Information processing device, information processing method and information processing program

Abstract

To provide an information processing device, an information processing method and an information processing program which can arrange a staff, in accordance with occurrence of a dementia state of a dementia patient.SOLUTION: An information processing device includes: a second acquisition unit 213 which acquires occurrence information related to occurrence prediction of a dementia state of an object dementia patient during a prediction period after a predetermined time, at the predetermined time; and a generation unit 214 which generates arrangement information about arrangement of a staff in charge required for care of the object dementia patient during the prediction period, on the basis of the acquired occurrence information.SELECTED DRAWING: Figure 12

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and an information processing program.

Background Art

[0002] In Japan, due to the improvement of living standards, the improvement of sanitary environment, and the improvement of medical standards accompanying the high economic growth after the war, the aging population has become remarkable. Therefore, combined with the decline in the birth rate, it has become an aging society with a high aging rate. Dementia is one of the problems faced by the aging society (for example, Patent Document 1). Dementia patients enter a nursing care facility or use home visiting nursing services, etc.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In nursing care facilities and home visiting nursing services, etc., when dementia symptoms appear in dementia patients, it is desirable that the staff can appropriately care for the dementia patients. Therefore, it is desirable to arrange staff in accordance with the occurrence of dementia symptoms in dementia patients.

[0005] The present invention has been made in view of such a situation. That is, an object of the present invention is to provide an information processing apparatus, an information processing method, and an information processing program capable of arranging staff in accordance with the occurrence of dementia symptoms in dementia patients.

Means for Solving the Problems

[0006] The above problems of the present invention are solved by the following means.

[0007] (1) An information processing apparatus including: an acquisition unit that acquires, at a predetermined time, occurrence information regarding prediction of occurrence of dementia symptoms of a target dementia patient in a prediction period after the predetermined time; and a generation unit that generates arrangement information regarding arrangement of staff members required for care of the target dementia patient in the prediction period based on the acquired occurrence information.

[0008] (2) The information processing apparatus according to (1) above, wherein the occurrence information includes at least any one of information regarding presence or absence of occurrence of the dementia symptoms, information regarding date and time of occurrence of the dementia symptoms, and information regarding types of the dementia symptoms that occur.

[0009] (3) The information processing apparatus according to (1) above, wherein the arrangement information includes at least information regarding care date and time when the staff members are required and information regarding the number of the staff members at the care date and time.

[0010] (4) The acquisition unit further acquires staff information regarding each of a plurality of staff members who care for dementia patients and target person information regarding the target dementia patient, and the generation unit generates the arrangement information based on the occurrence information, the staff information, and the target person information.

[0011] (5) The information processing apparatus according to (4) above, wherein the generation unit determines care date and time when the staff members are required, and selects one or a plurality of the staff members from among the plurality of staff members in association with the care date and time, thereby generating the arrangement information.

[0012] (6) The information processing apparatus according to (5) above, further including an output unit that outputs the generated arrangement information.

[0013] (7) The staff information includes identification information of each of the plurality of staff members, and the output unit outputs the arrangement information by associating the identification information of the staff members with the target dementia patient.

[0014] The information processing apparatus according to (1) above, further comprising a prediction unit that predicts the occurrence information.

[0015] (9) The information processing apparatus according to (8) above, wherein the prediction unit predicts the occurrence information based on a state index related to the living state of the target dementia patient and an environmental index related to the living environment of the target dementia patient.

[0016] (10) The information processing apparatus according to (9) above, wherein the state index includes at least one of an index representing the movement of the target dementia patient and an index representing the sleep state of the target dementia patient.

[0017] (11) The information processing apparatus according to (9) above, wherein the environmental index includes at least one of an index representing the temperature of the living environment, an index representing the atmospheric pressure of the living environment, and an index representing the humidity of the living environment.

[0018] (12) The information processing apparatus according to (8) above, wherein the prediction unit predicts the occurrence information using a machine learning model.

[0019] (13) An information processing method including: obtaining occurrence information related to the prediction of the occurrence of dementia symptoms of a target dementia patient in a prediction period after a predetermined time at the predetermined time; and generating arrangement information related to the arrangement of staff members required for the care of the target dementia patient in the prediction period based on the obtained occurrence information.

[0020] (14) An information processing program for causing a computer to execute a process including: obtaining occurrence information related to the prediction of the occurrence of dementia symptoms of a target dementia patient in a prediction period after a predetermined time at the predetermined time; and generating arrangement information related to the arrangement of staff members required for the care of the target dementia patient in the prediction period based on the obtained occurrence information.

Advantages of the Invention

[0021] In the information processing apparatus, information processing method, and information processing program according to the present invention, occurrence information regarding the prediction of dementia symptoms of a target dementia patient is acquired, and based on the acquired occurrence information, arrangement information regarding the arrangement of staff is generated. Therefore, it becomes possible to arrange staff in accordance with the occurrence of dementia symptoms in the dementia patient.

Brief Description of the Drawings

[0022]

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Mode for Carrying Out the Invention

[0023] Hereinafter, embodiments of the present invention will be described with reference to the attached drawings. In the description of the drawings, the same elements are denoted by the same reference numerals, and duplicate descriptions are omitted. Also, the dimensional ratios in the drawings are exaggerated for the convenience of explanation and may be different from the actual ratios.

[0024] <Embodiment> [Configuration of Monitoring System 1] (Overall Configuration) FIG. 1 is a diagram showing the overall configuration of the monitoring system 1 according to an embodiment, and FIG. 2 is a diagram showing an example of the detection unit 10 installed in the room of the target person 70.

