Information processing device and information processing method

Abstract

To provide an information processing device and information processing method for appropriately supporting a caregiver in providing care to a care recipient.SOLUTION: An information processing device disclosed herein comprises: a cause determination unit for determining whether behavior of a care recipient is abnormal behavior caused by dementia or not on the basis of (1) dementia level information of the care recipient and (2) at least one of environmental information, excretion information, and sleep information of the care recipient; and a support information output unit for outputting support information for supporting a caregiver in providing care to the care recipient based on a determination result of the cause determination unit and sensor information corresponding to a sensing result related to the caregiver providing care to the care recipient, or the care recipient.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and the like. [Background technology]

[0002] Conventionally, there are known systems that are used in medical settings, nursing care facilities, etc. For example, Patent Document 1 discloses a method for instructing how to assist a person being assisted to move. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-233471 Summary of the Invention [Problem to be solved by the invention]

[0004] An information processing device and an information processing method are provided that appropriately support a caregiver in providing care to a person being assisted. [Means for solving the problem]

[0005] The information processing device of this embodiment includes a factor determination unit that, when the behavior of an assisted person is determined to be abnormal, determines whether the abnormal behavior is due to dementia and whether it is due to an excretion disorder based on (1) dementia level information of the assisted person and (2) at least one of environmental information, excretion information, and sleep information of the assisted person, and a support information output unit that outputs support information to support the assistant in assisting the assisted person based on the determination result of the factor determination unit and sensor information, which is a sensing result regarding the assistant assisting the assisted person or the assisted person. [Brief explanation of the drawings]

[0006] [Figure 1] 1 shows an example of the configuration of an information processing system including an information processing device. [Figure 2A] An example of a nursing bed, which is a nursing device. [Figure 2B] An example of a lift, a care device. [Figure 2C] An example of a sensing device. [Figure 2D] An example of a sensing device. [Figure 3] 1 shows an example of a server system configuration. [Figure 4] 1 shows an example of the configuration of a mobile terminal device. [Figure 5] An explanatory diagram of a neural network. [Figure 6] Example of input and output of a neural network for factor determination. [Figure 7] 10 is a flowchart illustrating a learning process for determining a factor. [Figure 8] 10 is a flowchart illustrating a cause determination process. [Figure 9] Example of input and output of the neural network for supporting information output. [Figure 10] An example of the configuration of a neural network for outputting supporting information. [Figure 11] An example of the configuration of a neural network for outputting supporting information. [Figure 12] An example of the configuration of a neural network for outputting supporting information. [Figure 13] An example of the relationship between the neural network for determining factors and the neural network for outputting supporting information. [Figure 14] 10 is an example of first association information. [Figure 15] 10 is an example of second association information. [Figure 16] 10 is an example of third association information. [Figure 17] An example of the settings screen. [Figure 18] 10 is a flowchart illustrating a setting process. [Figure 19] 10 is a flowchart illustrating a process for outputting each piece of support information. [Figure 20] 10 is a flowchart illustrating a start determination of an assistance sequence. [Figure 21] 10 is a flowchart illustrating a meal assistance sequence. [Figure 22] 10 is a flowchart illustrating an excretion assistance sequence. [Figure 23] 10 is a flowchart illustrating a transfer and movement assistance sequence. [Figure 24A] FIG. 10 is a diagram illustrating transitions between a plurality of assistance sequences. [Figure 24B] FIG. 10 is a diagram illustrating transitions between a plurality of assistance sequences. [Figure 25A] An example of the settings screen. [Figure 25B] An example of the display screen for adding data. [Figure 25C] 10 is an example of a display screen for determining input data. [Figure 25D] An example of a display screen for presenting learning results. [Figure 26] 1 shows an example of a basic configuration of a neural network according to this embodiment. [Figure 27] FIG. 1 is an explanatory diagram of a process for determining the structure of a neural network by classification. [Figure 28] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 29] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 30] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 31] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 32] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 33] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 34] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 35] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 36]Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 37] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 38] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 39] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 40] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 41] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 42] Specific examples of input data used to support a caregiver in providing assistance to a person being assisted. [Figure 43] A specific example of output data when supporting meal assistance. [Figure 44] A specific example of output data when supporting excretion assistance. [Figure 45] A specific example of output data for supporting transfer and mobility assistance. DETAILED DESCRIPTION OF THE INVENTION

[0007] Hereinafter, the present embodiment will be described with reference to the drawings. In the drawings, identical or equivalent elements are designated by the same reference numerals, and duplicate explanations will be omitted. Note that the present embodiment described below does not unduly limit the content described in the claims. Furthermore, not all of the configurations described in the present embodiment are necessarily essential components.

