WO2021256822A1 - Activity of daily living-based dementia prediction method and device therefor - Google Patents

Abstract

An activity of daily living-based dementia prediction method is provided. The activity of daily living-based dementia prediction method according to one embodiment of the present invention may comprise the steps of: obtaining activity of daily living related to dementia prediction by monitoring indoor behaviors of a user through an IoT device installed indoors; determining a daily pattern for the behaviors of the user by using the activity of daily living; and determining that the user is in a dementia risk state when it is detected that the behaviors of the user deviate from the daily routine through the activity of daily living.

Description

Dementia prediction method and device based on daily living ability information

The present invention relates to a method for predicting dementia based on daily living ability information and an apparatus therefor. More particularly, it relates to a dementia prediction method based on daily living ability information, and an apparatus therefor, capable of predicting and diagnosing whether or not dementia occurs early by monitoring a user's daily life based on smart home technology.

Currently, dementia is emerging as a social problem in an aging society with a declining population. In particular, since dementia cannot be cured according to the current medical level, the only solution is to predict dementia early and slow the rate of dementia. However, in the case of the elderly living alone or the elderly who are vulnerable to medical care, there is no way to predict dementia early because the social conditions for closely observing the risk of dementia are lacking. In particular, in the case of the aged elderly, since they spend most of their time at home, there is a limit to monitoring the elderly through external personnel.

Activity of Daily Living (ADL) refers to basic daily activities necessary to take care of oneself, which is treated as an important factor in predicting dementia. If the daily living ability information is monitored in real time and used usefully, it will be possible to predict dementia in the elderly at an early stage.

Therefore, there is a need for an efficient method for predicting dementia by using daily living ability information for the elderly who spend most of their time at home or spend most of their time in a similar life pattern.

The technical problem to be solved by the present invention is to provide a dementia prediction method based on daily living ability information that can predict and diagnose dementia at an early stage by monitoring the user's daily life based on smart home technology.

Another technical problem to be solved by the present invention is to provide a dementia prediction method based on daily living ability information that can precisely monitor the user's behavior as the user's daily life is monitored according to the user's daily life collection items.

Another technical problem to be solved by the present invention is to provide a dementia prediction method based on daily living ability information that can accurately determine and predict the user's dementia risk by more closely observing the user's wandering along the movement path.

The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

In order to solve the above technical problem, the dementia prediction method based on daily living ability information according to an embodiment of the present invention includes the steps of monitoring a user's indoor behavior through an indoor IoT device to obtain daily living ability information related to dementia prediction , determining a life pattern for the user's behavior by using the daily living ability information, and detecting that the user's behavior deviates from the life pattern through the daily living ability information obtained after determining the life pattern In this case, the method may include determining that the user is in a dementia risk state.

In order to solve the above technical problem, the apparatus for predicting dementia based on daily living ability information according to another embodiment of the present invention includes a processor, a network interface, a memory that is executed by the processor to load a computer program, and the computer program Including a storage to store, the computer program, the instructions for obtaining daily living ability information related to dementia prediction by monitoring the indoor behavior of the user through the IoT device installed indoors, the user's daily living ability information using the It may include an instruction for determining a life pattern for the behavior, and an instruction for determining that the user is in a dementia risk state when it is detected that the user's behavior deviates from the life pattern through the daily life ability information.

1 is a schematic diagram of a dementia prediction system based on daily living ability information according to an embodiment of the present invention.

2 is a flowchart of a dementia prediction method based on daily living ability information according to an embodiment of the present invention.

3A and 3B are exemplary diagrams illustrating a smart home system installed in a user's residence in order to monitor the user's behavior.

4 is a diagram for explaining pre-classified daily life collection items collected from user actions.

FIG. 5 is a view for specifically explaining step S200 of FIG. 2 .

6 is a diagram illustrating an example of data according to a user's personal characteristics.

FIG. 7 is a view for specifically explaining step S300 of FIG. 2 .

8 is a diagram for explaining a method of analyzing a user's roaming according to a movement path of the user.

9 is a schematic diagram of a dementia prediction system based on daily living ability information according to another embodiment of the present invention.

10 is a hardware configuration diagram of an apparatus for predicting dementia based on daily living ability information according to another embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. Advantages and features of the present invention, and a method for achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments published below, but may be implemented in various different forms, and only these embodiments allow the publication of the present invention to be complete, and common knowledge in the technical field to which the present invention pertains It is provided to fully inform the possessor of the scope of the invention, and the present invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout.