[0025] As shown in FIG. 1, the monitoring system 1 includes a plurality of detection units 10, a server 20, a manager terminal 30, and one or more staff terminals 40. These are communicably connected to each other via a network 50 such as a LAN (Local Area Network), a telephone network, or a data communication network by wire or wirelessly. The network 50 may include a relay device such as a repeater, a bridge, a router, or a cross-connect that relays communication signals. In the example shown in FIG. 1, the staff terminal 40 is communicably connected to the detection unit 10, the server 20, and the manager terminal 30 by a network 50 such as a wireless LAN including an access point 51 (for example, a LAN conforming to the IEEE802.11 standard).

[0026] The monitoring system 1 is arranged at an appropriate location according to the target person 70. The target person 70 is, for example, a patient who requires nursing due to illness, injury, etc., a care recipient who requires care due to a decline in physical ability due to old age, a single person living alone, or a patient hospitalized in a hospital facility. In particular, from the perspective of enabling early detection and early response, the target person 70 can be a person who requires the discovery when a predetermined unfavorable event such as an abnormal state occurs to that person. In this embodiment, the target person 70 is a target dementia patient. The monitoring system 1 is preferably arranged in buildings such as elderly welfare facilities, hospitals, and households. In the example shown in FIG. 1, the monitoring system 1 is arranged in the home of the target person 70.

[0027] The detection unit 10 is arranged, for example, in a room such as a bedroom which is the observation area of the target person 70. In the example shown in FIG. 1, four detection units 10 are respectively arranged in the homes of Mr. A, Mr. B, Mr. C, and Mr. D who are the target persons 70. The observation area of the detection unit 10 includes the bed 60. The staff 80 who performs corresponding actions such as care or nursing for the target person 70 each carry a staff terminal 40 which is a mobile terminal. Hereinafter, the corresponding actions such as care or nursing for the target person 70 by the staff 80 may be referred to as care. The position, number, etc. of each component included in the monitoring system 1 are not limited to the example shown in FIG. 1.

[0028] (Detection unit 10) FIG. 3 is a block diagram showing the hardware configuration of the detection unit 10. As shown in FIG. 3, the detection unit 10 has a control unit 11, a communication unit 12, a camera 13, a care call unit 14, and an audio input / output unit 15, and these are interconnected by a bus.

[0029] The control unit 11 is composed of a CPU (Central Processing Unit) and memories such as a RAM (Random Access Memory) and a ROM (Read Only Memory), and performs control and arithmetic processing of each part of the detection unit 10 according to a program. Note that the control unit 11 may further include an HDD (Hard Disk Drive) as a memory.

[0030] The communication unit 12 is an interface circuit (such as a LAN card) for communicating with other devices, such as the server 20, the administrator terminal 30, or the staff terminal 40, via the network 50.

[0031] The camera 13 is disposed, for example, on the ceiling of the bedroom or the upper part of the wall, and captures an area including the bed 60 of the subject 70 as an observation area, and outputs a captured image (image data). Hereinafter, the image captured by the camera 13 is also simply referred to as a "captured image". The captured image includes an image including the subject 70. The captured image includes still images and moving images. The camera 13 is a near-infrared camera, but a visible light camera may be used instead, or these may be used in combination.

[0032] The control unit 11 recognizes the actions of the subject 70 from the captured images captured by the camera 13. The actions to be recognized include "getting up" from the bed 60, "getting out of bed" from the bed 60, "falling" from the bed 60, and "falling" to the floor or the like.

[0033] The control unit 11 detects a silhouette of an image (hereinafter referred to as a "human silhouette") from a plurality of captured images (moving images). The human silhouette can be detected, for example, by extracting a range of pixels with a relatively large difference by a time difference method of extracting the difference between images around the shooting time. The human silhouette may be detected by a background difference method of extracting the difference between the captured image and the background image. Getting up, getting out of bed, falling, and toppling are recognized from the detected human silhouette based on the posture of the subject 70 (such as standing, sitting, and lying down) and the relative position with respect to the furnishings in the bedroom such as the bed 60. These recognitions may be performed by a program processed by the CPU of the control unit 11, or may be performed by an embedded processing circuit. Also, without being limited to this, all or most of these recognition processes may be performed on the server 20 side, and the control unit 11 may only transmit the captured images to the server 20. When the control unit 11 recognizes any action, it transmits a notification indicating that an event has occurred to the server 20 or the like.

[0034] The care call unit 14 includes a push-button switch, and detects a care call when the switch is pressed by the target person 70. A care call is also called a nurse call. Instead of the push-button switch, a voice microphone may be used to detect the care call. When the switch of the care call unit 14 is pressed, that is, when a care call is detected, the control unit 11 transmits a notification (care call notification) indicating that there is a care call to the server 20 or the like via the communication unit 12 and the network 50.

[0035] The voice input / output unit 15 is, for example, a speaker and a microphone, and enables a voice call by transmitting and receiving voice signals to and from a staff terminal 40 or the like via the communication unit 12. Note that the voice input / output unit 15 may be connected to the detection unit 10 via the communication unit 12 as an external device of the detection unit 10.

[0036] Further, the detection unit 10 may further include a Doppler shift type body movement sensor that transmits and receives microwaves toward the direction of the bed 60 and detects a Doppler shift of the microwaves generated by the body movement (for example, breathing movement) of the target person 70. With this body movement sensor, when detecting the body movement (up and down movement of the chest) of the chest accompanying the breathing movement of the target person 70 and detecting a disturbance in the period or an amplitude of the body movement of the chest below a preset threshold value in the body movement of the chest, it is recognized as a minute body movement abnormality.

[0037] The detection unit 10 may further include a sensor that detects the living environment of the target person 70, such as the temperature, atmospheric pressure, and humidity in the home of the target person 70. The detection unit 10 may include a sensor that detects the temperature, atmospheric pressure, and humidity inside the home other than the bedroom of the target person 70, or may include a sensor that detects the temperature, atmospheric pressure, and humidity outside the home.