[0008] 1. System configuration example FIG. 1 shows an example of the configuration of an information processing system 10 including an information processing device according to this embodiment. The information processing system 10 according to this embodiment digitalizes the "intuition" and "tacit knowledge" of caregivers in, for example, nursing care facilities, to provide instructions to caregivers so that they can provide appropriate care regardless of their level of expertise. The information processing system 10 shown in FIG. 1 includes a server system 100, a caregiver device 200, a nursing care device 300, and a sensor group 400. However, the configuration of the information processing system 10 is not limited to that shown in FIG. 1, and various modifications are possible, such as omitting some components or adding other components. The same applies to the configurations shown in FIGS. 3 and 4, which will be described later, in which modifications such as omissions and additions of components are possible.

[0009] The information processing device of this embodiment corresponds to, for example, the server system 100. However, the method of this embodiment is not limited to this, and the processing of the information processing device may be executed by distributed processing using the server system 100 and another device. For example, the information processing device of this embodiment may include the server system 100 and the caregiver device 200. An example in which the information processing device is the server system 100 will be described below.

[0010] The server system 100 is connected to the caregiver device 200, the care device 300, and the sensor group 400 via, for example, a network NW. The network NW here is, for example, a public communication network such as the Internet, but may also be a LAN (Local Area Network) or the like. For example, the caregiver device 200, the care device 300, and the sensor group 400 are installed in a care facility or the like. The server system 100 performs processing based on information from the sensor group 400, and outputs information to the caregiver device 200 and remotely controls the care device 300 based on the processing result.

[0011] While FIG. 1 illustrates an example in which the caregiver device 200, the care device 300, and the sensor group 400 can each communicate with the server system 100 via the network NW, the present invention is not limited to this. For example, a relay device (not shown) may be provided in a care facility or the like. The relay device is a device that can communicate with the server system 100 via the network NW. Information output from the sensor group 400 may be aggregated in the relay device using a LAN within the care facility, and the relay device may transmit the information to the server system 100. Alternatively, information from the server system 100 may be transmitted to the relay device, and the relay device may transmit necessary information to the caregiver device 200 or the care device 300. For example, it is assumed that multiple caregiver devices 200 and multiple care devices 300 are used simultaneously in a care facility. The relay device may perform a process of selecting the caregiver device 200 or the care device 300 to which the server system 100 should transmit information. Alternatively, the relay device may be an administrator terminal used by an administrator of the care facility and operate based on an operation input by the administrator. For example, information from the server system 100 may be displayed on the display unit of the relay device, and an administrator viewing the display result may select the destination caregiver device 200 or the care device 300. As described above, the information processing device of this embodiment can be implemented in various modifications, and for example, the relay device may be included in the information processing device.

[0012] The server system 100 may be one server or may include multiple servers. For example, the server system 100 may include a database server and an application server. The database server stores various data, which will be described later with reference to Fig. 3. The application server performs the processes, which will be described later with reference to Figs. 7, 8, 18 to 23, etc. The multiple servers here may be physical servers or virtual servers. Furthermore, when a virtual server is used, the virtual server may be provided on one physical server or may be distributed across multiple physical servers. As described above, the specific configuration of the server system 100 in this embodiment can be modified in various ways.