Unless otherwise defined, all terms (including technical and scientific terms) used herein may be used with the meaning commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless clearly defined in particular. The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural unless specifically stated otherwise in the phrase.

Hereinafter, some embodiments of the present invention will be described with reference to the drawings.

1 is a schematic diagram of a dementia prediction system based on daily living ability information according to an embodiment of the present invention.

Referring to FIG. 1 , a dementia prediction system based on daily living ability information according to an embodiment of the present invention may include a dementia prediction server 100 , a wearable device 200 , and an IoT device 300 .

The dementia prediction server 100 may monitor the user's daily life using data received from the user's wearable device 200 and the IoT device 300 . The dementia prediction server 100 may monitor the user's daily life and determine a life pattern according to the user's behavior. The dementia prediction server 100 indicates that the risk of dementia has occurred to the user when the user's behavior deviates from the life pattern or the user's behavior is similar to that of a dementia patient through the daily life ability information obtained after determining the life pattern. can judge The dementia prediction server 100 may store and manage information received from the wearable device 200 and the IoT device 300 in the DB 400 .

The wearable device 200 may be a device that can be attached to a user, and may detect the user's biometric information and location information and transmit it to the dementia prediction server 100 . For example, the wearable device 200 may detect biosignals of the user's heart rate and walking speed. Also, the wearable device 200 may generate location information by detecting the user's current location. The wearable device 200 may be interlocked with the user's terminal 210 to transmit/receive information to and from the user's terminal 210 . That is, the wearable device 200 may directly transmit/receive information to/from the dementia prediction server 100 or may transmit/receive information to/from the dementia prediction server 100 through a user terminal.

The IoT device 300 may be configured as a device that generates information on the operation of an electronic product and a detection signal of a sensor. The IoT device 300 may detect a user's action in real time. The IoT device 300 may be installed in a house in which a user resides and may be constructed as a smart home system. In addition, the IoT device 300 may be installed in various places such as public institutions, nursing facilities, and medical facilities. In an embodiment, the IoT device 300 may identify the user of the wearable device 200 of the user. For example, the IoT device 300 may identify which user occupants through the wearable device 200 of the user and detect the action of the identified user even if a user other than his/her home enters the building.

An electronic product is a device operated by a user's manipulation, and the sensor may be composed of various sensors capable of detecting a user's motion. The IoT device 300 is set as a group of similar types of devices or devices in adjacent locations, and each device group is connected to the dementia prediction server 100 and the gateway 310 for transmitting and receiving information to predict dementia It can be transmitted to the server 100 .

The dementia prediction system based on daily living ability information according to an embodiment of the present invention can predict and diagnose the occurrence of dementia early by monitoring the user's daily life based on smart home technology.

Hereinafter, a method for predicting dementia based on daily living ability information according to another embodiment of the present invention will be described with reference to FIGS. 2 to 9 . This embodiment may be performed by a computing device. For example, the computing device may be the dementia prediction server 100 described with reference to FIG. 1 . In describing the present embodiment, descriptions of subjects performing some operations may be omitted. In this case, the performing subject is the computing device.

2 is a flowchart of a dementia prediction method based on daily living ability information according to an embodiment of the present invention.

In step S100 of FIG. 2 , daily life ability information related to dementia prediction may be obtained by monitoring the user's behavior. The user's behavior may be detected by IoT devices and wearable devices installed inside the building. The dementia prediction server may receive information about the operation of the IoT device and the user's bio-signals and location information measured by the wearable device.

The dementia prediction server may integrate and manage the information on the user's behavior into information called daily life ability information. That is, the information received from the IoT device and the wearable device may consist of various information such as a sensor detection signal, an operation signal of an electronic product, or location information. hard to be Therefore, the dementia prediction server may pre-process or classify the information as information for judging the user's behavior so that it can be easily used to generate daily life ability information, which is information about the user's behavior.

The dementia prediction server may monitor the user's behavior through the generated daily life ability information. For example, the dementia prediction server may generate daily life ability information about the location where the user has moved by using information on the operation of the electronic product or information on which object has been moved to where. The dementia prediction server may generate daily life ability information on what kind of action the user has taken, for example, sitting, walking, lying down, washing, etc. by using the signal detected by the sensor. Also, the dementia prediction server may generate daily living ability information about the user's health condition by using the user's biosignal.