[0038] The staff 80 is a person who performs various responses to the target person 70 according to the work. The work may include medical work or care work. The staff 80 belongs to, for example, a company that provides home care services.

[0039] The detection unit 10 transmits (outputs) information on detected events such as getting up, getting out of bed, and falling (hereinafter, also simply referred to as "event information"), Doppler shift, living environment, and photographed images, etc. to the server 20.

[0040] (Server 20) FIG. 4 is a block diagram showing the hardware configuration of the server 20.

[0041] The server 20 includes a control unit 21, a communication unit 22, and a storage unit 23. Here, the server 20 corresponds to a specific example of the information processing apparatus of the present invention. The server 20 predicts, for example, the presence or absence of the occurrence of dementia symptoms of the subject 70 in a prediction period after the data collection period, using information such as Doppler shift, living environment, and photographed images detected by the detection unit 10 during the data collection period. The prediction period is, for example, about one day from the time the subject gets up on the current day to the time the subject gets up on the next day, and the data collection period is, for example, a period of about one week retroactively from the time the subject gets up on the current day. The dementia symptoms are so-called BPSD (Behavioral and Psychological Symptoms of Dementia).

[0042] The server 20 may be provided in the same building as the administrator terminal 30, or may be provided at a remote location and connected via a network. For example, the server 20 may be a cloud server virtually constructed by a plurality of servers arranged on a network such as the Internet. Each component is communicably connected to each other by a bus. Since the control unit 21 and the communication unit 22 have the same functions as the control unit 11 and the communication unit 22 of the detection unit 10, respectively, detailed descriptions thereof are omitted. The specific functions of the control unit 21 will be described later.

[0043] The storage unit 23 stores the information processing program according to the present embodiment. The above components of the server 20 are controlled by the control unit 21 according to the program. The storage unit 23 stores various information such as an event list, photographed images, the Doppler shift of the target person 70, the living environment of the target person 70, target person information, a care list, staff information, and index information.

[0044] FIG. 5 shows an example of target person information. The target person information is information regarding each of the plurality of target persons 70. The target person information includes, for example, identification information such as the name and ID of each target person 70, gender, age, place of residence, family members, health status, disease history, care need certification level, presence or absence of dementia, and if present, the dementia level, presence or absence of wheelchair use, and information regarding precautions. The dementia level is, for example, the result of the MMSE (Mini-Mental State Examination) or the like.

[0045] The care list includes care histories such as the food intake amount, water intake amount, and excretion status regarding each target person 70. The care list also includes the care history when dementia symptoms occur in each target person 70.

[0046] FIG. 6 shows an example of staff information. The staff information is information regarding each of the plurality of staff members 80 who care for the target person 70. The staff information includes, for example, identification information such as the name and ID of each staff member 80, gender, years of experience in caregiving or nursing, place of residence, and work schedule. The staff information may also include information regarding skills such as the presence or absence of qualifications related to caregiving or nursing.

[0047] The index information is information including at least one of a state index and an environmental index of the target person 70. The state index is an index regarding the living state of the target person 70 during the data collection period. The environmental index is an index regarding the living environment of the target person 70 during the data collection period. The index information preferably includes the state index and the environmental index. Thereby, it becomes possible to predict the presence or absence of the occurrence of dementia symptoms of the target person 70 with higher accuracy.

[0048] Figures 7 and 8 show an example of a state indicator, and Figure 9 shows an example of an environmental indicator. The state indicator is, for example, an indicator related to ADL (Activities of Daily Living), and preferably includes at least one of an indicator representing the movement of the subject 70 and an indicator representing the sleep state of the subject 70. This is because the movement and sleep state of the subject 70 are considered to be highly related to the occurrence of dementia symptoms in the subject 70.

[0049] The movement of the subject 70 includes, for example, at least one of movement, stop, repetitive behavior, and wobbling of the subject 70.

[0050] For example, the indicators representing the movement of the subject 70 are Indicator 1 to Indicator 3 of the daytime movement range, Indicator 4 to Indicator 6 of the nighttime movement range, Indicator 15 of the overhanging area, Indicator 16 of the movement speed, and Indicator 21 to Indicator 24 of the movement time and movement distance. Indicators 1 to 6 are calculated based on the area of the visit area of the subject 70 outside the bed 60. Indicator 15 is calculated based on the overhanging area from the high-frequency passage route outside the bed 60 at night. Indicator 16 is calculated based on the movement speed of the subject 70 outside the bed 60 at night. Indicators 21 to 24 are calculated based on the staying time and movement distance of the subject 70 outside the bed 60.

[0051] For example, the indicators representing the stop of the subject 70 are Indicator 17 to Indicator 20 of the stop. These Indicators 17 to 20 are calculated based on the time and number of times the subject 70 stops outside the daily activities outside the bed 60.

[0052] For example, the indicators representing the repetitive behavior of the subject 70 are Indicator 7 to Indicator 10 of the daytime repetitive behavior and Indicator 11 to Indicator 14 of the nighttime repetitive behavior. Indicators 7 to 14 are calculated based on the number and time of the repetitive behavior of the subject 70.

[0053] For example, the indicator representing the wobbling of the subject 70 is Indicator 25 of the wobbling. Indicator 25 is calculated based on the wobbling degree of the trajectory of the subject 70 outside the bed 60 at night.

[0054] The sleep state of the subject 70 includes, for example, at least any one of the sleep time, sleep rate, and sleep stability of the subject 70. For example, the indicators representing the sleep time and sleep rate of the subject 70 are indicators 26 to 30 of the night sleep time·sleep rate. Indicators 26 to 30 are calculated based on the night sleep time and night awakening time of the subject 70. For example, the indicators representing the sleep stability of the subject 70 are indicators 31 and 32 of the night sleep stability. Indicators 31 and 32 are calculated based on the sleep pattern stability of the subject 70 from 0:00 am to 5:00 am.