[0013] The caregiver device 200 is a device used by a caregiver who provides care to a person being assisted (a patient or resident) in a care facility or the like, and is a device used to present information to the caregiver or to allow the caregiver to input information. For example, the caregiver device 200 may be a device carried or worn by the caregiver. For example, the caregiver device 200 includes a mobile terminal device 210 and a wearable device 220. The mobile terminal device 210 is, for example, a smartphone, but may also be another portable device. The wearable device 220 is a device that can be worn by the caregiver, for example, a headset including earphones or headphones and a microphone. The wearable device 220 may also be a device in the form of glasses, a wristwatch, or another shape. The caregiver device 200 may also be another device such as a PC (Personal Computer).

[0014] The care device 300 is a device used to provide care (including assistance) to a person being assisted in a care facility, etc. While the caregiver device 200 is a device that mainly presents information to a caregiver, the care device 300 is a device for directly providing assistance to a person being assisted. For example, the nursing care device 300 may include a nursing care bed 310 with an adjustable bottom angle and height (the bottom may be plate-shaped or mesh-shaped, and any shape), and a lift 320 for transferring a person being cared for from the nursing care bed 310 to a wheelchair. The nursing care device 300 may also include other devices such as a wheelchair, a walker, rehabilitation equipment, and a food delivery cart for delivering meals.

[0015] Fig. 2A shows an example of a nursing bed 310. The nursing bed 310 has multiple bottom sections whose heights and angles can be changed. This allows the posture of a person being assisted lying on the nursing bed 310 to be flexibly changed. Fig. 2B shows an example of a lift 320. The lift 320 is a device used for transferring a person being assisted who has a low ADL (Activity of Daily Living) evaluation index and is difficult to transfer manually, for example.

[0016] The sensor group 400 includes multiple sensors arranged in a care facility or the like. The sensor group 400 may include a motion sensor 410, an image sensor 420, and an odor sensor 430. The motion sensor 410 may be an acceleration sensor, a gyro sensor, or another sensor capable of detecting motion. The motion sensor 410 may be a sensor that detects the movement of a person being assisted, or a sensor that detects the movement of a caregiver. The image sensor 420 is a sensor that converts an image of a subject formed through a lens into an electrical signal. The odor sensor 430 is a sensor that detects and digitizes odors. The sensor group 400 may also include various sensors such as a temperature sensor, a humidity sensor, an illuminance sensor, a magnetic sensor, a position sensor, and an air pressure sensor.

[0017] 1, the caregiver device 200, the care device 300, and the sensor group 400 are depicted separately. For example, the sensors included in the sensor group 400 may be placed in rooms, dining rooms, hallways, stairs, etc. in the care facility. For example, cameras including an image sensor 420 are placed at various locations in the care facility. A sensing device for sensing information necessary for care may also be used. By providing sensors at various locations in the care facility, not only can necessary information be sensed but also the location of the sensor can be identified.

[0018] For example, FIG. 2C shows an example of a sensing device 440 placed on the mattress of a nursing bed 310. The sensing device 440 shown in FIG. 2C includes, for example, an odor sensor 430, and detects whether the person being assisted has excreted. Note that the sensing device 430 may also be capable of determining whether the person is ill based on body odor or exhaled breath. FIG. 2D shows an example of a sensing device 450 placed under the mattress on the nursing bed 310 (placed between the nursing bed 310 and the mattress). The sensing device 450 shown in FIG. 2D includes, for example, a pressure sensor, and is capable of detecting the heart rate, respiratory rate, and activity level of the person being assisted. Note that the sensing device 450 may also be capable of determining whether the person being assisted is asleep and whether they have left the bed.

[0019] However, the method of this embodiment is not limited to the above example, and the sensors included in the sensor group 400 may be provided in the caregiver device 200 or the care device 300. For example, a camera, an acceleration sensor, a gyro sensor, a GPS (Global Positioning System) sensor, etc., included in the mobile terminal device 210 may be used as the sensors included in the sensor group 400. Furthermore, the care device 300 may be provided with a motion sensor for detecting the posture of the care device 300, a camera for capturing an image of the person being assisted or the caregiver using the care device 300, etc.

[0020] 3 is a block diagram showing a detailed configuration example of the server system 100. The server system 100 includes, for example, a processing unit 110, a storage unit 120, and a communication unit .