Hereinafter, a detailed method of acquiring daily living ability information by monitoring a user's indoor behavior will be described with reference to FIGS. 3 and 4 .

3A and 3B are exemplary diagrams illustrating a smart home system installed in a user's residence to monitor the user's behavior, and FIG. 4 is a diagram for explaining the pre-classified daily life collection items collected from the user's behavior.

Referring to FIG. 3A , the IoT device may be installed indoors in a user's residence and implemented as a smart home system. The IoT device may be composed of an indoor electronic product or a sensor installed at various locations in the room. The IoT device may transmit information on the operation of the electronic product to the dementia prediction server, or may transmit a signal generated by detecting a user's behavior to the dementia prediction server. As shown in the interior design diagram 10 of FIG. 3A and the interior design diagram 10-2 of FIG. 3B , a power meter is attached to the electronic product, and motion sensors, audio sensors, and contact sensors are installed throughout the room. can When the electronic product operates, the power meter may measure the power usage of the electronic product and transmit information on the power usage to the dementia prediction server. The motion sensor and the touch sensor may transmit a detected signal to the dementia prediction server when a user's movement is detected, such as moving from the living room to the bathroom or from the bathroom to the living room. The audio sensor may transmit information about the user's voice and sound generated by the user's movement to the dementia prediction server. In this case, the wearable device may transmit the user's location information and the user's bio-signals to the dementia prediction server.

FIG. 3A is a design diagram 10-1 of an interior showing sensors disposed according to the first rule in an interior where a normal user is located. In an embodiment, the sensor may be disposed according to a first rule defining the arrangement of the sensor for detecting the motion of the user of the normal group, and data may be collected from the disposed sensor.

In the room where normal users are located, data can be collected through vibration sensors, door sensors, motion sensors, temperature and humidity sensors, smart plugs, and lidar sensors. Vibration sensors are placed in medicine boxes, refrigerators, rice cookers, sinks and toilets. The door sensor may be disposed on the front door, microwave oven, and wardrobe, and the motion sensor may be disposed on shoe cabinets, TVs, chests of drawers, and desks. A temperature and humidity sensor is placed in the bathroom and gas stove, a smart plug is placed in a TV and a bed, and a lidar sensor can be placed on a wall sofa.

The arrangement of the sensors according to the first rule described with reference to FIG. 3A may indicate a position at which the ADL collection items of the users of the normal group can be most effectively collected. Specifically, the arrangement of the sensor according to the first rule may be the optimal arrangement for observing the users of the normal group acquired through the experimental results while going through the experimental process of comparing the motions of the users of the dementia patient and the normal group. have. Accordingly, the arrangement of the sensors according to the first rule may be an arrangement method set so that the ADL collection items of the normal users can be maximally collected using the minimum number of sensors.

FIG. 3B is a design diagram 10-2 of an interior showing sensors disposed according to the second rule in an interior where a user of the dementia group is located. In an embodiment, the sensor may be disposed according to the second rule defining the arrangement of the sensor for detecting motion for the user of the dementia group, and data may be collected from the disposed sensor.

Data can be collected through vibration sensors, door sensors, motion sensors, temperature and humidity sensors, smart plugs, and lidar sensors in the room where the user of the dementia group is located.

Vibration sensors may be disposed in refrigerators, sinks, rice cookers, medicine cabinets, toilets, vacuum cleaners and trash cans, door sensors may be disposed in front doors and microwave ovens, and motion sensors may be disposed in balcony windows and TVs. Temperature and humidity sensors are placed in toilets and gas ranges, smart plugs are placed in washing machines, electric blankets, and TVs, and lidar sensors are placed on the wall sofa to collect data on the user's location and movement in the room.

The arrangement of the sensor according to the second rule described with reference to FIG. 3B may indicate a position where the ADL collection item of the user of the dementia group can be most effectively collected. Specifically, the arrangement of the sensors according to the second rule may be the optimal arrangement for observing the users of the dementia group, which were acquired through the experimental results while going through the experimental process of comparing the motions of the users of the dementia patient and the normal group. have. Accordingly, the arrangement of the sensors according to the second rule may be an arrangement method set to maximally collect the ADL collection items of the users of the dementia group using the minimum number of sensors.