[0055] The environmental indicators include at least any one of the indicator representing the temperature of the living environment of the subject 70, the indicator representing the atmospheric pressure of the living environment of the subject 70, and the indicator representing the humidity of the living environment of the subject 70. This temperature, atmospheric pressure, and humidity are, for example, the temperature, atmospheric pressure, and humidity in the bedroom of the subject 70. For example, the indicator representing the temperature of the living environment of the subject 70 is indicator 35, the indicator representing the atmospheric pressure of the living environment of the subject 70 is indicator 33, and the indicator representing the humidity of the living environment of the subject 70 is indicator 34.

[0056] Indicators 1 to 35 are calculated, for example, based on the information detected by the detection unit 10. For example, indicators 1 to 35 calculated by the detection unit 10 are transmitted to the server 20 and stored in the storage unit 23. Alternatively, based on the information transmitted from the detection unit 10, the server 20 may calculate indicators 1 to 35 and store them in the storage unit 23. The detection unit 10 may calculate a part of indicators 1 to 35, and the server 20 may calculate the other part. Alternatively, based on the information transmitted from the detection unit 10, an external device may calculate a part or all of indicators 1 to 35, and indicators 1 to 35 transmitted from the external device to the server 20 may be stored in the storage unit 23.

[0057] The storage unit 23 further stores a machine learning model learned by machine learning. In the present embodiment, the server 20 uses this machine learning model to predict the presence or absence of dementia symptoms in the subject 70. The storage unit 23 may store learning data used for learning.

[0058] Each of the above-mentioned information may be stored outside the storage unit 23 of the server 20, for example, in various storages such as a cloud server connected to the server 20 via a network. Also, each of the above-mentioned information is not limited to the form of being constructed as a single database, and may be stored in a distributed manner in a plurality of databases associated with each other.

[0059] The server 20 determines (identifies) which subject 70 the events such as getting up, getting out of bed, falling, and nurse call recognized by the detection unit 10 are related to, by receiving the event information and the captured image from the detection unit 10 alone or in cooperation with the detection unit 10. This determination is made, for example, by determining the subject 70 associated with the position information of the home or the like where the detection unit 10 that recognized the event is installed.

[0060] When the server 20 receives event information from the detection unit 10, it notifies the staff 80 of the occurrence of the event and instructs the response to the event by transmitting an event notification including the name, home address, and content of the event of the subject 70 who caused the event to the staff terminal 40.

[0061] (Administrator terminal 30) FIG. 10 is a block diagram showing the hardware configuration of the administrator terminal 30. The administrator terminal 30 is a so-called PC (Personal Computer), and has a control unit 31, a communication unit 32, a display unit 33, and an input unit 34, which are interconnected by a bus.

[0062] The control unit 31 has a configuration similar to that of the control unit 11 of the detection unit 10, and includes a CPU, a RAM, a ROM, and the like.

[0063] The communication unit 32 is an interface for various local connections such as a network interface for wired communication according to standards such as Ethernet (registered trademark), and an interface for wireless communication according to standards such as Bluetooth (registered trademark) and IEEE802.11, and communicates with each terminal connected to the network 50.

[0064] The display unit 33 is, for example, a liquid crystal display, and displays various information.

[0065] The input unit 34 includes a keyboard, a numeric keypad, a mouse, etc., and inputs various information.

[0066] In the present embodiment, the administrator terminal 30 is used as a terminal for the administrator 90. The administrator 90 is, for example, a manager who supervises the staff 80.

[0067] The administrator terminal 30 receives instructions regarding the analysis and output of care information from the administrator 90, etc., and transmits the received instructions to the server 20.

[0068] (Staff terminal 40) FIG. 11 is a block diagram showing the hardware configuration of the staff terminal 40. The staff terminal 40 has a control unit 41, a wireless communication unit 42, a display unit 43, an input unit 44, and an audio input / output unit 45, and these are interconnected by a bus.

[0069] The control unit 41 has the same configuration as the control unit 11 of the detection unit 10, and includes a CPU, a RAM, a ROM, etc.

[0070] The wireless communication unit 42 is capable of wireless communication using standards such as Wi-Fi and Bluetooth (registered trademark), and wirelessly communicates with each device via the access point 51 or directly.

[0071] The display unit 43 and the input unit 44 are, for example, a touch panel type display, and a touch sensor as the input unit 44 is superimposed on the display surface of the display unit 43 composed of liquid crystal or the like. Various instructions for the staff 80 are notified by the display unit 43 and the input unit 44. Further, the display unit 43 and the input unit 44 display an operation screen on which an event notification is displayed, accept inputs of responses to acceptances of responses to events through the operation screen, inputs of care records, and various other operations. When the dementia symptoms of the target person 70 occur, for example, the staff 80 inputs via the input unit 44 to the staff terminal 40 which target person 70 has developed what kind of dementia symptoms. The staff 80 may input the date and time of onset of the dementia symptoms.

[0072] The voice input / output unit 45 is, for example, a speaker and a microphone, and enables voice calls by the staff 80 with other staff terminals 40 via the wireless communication unit 42. The staff terminal 40 can be configured by a portable communication terminal device such as, for example, a tablet computer, a smartphone, or a mobile phone.

[0073] [Functions of the control unit 21] Next, the specific functions of the control unit 21 of the server 20 will be described.

[0074] FIG. 12 is a block diagram showing the functional configuration of the control unit 21. The control unit 21 functions as a first acquisition unit 211, a prediction unit 212, a second acquisition unit 213, a generation unit 214, and an output unit 215 by reading a program stored in the storage unit 23 and executing processing. Here, the second acquisition unit 213 corresponds to a specific example of the acquisition unit of the present invention.