[0021] The processing unit 110 of this embodiment is configured by the following hardware. The hardware can include at least one of a circuit for processing digital signals and a circuit for processing analog signals. For example, the hardware can be configured by one or more circuit devices or one or more circuit elements mounted on a circuit board. The one or more circuit devices are, for example, an integrated circuit (IC), a field-programmable gate array (FPGA), etc. The one or more circuit elements are, for example, a resistor, a capacitor, etc.

[0022] The processing unit 110 may also be implemented by the following processor. The server system 100 of this embodiment includes a memory that stores information and a processor that operates based on the information stored in the memory. The information may be, for example, a program and various data. The processor includes hardware. Various types of processors may be used, such as a central processing unit (CPU), a graphics processing unit (GPU), or a digital signal processor (DSP). The memory may be a semiconductor memory such as a static random access memory (SRAM), a dynamic random access memory (DRAM), or a flash memory, or may be a register, a magnetic storage device such as a hard disk drive (HDD), or an optical storage device such as an optical disk drive. For example, the memory stores computer-readable instructions, and the processor executes the instructions to realize the functions of the processing unit 110 as processing. The instructions may be instructions from an instruction set that constitutes a program, or instructions that instruct the hardware circuitry of the processor to operate.

[0023] The processing unit 110 includes a factor determining unit 111 , a support information output unit 112 , a setting unit 113 , and a learning unit 114 .

[0024] The factor determination unit 111 determines whether the behavior of the person being assisted is abnormal behavior caused by dementia based on input including at least dementia level information of the person being assisted. For example, the factor determination unit 111 determines whether the behavior of the person being assisted is abnormal behavior caused by dementia based on (1) the dementia level information of the person being assisted, and (2) at least one of environmental information, excretion information, and sleep information of the person being assisted. Details of each piece of information will be described later.

[0025] The support information output unit 112 outputs support information that supports the caregiver in assisting the person being assisted, based on the determination result output by the factor determination unit 111 and sensor information that is a sensing result related to the caregiver assisting the person being assisted or the person being assisted. Details of the support information will be described later.

[0026] The setting unit 113 performs setting processing when using the information processing system 10 according to this embodiment. For example, a caregiver who is a user of the information processing system 10 may be able to set which support information from a large amount of support information to output. In this case, as will be described later with reference to FIGS. 17 and 18, the setting unit 113 performs processing for accepting setting operations by the caregiver and updating the setting information. The setting unit 113 may also perform setting processing for adding custom support information specific to the user to the output. Specific examples will be described later with reference to FIGS. 25A to 25D, etc.

[0027] The learning unit 114 outputs a trained model by performing machine learning based on training data. Here, the machine learning is, for example, supervised learning. Training data in supervised learning is a data set in which input data corresponding to the input of the model is associated with ground truth data representing appropriate output data when the input data is input. The learning unit 114 may generate the trained model by performing machine learning using, for example, a neural network. Hereinafter, a neural network is abbreviated as NN. For example, the learning unit 114 performs processing to generate an NN 121 for factor determination and an NN 122 for support information output. Details of the processing by the learning unit 114 will be described later. However, machine learning is not essential in this embodiment, and the learning unit 114 can be omitted. Even when machine learning is performed, the learning process can be executed in a learning device different from the server system 100, and in this case, the learning unit 114 can also be omitted.

[0028] The storage unit 120 is a work area for the processing unit 110 and stores various types of information. The storage unit 120 can be realized by various types of memory, and the memory may be a semiconductor memory such as an SRAM, a DRAM, a ROM, or a flash memory, or may be a register, a magnetic storage device, or an optical storage device.

[0029] The storage unit 120 stores information used in processing by the factor determination unit 111 and information used in processing by the support information output unit 112. For example, the storage unit 120 may store a factor determination NN 121 and a support information output NN 122 acquired by machine learning using a NN. Note that the factor determination NN 121 and the support information output NN 122 here include parameters used in calculations using the structure in addition to information defining the structure of the NN. The parameters are, specifically, weights whose values are determined by machine learning.