The dementia prediction method based on daily living ability information according to an embodiment of the present invention monitors the daily life of a dementia patient using the data collected through the arrangement of the sensors described in FIGS. 3A and 3B and predicts dementia at an early stage. Accordingly, in the field of dementia, where early prediction is more important than treatment, there is an advantage in that it is possible to predict dementia patients early with minimum cost and minimum time.

In addition, since the dementia prediction method based on daily living ability information using the arrangement of sensors according to the first and second rules has superior performance in predicting dementia at an early stage compared to the results predicted by the arrangement of other sensors, The method of predicting dementia according to the sensor positions of the first rule and the second rule may be considered as a technology that is likely to be studied in depth in the field of dementia technology.

Referring to FIG. 4 , the dementia prediction server may classify daily living ability information according to a preset daily life collection item 30 . In another embodiment, the dementia prediction server can machine-learning information received from IoT devices and wearable devices to cluster the user's daily actions into predetermined items, and according to the clustered items, daily life collection items 30 can be set. Activity of Daily Living (ADL) includes using the phone, preparing food, cooking, using home appliances, doing housework, locking the door, grooming, managing belongings, taking medicine, talking about the latest, managing money, It can be classified into items such as toilet use, indoor movement and walking, transportation use and wandering.

Among the daily life ability information, 'Keeping appointments and meetings', 'Leisure and hobbies', 'Going out for a short distance', 'Shopping', 'Using public transportation', etc. can be located based on GPS, but Because there is a limit to collecting information on daily life ability such as 'confirming whether or not activities have been performed properly' and 'talking about recent days', 'managing belongings' and 'money management' with IOT equipment. It may be collected through parental interviews or observer confirmation.

In particular, using the data collected in FIGS. 3A and 3B, the user's cooking behavior, cleaning behavior, door locking behavior, household appliance use behavior, housework behavior, grooming behavior, taking medicine, and indoor movement behavior of the user are changed. can be decided.

For example, a cooking operation may use a sensor disposed on a microwave oven, a refrigerator, a rice cooker, a sink faucet, and a gas stove, and a cleaning operation may use a sink faucet (washing dishes), a sensor disposed in a washing machine, and door lock The following operation may use a sensor disposed on a front door, an electric blanket, and a TV.

The operation of using the home appliance may use the sensors disposed in the washing machine, the microwave oven, and the TV, and the operation of doing housework may use the sensors disposed in the washing machine, the vacuum cleaner, and the sink in the case of washing dishes. For grooming, a sensor placed in the bathroom can be used for showering, and a sensor placed in a wardrobe for dressing. For taking medicine, a sensor placed on the medicine cabinet can be used. It can be determined through the movement line in (living room).

As described above, a specific method of acquiring daily living ability information by monitoring a user's indoor behavior has been described with reference to FIGS. 3 and 4, and step S200 will be described with reference to FIG. 2 again.

In step S200, a life pattern for the user's behavior may be determined using the daily living ability information. The dementia prediction server may determine the user's life pattern according to the daily life collection items. The dementia prediction server may determine the user's life pattern for phone use, food preparation life pattern, cooking life pattern, life pattern for household appliance use, and the like according to daily life collection items. For example, the dementia prediction server analyzes voice volume, word usage pattern, voice tone, phone usage time, data usage, movement direction, etc. can decide

Hereinafter, a detailed method of determining a user's life pattern will be described with reference to FIGS. 5 and 6 .

Referring to FIG. 5 , data according to the user's personal characteristics may be acquired in step S201. Specifically, data on the characteristics of the spatial structure in which the user lives, data on the physical characteristics of the user, and data on the degree of the user's dementia symptoms may be obtained. Data according to the personal characteristics may be input from a medical institution, a public institution, or a user terminal. In step S203, a life pattern for the user's behavior may be determined in consideration of the received data.

Referring to FIG. 6 , the dementia prediction server may receive data on the characteristics of the spatial structure in which the user lives, and determine the user's life pattern according to the characteristics of the spatial structure. The data on the characteristics of spatial structure can be divided into house structure, sleeping pattern, eating pattern, etc. Type 1 is 'a structure that uses a dining table without using a bed in a detached house', and Type 2 is 'a bed and a dining table in an apartment. ', and Type3 may be data of 'a structure that does not use beds and tables in rental APT'.