[0075] The first acquisition unit 211 acquires index information. As described above, the index information includes at least one of a state index related to the living state of the target person 70 during the data collection period and an environmental index related to the living environment of the target person 70 during the data collection period. The first acquisition unit 211 acquires the index information from, for example, the storage unit 23.

[0076] The prediction unit 212 predicts occurrence information regarding the occurrence of dementia symptoms of the subject 70 during the prediction period based on the index information acquired by the first acquisition unit 211. The prediction period is a period after the data collection period. The occurrence information preferably includes at least any one of information regarding the presence or absence of the occurrence of dementia symptoms, information regarding the occurrence date and time of dementia symptoms, and information regarding the type of dementia symptoms that occur. By including the information regarding the occurrence date and time of dementia symptoms in the occurrence information, it becomes possible to arrange an appropriate number of staff 80, etc. according to this occurrence date and time. By including the information regarding the type of dementia symptoms in the occurrence information, the staff 80, etc. can take more detailed measures in advance. The prediction unit 212 predicts the occurrence information using, for example, a machine learning model. Thereby, it becomes possible to predict the presence or absence, etc. of the occurrence of dementia symptoms of the subject 70 with higher accuracy. The generation of the machine learning model will be described later.

[0077] FIG. 13 illustrates the index information acquired by the first acquisition unit 211 and the occurrence information of the subjects 70 (Mr. A, Mr. B) predicted by the prediction unit 212 based on this index information. This data collection period is, for example, a period of about one week from the time the subject 70 wakes up on December 26, 2022 to the time the subject 70 wakes up on January 2, 2023. The prediction period is a period of about one day from the time the subject 70 wakes up on January 2, 2023 to the time the subject wakes up on January 3, 2023. The data collection period is preferably longer than the prediction period, and more preferably a period of one week or more. Thereby, it becomes possible to predict the occurrence of dementia symptoms with higher accuracy.

[0078] The index information includes, for example, indices 33 to 35(a) at the time the subject 70 wakes up on January 2, 2023. The index information includes indices 1 to 30(d) from the time the subject 70 wakes up on January 1, 2023 to the time the subject 70 wakes up on January 2, 2023. The index information includes indices 31 and 32(g) from the time the subject 70 wakes up on December 26, 2022 to the time the subject 70 wakes up on January 2, 2023.

[0079] Preferably, the index information acquired by the first acquisition unit 211 includes at least one of information regarding changes in the state index of the subject 70 during the data collection period and information regarding changes in the environmental index of the subject 70 during the data collection period. Thereby, it becomes possible to predict the occurrence information of the subject 70 with higher accuracy.

[0080] For example, the index information includes information (b) regarding the difference between the indexes 33 to 35 at the time of waking up of the subject 70 on January 2, 2023 and the indexes 33 to 35 at the time of waking up of the subject 70 on January 1, 2023. The index information includes information (c) regarding the variation of the indexes 33 to 35 from the time of waking up of the subject 70 on December 26, 2022 to the time of waking up of the subject 70 on January 2, 2023. The variation of the indexes 33 to 35 can be calculated using, for example, the standard deviation.

[0081] For example, the index information includes information (f) regarding the comparison between the indexes 26 to 30 from the time of waking up on January 1, 2023 to the time of waking up on January 2, 2023 and the indexes 26 to 30 from the time of waking up on December 31, 2022 to the time of waking up on January 1, 2023. The information regarding this comparison is calculated by division, for example.

[0082] For example, the index information includes information (e) regarding the comparison between the indexes 1 to 25 from the time of waking up of the subject 70 on January 1, 2023 to the time of waking up on January 2, 2023 and the average of the indexes 1 to 25 from the time of waking up on December 26, 2022 to the time of waking up on January 1, 2023. The information regarding this comparison is calculated by division, for example.

[0083] The prediction unit 212 predicts, for example, that for person A, dementia symptoms such as wandering will occur between waking up on January 2, 2023 and waking up on January 3, 2023. The types of dementia symptoms include, in addition to wandering, for example, visual hallucinations, auditory hallucinations, delusions, unauthorized outings, shadowing behavior, irritability, depression, drowsiness tendency, and loud voice, etc. The occurrence information predicted by the prediction unit 212 may include information regarding a plurality of types of dementia symptoms. For example, the prediction unit 212 may predict that for person A, dementia symptoms such as wandering and loud voice will occur.

[0084] The prediction unit 212 predicts, for example, that for person B, no dementia symptoms will occur between waking up on January 2, 2023 and waking up on January 3, 2023.

[0085] The second acquisition unit 213 acquires, at a predetermined time, occurrence information in a prediction period after this predetermined time. The second acquisition unit 213 acquires, for example, the occurrence information predicted by the prediction unit 212.

[0086] FIG. 14 shows an example of the occurrence information acquired by the second acquisition unit 213. The second acquisition unit 213 acquires, for example, at 7:00 am on December 31, 2022, the occurrence information from 7:00 am on January 1, 2023 to 7:00 pm on January 4, 2023. That is, the predetermined time is 7:00 am on December 31, 2022, and the prediction period is the period from 7:00 am on January 1, 2023 to 7:00 pm on January 4, 2023. For example, in this occurrence information, it is predicted that for person A, visual hallucinations and wandering dementia symptoms will occur in the afternoon of January 1, 2023. For person B, it is predicted that no dementia symptoms will occur within the prediction period. For person C, it is predicted that dementia symptoms of inappropriate behavior will occur in the morning of January 3, 2023. For person D, it is predicted that dementia symptoms of anxiety will occur in the morning of January 4, 2023.

[0087] The second acquisition unit 213 further acquires, for example, staff information and subject information (see FIGS. 5 and 6). The second acquisition unit 213 acquires the staff information and the subject information from the storage unit 23, for example.