[0030] The storage unit 120 may also store first association information 123, second association information 124, and third association information 125. The first association information 123 is information that associates a caregiver with information indicating whether or not each piece of support information is to be output to the caregiver. The second association information 124 is information that associates support information with sensor information required to output the support information. The third association information 125 is information that associates a given nursing care facility with sensor information that can be acquired at the nursing care facility. Specific examples of each piece of association information will be described later using FIGS. 14 to 16. The storage unit 120 may also store other information.

[0031] The communication unit 130 is an interface for performing communication via the network NW, and includes, for example, an antenna, an RF (radio frequency) circuit, and a baseband circuit. The communication unit 130 may operate under the control of the processing unit 110, or may include a processor for communication control different from the processing unit 110. The communication unit 130 is an interface for performing communication according to, for example, TCP / IP (Transmission Control Protocol / Internet Protocol). However, the specific communication method can be modified in various ways.

[0032] 4 is a block diagram showing a detailed configuration example of the mobile terminal device 210, which is an example of the caregiver device 200. The mobile terminal device 210 includes, for example, a processing unit 211, a storage unit 212, a communication unit 213, a display unit 214, and an operation unit 215.

[0033] Processing unit 211 is configured by hardware including at least one of a circuit for processing digital signals and a circuit for processing analog signals. Processing unit 211 may also be realized by a processor. Various types of processors, such as a CPU, a GPU, or a DSP, can be used as the processor. The processor executes instructions stored in the memory of mobile terminal device 210, thereby realizing the functions of processing unit 211 as processing.

[0034] The storage unit 212 is a work area for the processing unit 211, and is realized by various types of memory such as SRAM, DRAM, and ROM.

[0035] The communication unit 213 is an interface for communication via the network NW, and includes, for example, an antenna, an RF circuit, and a baseband circuit. The communication unit 213 communicates with the server system 100 via, for example, the network NW.

[0036] Display unit 214 is an interface that displays various information and may be a liquid crystal display, an organic EL display, or any other type of display. Operation unit 215 is an interface that accepts user operations. Operation unit 215 may be buttons or the like provided on mobile terminal device 210. Display unit 214 and operation unit 215 may also be a touch panel that is integrally configured.

[0037] The mobile terminal device 210 may also include components not shown in FIG. 4 , such as a light emitting unit, a vibration unit, and a sound output unit. The light emitting unit is, for example, an LED (light emitting diode) and provides notification by emitting light. The vibration unit is, for example, a motor and provides notification by vibration. The sound output unit is, for example, a speaker and provides notification by sound. As described above, the mobile terminal device 210 may also include a sensor included in the sensor group 400.

[0038] 2. Factor determination and supporting information output The information processing device of this embodiment performs a process of determining the factors behind the behavior of the person being assisted and a process of outputting support information that supports the caregiver in providing assistance to the person being assisted. In this way, it is possible to have the caregiver provide appropriate assistance to the person being assisted, taking into account factors such as dementia. Below, machine learning will be described as a specific example of a method for determining factors and outputting support information. However, the method of this embodiment is not limited to using machine learning, and various modifications are possible. Furthermore, while an example using NN as machine learning will be described below, other machine learning methods such as SVM (support vector machine) may also be used, or methods that are an extension of NN or SVM may also be used.

[0039] 2.1 A brief explanation of NN FIG. 5 shows an example of the basic structure of a neural network (NN). Each circle in FIG. 5 is called a node or neuron. In the example of FIG. 5, the NN has an input layer, two or more hidden layers, and an output layer. The input layer is I, the hidden layers are H1 and Hn, and the output layer is O. In the example of FIG. 5, the number of nodes in the input layer is 2, the number of nodes in each hidden layer is 5, and the number of nodes in each layer can be modified in various ways. In addition, FIG. 5 shows an example in which each node in a given layer is connected to all nodes in the next layer, but this configuration can also be modified in various ways.

[0040] The input layer receives input values and outputs them to the hidden layer H1. In the example of Figure 5, the input layer I receives two types of input values. Each node in the input layer may perform some processing on the input value and output the processed value.