The dementia prediction server may receive data on the user's physical characteristics, and determine the user's life pattern according to the user's physical characteristics. Data on physical characteristics can be divided into gender, age, family status, etc. Type 1 is 'male, 70's living alone', Type 2 is 'female, 70's, couple', and Type 3 is 'male, 80's, It may be data of 'living alone'.

The dementia prediction server may receive data on the severity of the user's dementia symptoms, and determine the user's life pattern according to the severity of the dementia symptoms. Data on the degree of dementia symptoms can be divided into normal, MCI, AD, etc., Type1 may be 'normal', Type2 may be 'MCI', and Type3 may be 'AD' data.

In step S203, in consideration of the data on the characteristics of the spatial structure in which the user lives, the data on the physical characteristics of the user, and the data on the degree of the user's dementia symptoms, a life pattern reflecting the individual characteristics of the user can be determined. have.

In step S300 of FIG. 2 , when it is detected that the user's behavior deviates from the life pattern through the daily living ability information, it may be analyzed that the user is in a dementia risk state. For example, assuming that the phone use life pattern is less than 1 minute, and the same word is repeated less than 3 times in the same sentence, the user's phone usage time exceeds 1 hour and When the repetition of the same word exceeds 10 times or when it is detected that an inscription sentence is generated, the dementia prediction server may determine that the user is in a dementia risk state. In addition, the dementia prediction server may determine that the user is in a dementia risk state when the user's moving path continuously repeats the same path as will be described later with reference to FIG. 7 or less.

In one embodiment, the ADL difference between the normal user and the dementia user can be derived through the first-stage analysis (the difference between normal and dementia by ADL activity), the second-stage analysis (Dementia Index of ADL), and the third-stage (dementia prediction model) analysis. have.

In the first stage analysis (difference between normal and dementia by ADL activity), in the case of cooking, the total number of cooking times per period, gas stove temperature and humidity cycle, cooking time, and cooking movement can be analyzed. In the case of cleaning, it can be analyzed by checking the total number of cleanings per period, the opening/closing of the faucet, and the movement of the faucet. In the case of door enforcement, it can be analyzed through the opening/closing of the front door, the electric blanket, and the ON/OFF of the TV. In the case of home appliance use, it can be analyzed through the number of times used by each product, usage time, usage cycle, and movement. In the case of housework, it can be analyzed through the number of times of cleaning/washing, time, cycle, and movement. In the case of grooming, it can be analyzed through the shower-toilet temperature and humidity cycle, the number of doors to the closet and the time period. In the case of taking medicine, it can be analyzed through the number of times the medicine box is used and the duration of use. In the case of indoor movement, it can be analyzed through movement from living space to kitchen to room, movement distance, movement time, and location.

Step 2 analysis (Dementia Index of ADL) is performed after (A) calculating the average ADL activity score, (B) calculating the abnormal behavior score for each ADL activity (number of times of taking medicine and time of day, number of times of cleaning, number of showers, number of dishes, etc.), (C) ADL abnormal behavior score can be calculated by combining two or more sensors. For example, if the front door is closed & the lighting is low, but the TV or electric blanket is ON, the sink faucet is open but the location is not in the kitchen and stay stays in the kitchen. can After that, (D) indoor movement and abnormal patterns (analysis of patterns that differ from normal movement: staying in a different place for a very long time, frequently wandering, etc.) are analyzed, and long-term ADL changes can be analyzed and verified.

Step 3 (dementia prediction model) collects ADL data, calculates the average (A) and ADL dementia score for each ADL (B, C, R, E), and classification based on dementia analysis rules and machine learning-based classification (analysis) model selection and training), and this can be used to predict dementia early.

So far, the overall process of the dementia prediction method based on daily living ability information according to an embodiment of the present invention has been described. Hereinafter, a specific method of predicting dementia by monitoring the user's wandering among the daily life collection items of FIG. 4 will be described with reference to FIGS. 7 and 8 .

FIG. 7 is a view for explaining step S300 of FIG. 2 in detail, and FIG. 8 is a view for explaining a method of analyzing a user's roaming according to a user's moving path.

Referring to FIG. 7 , the user's actions in daily life according to the daily life collection items classified in advance in step S301 may be detected. In this case, whether the user wanders according to movement may be monitored. The user's wandering may be analyzed using the information on the movement path collected by the wearable device in step S302.