[0088] Based on the occurrence information acquired by the second acquisition unit 213, the generation unit 214 generates allocation information. The allocation information is information regarding the allocation of the staff 80 required for the care of the subject 70 during the prediction period. The allocation information includes at least information regarding the care date and time when the staff 80 is required and information regarding the number of the staff 80 at the care date and time. Thereby, appropriate care by the staff 80 becomes possible for the subject 70 in whom dementia symptoms have occurred during the prediction period. The generation unit 214 generates the allocation information, for example, by determining the care date and time and associating one or a plurality of the staff 80 selected from among the plurality of the staff 80 with this care date and time. The generation unit 214 may use a predetermined algorithm for the selection of the staff 80.

[0089] The generation unit 214 selects the staff 80 as follows, for example. First, based on the acquired occurrence information, the generation unit 214 determines the care date and time of the subject 70, the number of the staff 80 at the care date and time, and the skills required for the staff 80. Next, the generation unit 214 extracts the staff 80 who can work at the care date and time based on the staff information. Subsequently, the generation unit 214 further extracts the staff 80 that matches the wishes, characteristics, situation, etc. of each subject 70 from among the extracted staff 80 based on the subject information. After this, the generation unit 214 selects the staff 80 from among the extracted staff 80 based on, for example, the travel time to the home of the subject 70.

[0090] FIG. 15 shows an example of the allocation information generated by the generation unit 214. FIG. 15 shows the occurrence information acquired by the second acquisition unit 213 together with the allocation information.

[0091] For example, for the generation information of Mr. A, the care date and time are determined to be in the afternoon of January 1, 2023, the number of staff 80 is one, and the required skill of staff 80 is determined to be an experience of 5 years or more. Next, based on the staff information (Figure 6), the generation unit 214 extracts the staff 80 who can work in the afternoon of January 1, 2023. The generation unit 214 extracts, for example, staff R, staff S, and staff T. Subsequently, based on the target person information (Figure 5), the generation unit 214 extracts staff R and staff T who are female from among staff R, staff S, and staff T. This is because Mr. A has a better compatibility with female staff 80 than with male staff 80. After that, the generation unit 214 selects staff R from among staff R and staff T. This is because staff R has 10 years of experience and the travel time to Mr. A's home is shorter than that of staff T. The generation unit 214 further determines the visit time to Mr. A's home to be from 2:00 pm to 4:00 pm on January 1, 2023 based on the travel time of staff R, etc. That is, the generated placement information includes that staff R is placed as the person in charge of Mr. A from 2:00 pm to 4:00 pm on January 1, 2023.

[0092] For example, for the occurrence information of Mr. C, the care date and time are determined to be the morning of January 3, 2023, and the number of staff 80 is determined to be two. Next, based on the staff information (Figure 6), the generation unit 214 extracts the staff 80 who can work in the morning of January 3, 2023. The generation unit 214 extracts, for example, staff U, staff V, and staff W. Subsequently, based on the target person information (Figure 5) and the travel time to Mr. C's home, etc., the generation unit 214 selects staff V and staff W. Since Mr. C has wheelchair transfer, male staff 80 (staff W) is more suitable than female staff 80 (staff U and staff V). Also, this is because the travel time to Mr. C's home is shorter for staff V than for staff U. The generation unit 214 further determines the visit time to Mr. C's home to be from 11:00 am to 12:00 pm on January 3, 2023 based on the travel time of staff V and staff W, etc. That is, the generated allocation information includes that staff V and staff W are allocated as the responsible staff for Mr. C from 11:00 am to 12:00 pm on January 3, 2023.

[0093] For example, for the occurrence information of Mr. D, the care date and time are determined to be the morning of January 4, 2023, and the number of staff 80 is determined to be one. Next, based on the staff information (Figure 6), the generation unit 214 extracts the staff 80 who can work in the morning of January 4, 2023. The generation unit 214 extracts, for example, staff X, staff Y, and staff Z. Subsequently, based on the target person information (Figure 5) and the travel time to Mr. D's home, etc., the generation unit 214 selects staff Y. Mr. D has a good compatibility with staff Y. Also, this is because the travel time of staff Y to Mr. D's home is relatively short. The generation unit 214 further determines the visit time to Mr. D's home to be from 10:00 am to 12:00 pm on January 4, 2023 based on the travel time of staff Y, etc. That is, the generated allocation information includes that staff Y is allocated as the responsible staff for Mr. D from 10:00 am to 12:00 pm on January 4, 2023.

[0094] The output unit 215 outputs the arrangement information generated by the generation unit 214. For example, the output unit 215 outputs the arrangement information by associating the identification information of the staff in charge with each of the target persons 70. For example, the arrangement information is output by associating the name of the target person 70 with the name of the staff in charge of caring for the target person 70 (see FIG. 15). The output unit 215 outputs the arrangement information, for example, by transmitting the arrangement information to the staff terminal 40 via the communication unit 22. The staff terminal 40 causes the display unit 43 to display the received arrangement information, for example. The output unit 215 may output the arrangement information by transmitting the arrangement information to the administrator terminal 30 via the communication unit 22. The administrator terminal 30 causes the display unit 33 to display the received arrangement information, for example. The output unit 215 may output the arrangement information by transmitting the arrangement information to an external device via the communication unit 22.

[0095] The output unit 215 may further output the occurrence information of the target person 70 predicted by the prediction unit 212. The output unit 215 outputs the occurrence information, for example, by transmitting the occurrence information to the staff terminal 40 via the communication unit 22. The staff terminal 40 causes the display unit 43 to display the received occurrence information, for example. For example, the display unit 43 displays that it is highly likely that person A will experience hallucinations and wandering dementia symptoms in the afternoon of January 1, 2023. The display unit 43 may display a prior response instruction to the staff 80 regarding the occurrence of dementia symptoms in the target person 70. When the prediction unit 212 predicts that there will be no occurrence of dementia symptoms in the target person during the prediction period, the output unit 215 may not output the occurrence information.

