data obtained in the user's daily life can be effectively utilized to maintain and improve the user's health.
• Utilization of biometric data includes supporting the user and sharing information with relevant parties in the medical and nursing care fields.
• Health promotion program providing system 10 Sensor 20 Analysis device 30 Program generating device 40 Output device 50 Storage device H User

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