In step S303, a method of determining the user's roaming may be different depending on whether the user is located indoors or outdoors. If it is determined in step S303 that the user is located indoors, steps S304 to S306 may be performed, and if it is determined that the user is located outdoors, steps S307 to S309 may be performed.

When it is determined in step S303 that the user is located indoors, the use information of the building may be checked in step S304. The use information of the building may include information on what purpose the building is used, such as a school, an institution, a company, an entertainment establishment, a restaurant, and the like. In step S305, if it is determined that the building is a place that deviates from the user's life pattern by analyzing whether the building information according to the use information of the building is related to the user's life pattern, step S306 may be performed. In this case, if it is determined that the user stays longer than the threshold time, it may be determined that the user wanders in step S310. If the user leaves the building without staying longer than the threshold time, the process returns to step S302 and the user's behavior may be monitored. If in step S305 the building is not a place out of the user's life pattern, step S307 may be performed, and the operation of step S307 will be described later.

If it is determined in step S303 that the user is not located indoors, it may be determined that the user stays outdoors, and then in step S307 it may be determined whether the user's location is a place frequently visited by the user. In the case of a frequently visited place, the user's biosignal may be analyzed if it is a pre-specified exceptional place in step S308. If it is determined in step S309 that the cognitive ability of the user is lowered on the biosignal, it may be determined that the user is wandering in step S310. If it is determined in step S309 that the cognitive ability of the user is not lowered in the biosignal, it is determined that the user is not wandering, and the process returns to step S302 and the user's behavior may be monitored.

Referring to FIG. 8 , the dementia prediction server may receive GPS data 70 about the user's moving path from the wearable device. The dementia prediction server may determine whether the user is located in a building through this path.

In this case, the dementia prediction server may generate a wandering detection result 80 classified into a normal user's physical state, a wandering state, and a wandering exception. The dementia prediction server may receive bio-signals such as the user's heart rate and walking speed, and the dementia prediction server may determine whether the user's physical condition is in a normal pattern and generate the data 80 of the wandering detection result. In one embodiment, the dementia prediction server may determine whether the user is wandering by determining whether the user is repeatedly moving without a purpose for the visited place based on the user's heart rate, walking speed, and information on the visited place. .

The dementia prediction method based on daily living ability information according to an embodiment of the present invention not only determines the wandering by analyzing the user's movement path, but also collects the usage information of the building to analyze the user's wandering. It has the advantage of being able to predict accurately.

9 is a schematic diagram of a dementia prediction system based on daily living ability information according to another embodiment of the present invention.

Referring to FIG. 9 , the dementia prediction system based on daily living ability information according to another embodiment of the present invention includes a dementia prediction server 100 , a wearable device 200 , an IoT device 300 , a service DB 400 , and early dementia. It may include a diagnostic web 500 and a sensor management web 600 . The dementia prediction system based on daily living ability information according to another embodiment of the present invention is mostly the same as the configuration described in FIG. 1 , but the dementia prediction server 100 is specifically implemented, and the service DB 400 and early dementia diagnosis 500 This is an embodiment in which the web and sensor management web 600 is added. Since the operations of the wearable device 200 and the IoT device 300 in this embodiment are the same as in FIG. 1 , a description thereof will be omitted.

The dementia prediction server 100 may be implemented as an IoT open platform, Mobius Server. Dementia prediction server 100 may perform Data storage Access, Requester, and Responder, and may be implemented as an MQTT proxy server using a protocol of MQTT, COAP, and HTTP, and using a platform of Node Js. Mobius Server can process data in connection with Filesystem, Mobius DB, and HDFS. The dementia prediction server 100 may store and manage information received from the wearable device 200 and the IoT device 300 in the service DB 400 . The dementia early diagnosis web 500 may manage the user's dementia risk through the web using information stored in the service DB 400 , and the sensor management web 600 may separately collect and manage only sensor information.

Hereinafter, an exemplary computing device 500 capable of implementing the devices described in various embodiments of the present invention will be described with reference to FIG. 10 .

10 is an exemplary hardware configuration diagram illustrating the computing device 500 .