[0096] Note that the detection unit 10, the server 20, the administrator terminal 30, and the staff terminal 40 may include components other than the above-described components, or may not include some of the above-described components.

[0097] [Operation of Server 20] Next, the operation of the server 20 in the monitoring system 1 will be described.

[0098] FIG. 16 is a flowchart showing the operation of the server. This flowchart can be executed by the control unit 21 according to a program stored in the storage unit 23 of the server 20.

[0099] First, the control unit 21 acquires the index information of the target person 70 (step S101). For example, the control unit 21 reads and acquires the index information stored in the storage unit 23. This index information includes, for example, status information regarding the living state of the target person 70 during the data collection period and environmental information regarding the living environment of the target person 70 during the data collection period.

[0100] Subsequently, the control unit 21 predicts the occurrence information regarding the occurrence of dementia symptoms of the target person 70 in the prediction period after the data collection period based on the index information acquired in the process of step S101 (step S102). For example, the control unit 21 predicts the occurrence information of the target person 70 by inputting the index information acquired in the process of step S101 into a machine learning model.

[0101] Next, the control unit 21 acquires the occurrence information predicted in the process of step S102 (step S103). Further, the control unit 21 acquires the target person information and the staff information (step S104). The control unit 21 may acquire at least one of the target person information and the staff information before acquiring the occurrence information, or may acquire the occurrence information, the target person information, and the staff information simultaneously.

[0102] Subsequently, the control unit 21 generates the arrangement information based on the occurrence information, the target person information, and the staff information acquired in the processes of step S103 and step S104 (step S105).

[0103] After that, the control unit 21 outputs the arrangement information generated in the process of step S105 (step S106) and ends the process.

[0104] [Generation of Machine Learning Model] Next, a method for generating a machine learning model used by the control unit 21 will be described.

[0105] FIG. 17 is a flowchart showing a method for generating a machine learning model. For example, the control unit 21 functions as a model generation unit to generate a machine learning model. Here, the server 20 corresponds to a specific example of a machine learning device.

[0106] In the process of FIG. 17, machine learning is executed using the learning data prepared in advance. This learning data includes a large number of data sets of explanatory variables and target variables. The explanatory variable is learning index information of a dementia patient, and the target variable is learning occurrence information of a dementia patient. The learning index information includes at least one of a learning state index regarding the living state of a dementia patient during a learning data collection period and a learning environment index regarding the living environment of a dementia patient during a learning data collection period. The learning state index and the learning environment index can be obtained, for example, based on the information detected by the detection unit 10. The learning occurrence information is information regarding the occurrence of dementia symptoms of a dementia patient during a learning prediction period after the learning data collection period. The learning occurrence information can be obtained, for example, from the care history of a dementia patient input to the staff terminal 40 by the staff 80. The learning index information and the learning occurrence information are, for example, past information of a plurality of subjects 70. The learning index information and the learning occurrence information may be stored in the storage unit 23.

[0107] For the learner (not shown), for example, a stand-alone high-performance computer using processors such as a CPU and a GPU, or a cloud computer is used. In the following, a learning method using a neural network configured by combining perceptrons such as deep learning will be described, but it is not limited to this, and various methods can be applied. For example, random forest, decision tree, support vector machine (SVM), logistic regression, k-nearest neighbor method, topic model, etc. can be applied.

[0108] (Step S111) The learning machine reads the training data. Initially, it reads the first set of training data, and for the i-th time, it reads the i-th set of training data.

[0109] (Step S112) The learning machine inputs the explanatory variables among the read training data into the neural network.

[0110] (Step S113) The learning machine compares the prediction result of the neural network with the target variable.

[0111] (Step S114) The learning machine adjusts the parameters based on the comparison result. For example, the learning machine adjusts the parameters so that the difference in the comparison result becomes smaller by executing a process based on backpropagation (error backpropagation method).

[0112] (Step S115) If the processing of all data from the 1st to the i-th set is completed (YES), the learning machine proceeds to step S116. If not (NO), the learning machine returns to step S111, reads the next training data, and repeats the processing from step S111 and below.

[0113] (Step S116) The learning machine determines whether to continue learning. If it continues (YES), it returns to step S111 and executes the processing from the 1st to the i-th set again in steps S111 to S115. If it does not continue (NO), it proceeds to step S117.

[0114] (Step S117) The learning device stores the machine learning model learned through the previous processes and then ends (END). The storage destination includes the storage unit 23 of the server 20. In the operation of the server 20 described above, the occurrence information regarding the onset of the dementia symptoms of the subject 70 is predicted using the machine learning model generated in this way. The learning device may learn the learning data for each facility, or may learn the learning data for each subject 70.

[0115] [Effects of Server 20 and Monitoring System 1] In the server 20 and the monitoring system 1 according to the present embodiment, occurrence information regarding the prediction of the onset of dementia symptoms of the subject 70 is acquired, and arrangement information regarding the arrangement of the in-charge staff is generated based on the acquired occurrence information. Therefore, it becomes possible to arrange the staff 80 in accordance with the onset of the dementia symptoms of the subject 70. This will be described below.

[0116] When creating the work schedule of the staff without predicting when and how the dementia symptoms will occur in dementia patients, there is a high possibility of waste in the work situation of the staff. Especially in home-visit care services and the like, this waste is likely to occur. Specifically, since the number of staff required for each subject and the date and time when care is required cannot be predicted, there may be cases where care is not required even when the staff visits the subject's home. In addition, there is a possibility that the staff may not be able to visit when care is required for the subject.