As shown in FIG. 10 , the computing device 500 loads one or more processors 510 , a bus 550 , a communication interface 570 , and a computer program 591 executed by the processor 510 . It may include a memory 530 and a storage 590 for storing the computer program (591). However, only the components related to the embodiment of the present invention are illustrated in FIG. 10 . Accordingly, a person skilled in the art to which the present invention pertains can know that other general-purpose components other than the components shown in FIG. 10 may be further included.

The processor 510 controls the overall operation of each component of the computing device 500 . The processor 510 includes at least one of a central processing unit (CPU), a micro processor unit (MPU), a micro controller unit (MCU), a graphic processing unit (GPU), or any type of processor well known in the art. may be included. In addition, the processor 510 may perform an operation on at least one application or program for executing the method/operation according to various embodiments of the present disclosure. Computing device 500 may include one or more processors.

The memory 530 stores various data, commands, and/or information. The memory 530 may load one or more programs 591 from the storage 590 to execute methods/operations according to various embodiments of the present disclosure. For example, when the computer program 591 is loaded into the memory 530 , logic (or a module) as shown in FIG. 4 may be implemented on the memory 530 . An example of the memory 530 may be a RAM, but is not limited thereto.

The bus 550 provides a communication function between components of the computing device 500 . The bus 550 may be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

The communication interface 570 supports wired/wireless Internet communication of the computing device 500 . The communication interface 570 may support various communication methods other than Internet communication. To this end, the communication interface 570 may be configured to include a communication module well-known in the art.

The storage 590 may non-temporarily store one or more computer programs 591 . The storage 590 is a non-volatile memory such as a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a flash memory, a hard disk, a removable disk, or well in the art to which the present invention pertains. It may be configured to include any known computer-readable recording medium.

The computer program 591 may include one or more instructions in which methods/operations according to various embodiments of the present invention are implemented. When the computer program 591 is loaded into the memory 530 , the processor 510 may execute the one or more instructions to perform methods/operations according to various embodiments of the present disclosure.

In one embodiment, it includes a processor, a network interface, a memory executed by the processor to load a computer program, and a storage for storing the computer program, wherein the computer program is installed indoors through an IoT device. An instruction for monitoring the indoor behavior of the user to obtain daily life ability information related to dementia prediction, an instruction for determining a life pattern for the user's behavior using the daily living ability information, and the user through the daily living ability information When it is detected that the behavior of the user deviates from the life pattern, an instruction for determining that the user is in a dementia risk state may be included.

The methods according to the embodiments of the present invention described so far may be performed by executing a computer program embodied as computer readable code. The computer program may be transmitted from the first computing device to the second computing device through a network such as the Internet and installed in the second computing device, thereby being used in the second computing device. The first computing device and the second computing device include all of a server device, a physical server belonging to a server pool for a cloud service, and a stationary computing device such as a desktop PC.

The computer program may be stored in a recording medium such as a DVD-ROM or a flash memory device.

Although embodiments of the present invention have been described above with reference to the accompanying drawings, those of ordinary skill in the art to which the present invention pertains can realize that the present invention can be embodied in other specific forms without changing its technical spirit or essential features. can understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

Claims (20)

1. A method performed by a computing device, comprising:

   Obtaining daily living ability information related to dementia prediction by monitoring the indoor behavior of the user through the IoT device installed indoors;

   determining a life pattern for the user's behavior by using the daily living ability information; and

   Comprising the step of determining that the user is in a dementia risk state when the behavior of the user is detected to deviate from the life pattern through the daily living ability information obtained after determining the life pattern,

   Dementia prediction method based on daily living ability information.
2. According to claim 1,

   The step of obtaining the daily life ability information includes:

   Using data obtained from an IoT device installed indoors, detecting an operation in the user's daily life according to pre-classified daily life collection items,

   Dementia prediction method based on daily living ability information.
3. 3. The method of claim 2,

   The step of detecting the user's motion in daily life comprises:

   Monitoring the user's use of the electronic product by detecting the amount of power of the electronic product through a power meter installed indoors, detecting the user's movement through a motion sensor, and detecting the user's contact through a vibration sensor doing,

   Dementia prediction method based on daily living ability information.
4. 3. The method of claim 2,

   Further comprising the step of analyzing the user's indoor wandering according to the user's movement information,

   Dementia prediction method based on daily living ability information.
5. 5. The method of claim 4,

   The step of analyzing the user's roaming indoors according to the user's movement information,

   Further comprising the step of detecting whether the user is wandering by determining whether the user is moving repeatedly without purpose based on the information on the user's heart rate and walking speed,

   Dementia prediction method based on daily living ability information.
6. 3. The method of claim 2,

   The step of detecting the user's motion in daily life comprises:

   Comprising the step of collecting data according to a first rule that defines the arrangement of the sensor for detecting the motion of the user of the normal group,

   Dementia prediction method based on daily living ability information.
7. 7. The method of claim 6,

   The step of collecting data according to the first rule comprises:

   Comprising the step of collecting data through a vibration sensor, a door sensor, a motion sensor, a temperature and humidity sensor, a smart plug and a lidar sensor in the room where the user of the normal group is located,

   Dementia prediction method based on daily living ability information.
8. 8. The method of claim 7,

   The step of collecting data according to the first rule comprises:

   Comprising the step of disposing the vibration sensor in a medicine box, a refrigerator, a rice cooker, a sink and a toilet,

   Dementia prediction method based on daily living ability information.
9. 8. The method of claim 7,

   The step of collecting data according to the first rule comprises:

   Including the step of arranging the door sensor in a front door, a microwave oven and a wardrobe,

   Dementia prediction method based on daily living ability information.
10. 8. The method of claim 7,

    The step of collecting data according to the first rule comprises:

    Including the step of placing the motion sensor on a shoe cabinet, TV, chest of drawers and desk,

    Dementia prediction method based on daily living ability information.
11. 8. The method of claim 7,

    The step of collecting data according to the first rule comprises:

    Including the step of disposing the temperature and humidity sensor in the bathroom and gas stove,

    Dementia prediction method based on daily living ability information.
12. 8. The method of claim 7,

    The step of collecting data according to the first rule comprises:

    Comprising the step of placing the smart plug in a TV, a washing machine, an electric blanket and a fan,

    Dementia prediction method based on daily living ability information.
13. 8. The method of claim 7,

    The step of collecting data according to the first rule comprises:

    The lidar sensor is disposed adjacent to the sofa, comprising the step of collecting data of the user's location and movement in the room,

    Dementia prediction method based on daily living ability information.
14. 3. The method of claim 2,

    The step of detecting the user's motion in daily life comprises:

    Comprising the step of collecting data according to a second rule defining the arrangement of a sensor for detecting motion for a user of the dementia group,

    Dementia prediction method based on daily living ability information.
15. 15. The method of claim 14,

    The step of collecting data according to the second rule comprises:

    Comprising the step of collecting data through a vibration sensor, a door sensor, a motion sensor, a temperature and humidity sensor, a smart plug and a lidar sensor in the room where the user of the dementia group is located,

    Dementia prediction method based on daily living ability information.
16. According to claim 1,

    The step of determining the life pattern,

    receiving data on the characteristics of the spatial structure in which the user lives, and determining the user's life pattern according to the characteristics of the spatial structure;

    Dementia prediction method based on daily living ability information.
17. processor;

    network interface;

    a memory executed by the processor to load a computer program; and

    A storage for storing the computer program,

    The computer program is

    instructions for monitoring a user's indoor behavior through an indoor IoT device to obtain daily life ability information related to dementia prediction;

    instructions for determining a life pattern for the user's behavior by using the daily living ability information; and

    Including instructions for determining that the user is in a dementia risk state when it is detected that the user's behavior deviates from the life pattern through the daily living ability information,

    Dementia prediction device based on daily living ability information.
18. 18. The method of claim 17,

    The instruction for obtaining the daily life ability information is,

    Using data obtained from an IoT device installed indoors, comprising an instruction for detecting an operation in the user's daily life according to a pre-classified daily life collection item,

    Dementia prediction device based on daily living ability information.
19. 19. The method of claim 18,

    The instruction for detecting the user's daily life,

    A user's use of the electronic product is monitored by detecting the amount of power of the electronic product through a power meter installed indoors, the user's movement is detected through a motion sensor, and a sound generated by the user's operation using an audio sensor , and including instructions for detecting the user's touch by a touch sensor,

    Dementia prediction device based on daily living ability information.
20. 19. The method of claim 18,

    The instruction for detecting the user's daily life,

    Including instructions for analyzing the user's indoor wandering according to the user's movement information,

    Dementia prediction device based on daily living ability information.

Images