[0117] On the other hand, in the server 20 and the monitoring system 1, the work schedule of the staff 80 can be arranged according to the predicted onset situation of the dementia symptoms of each subject 70. Therefore, it is possible to reduce the waste in the work situation and improve the efficiency of the attendance of the staff 80. In addition, since the staff 80 is at the home or the like of the subject 70 when care is required, it is possible to improve the satisfaction of the subject 70.

[0118] The configuration of the monitoring system 1 described above explains the main configuration in describing the features of the above-described embodiment, and is not limited to the above-described configuration, and various modifications can be made within the scope of the claims. Further, it does not exclude the configuration provided in a general monitoring system.

[0119] For example, in the above embodiment, an example in which the server 20 predicts the occurrence information has been described, but the server 20 may acquire the occurrence information predicted by an external device.

[0120] Further, in the above embodiment, an example in which the server 20 selects the person in charge has been described, but the server 20 may not select the person in charge. For example, based on the arrangement information output from the server 20, an external device may select the person in charge. Alternatively, based on the arrangement information output from the server 20, an administrator 90 or the like may select the person in charge.

[0121] Further, in the above embodiment, an example in which the server 20 uses a machine learning model to predict the occurrence information of the target person 70 has been described, but the server 20 may use other methods such as statistical processing to predict the occurrence information of the target person 70.

[0122] Further, a part or all of the functions of the server 20 may be provided in the administrator terminal 30, the staff terminal 40, or the detection unit 10.

[0123] Further, the detection unit 10, the server 20, the administrator terminal 30, and the staff terminal 40 may each be configured by a plurality of devices, or any one of the devices may be included in another device and configured as a single device.

[0124] Further, some steps of the above-described flowchart may be omitted, and other steps may be added. Also, a part of each step may be executed simultaneously, or one step may be divided into a plurality of steps and executed. Also, the execution order of each step may be changed.

[0125] In addition, the means and methods for performing various processes in the monitoring system 1 according to the above-described embodiment can be realized by either a dedicated hardware circuit or a programmed computer. The above program may be provided by a computer-readable recording medium such as a USB memory or a DVD (Digital Versatile Disc)-ROM, or may be provided online via a network such as the Internet. In this case, the program recorded on the computer-readable recording medium is usually transferred and stored in a storage unit such as a hard disk. Further, the above program may be provided as a single application software, or may be incorporated as a single function into the software of a device such as its detection unit.

Description of Signs

[0126] 1 Monitoring system, 10 Detection unit, 11 Control unit, 12 Communication unit, 13 Camera, 14 Care call unit, 15 Audio input / output unit, 20 Server, 21 Control unit, 22 Communication unit, 23 Storage unit, 30 Administrator terminal, 31 Control unit, 32 Communication unit, 33 Display unit, 34 Input unit, 40 Staff terminal, 41 Control unit, 42 Wireless communication unit, 43 Display unit, 44 Input unit, 45 Audio input / output unit, 46 Position acquisition unit, 50 Network, 51 Access point, 60 Bed, 70 Subject, 80 staff, 90 managers.

Claims

1. At a predetermined time, an acquisition unit that acquires occurrence information regarding prediction of occurrence of dementia symptoms of a target dementia patient in a prediction period after the predetermined time; A generation unit that generates arrangement information regarding arrangement of staff members necessary for care of the target dementia patient in the prediction period based on the acquired occurrence information An information processing apparatus comprising the same.

2. The information processing apparatus according to claim 1, wherein the occurrence information includes at least any one of information regarding presence or absence of occurrence of the dementia symptoms, information regarding date and time of occurrence of the dementia symptoms, and information regarding types of the dementia symptoms that occur.

3. The information processing apparatus according to claim 1, wherein the arrangement information includes at least information regarding care date and time when the staff members are necessary and information regarding the number of the staff members at the care date and time.

4. The acquisition unit further acquires staff information regarding each of a plurality of staff members who care for a dementia patient and target person information regarding the target dementia patient, The information processing apparatus according to claim 1, wherein the generation unit generates the arrangement information based on the occurrence information, the staff information, and the target person information.

5. The information processing apparatus according to claim 4, wherein the generation unit determines care date and time when the staff members are necessary, and generates the arrangement information by selecting one or a plurality of the staff members from among the plurality of staff members in association with the care date and time.

6. The information processing apparatus according to claim 5, further comprising an output unit that outputs the generated arrangement information.

7. The staff information includes identification information of each of the plurality of staff members, The information processing apparatus according to claim 6, wherein the output unit outputs the arrangement information by associating the identification information of the staff members with the target dementia patient.

8. The information processing apparatus according to claim 1, further comprising a prediction unit that predicts the occurrence information.

9. The information processing apparatus according to claim 8, wherein the prediction unit predicts the occurrence information based on a state index related to the living state of the target dementia patient and an environment index related to the living environment of the target dementia patient.

10. The information processing apparatus according to claim 9, wherein the state index includes at least one of an index representing the movement of the target dementia patient and an index representing the sleep state of the target dementia patient.

11. The information processing apparatus according to claim 9, wherein the environment index includes at least one of an index representing the temperature of the living environment, an index representing the atmospheric pressure of the living environment, and an index representing the humidity of the living environment.

12. The information processing apparatus according to claim 8, wherein the prediction unit predicts the occurrence information using a machine learning model.

13. Obtaining, at a predetermined time, occurrence information related to the prediction of the occurrence of dementia symptoms of a target dementia patient in a prediction period after the predetermined time; Generating arrangement information related to the arrangement of staff members required for the care of the target dementia patient in the prediction period based on the obtained occurrence information; An information processing method including.

14. Obtaining, at a predetermined time, occurrence information related to the prediction of the occurrence of dementia symptoms of a target dementia patient in a prediction period after the predetermined time; Generating arrangement information related to the arrangement of staff members required for the care of the target dementia patient in the prediction period based on the obtained occurrence information; An information processing program for causing a computer to execute a process including.

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