[0041] In a NN, weights are set between two connected nodes. W1 in Figure 5 is the weight between the input layer I and the first hidden layer H1. W1 represents the set of weights between a given node included in the input layer and a given node included in the first hidden layer. For example, W1 in Figure 5 is information containing 10 weights.

[0042] Each node in the first hidden layer H1 performs a weighted sum of the outputs of the nodes in the input layer I connected to that node using a weight W1, and then adds a bias. Each node then applies a nonlinear activation function to the sum to determine the output of that node. The activation function may be a ReLU function, a sigmoid function, or another function.

[0043] The same is true for subsequent layers. That is, in a given layer, the output of the previous layer is weighted using the weight W, and then a bias is added and an activation function is applied to determine the output for the next layer. The output of the output layer is the output of the NN.

[0044] As can be seen from the above explanation, in order to obtain desired output data from input data using a NN, it is necessary to set appropriate weights and biases. In learning, training data is prepared in which given input data is associated with ground truth data that represents correct output data for that input data. The NN learning process is a process of determining the most likely weights based on the training data. Note that various learning methods, such as backpropagation, are known for the NN learning process. In this embodiment, these learning methods can be widely applied, and therefore detailed description will be omitted.

[0045] Furthermore, the NN is not limited to the configuration shown in FIG. 5. For example, a convolutional neural network (CNN) may be used as the NN. The CNN has a convolutional layer and a pooling layer. The convolutional layer performs a convolution operation. Specifically, the convolution operation here is a filtering process. The pooling layer performs a process to reduce the vertical and horizontal size of the data. In the CNN, the characteristics of the filter used in the convolution operation are learned by performing a learning process using an error backpropagation method or the like. In other words, the weights in the NN include the filter characteristics in the CNN. Furthermore, a network with another configuration, such as an RNN (Recurrent Neural Network), may be used as the NN.

[0046] 2.2 Factor Determination FIG. 6 is a diagram illustrating input data and output data of the factor determination NN 121 used for factor determination. The input data for factor determination includes, for example, dementia level information. The input data also includes at least one of environmental information, sleep information, and excretion information. FIG. 6 shows an example in which the input data includes all of environmental information, sleep information, and excretion information. The input data may also include other information. For example, as shown in FIG. 6, the input data may include medication information and dietary water information. The configuration of the factor determination NN 121 is not limited to that shown in FIG. 6, and various modifications are possible.

[0047] The dementia level information is information that indicates the degree of progression of dementia of the person being assisted. For example, the dementia level information may be a score on the Mini-Mental State Examination (MMSE), a score on the Hasegawa Dementia Scale-Revised (HDS-R), or other information that indicates the results of a dementia test. The dementia level information may also be information based on brain images acquired using computed tomography (CT) or magnetic resonance imaging (MRI). For example, the dementia level information may be information that indicates the results of a doctor's diagnosis based on the brain images, the brain images themselves, or the results of some kind of image processing performed on the brain images.

[0048] The environmental information is information that represents the living environment of the person being assisted. The environmental information includes temperature information that represents the temperature of the living environment of the person being assisted, humidity information that represents the humidity, illuminance information that represents the illuminance, and atmospheric pressure information that represents the atmospheric pressure. For example, a temperature sensor, a humidity sensor, an illuminance sensor, and an atmospheric pressure sensor are placed in a place that is regularly used, such as the room or dining room of the person being assisted, and the temperature information, humidity information, illuminance information, and atmospheric pressure information are obtained based on the output of each sensor.

[0049] The environmental information may also include information about sound. For example, a microphone may be placed in a living environment such as a living room, and information collected by the microphone may be used as the environmental information. The environmental information may be information about sound pressure or information representing the results of frequency analysis. The environmental information may also include information about the time when a specific sound occurs.

[0050] The environmental information may also include information about the nursing bed 310 used by the person being assisted. The information related to the nursing bed 310 may be information specifying the model of the nursing bed 310, or may be information such as the type and hardness of a mattress used in conjunction with the nursing bed 310. The information related to the nursing bed 310 may also include information indicating the driving results of the nursing bed 310. For example, information related to the angle and height of the bottom of the nursing bed 310, or information such as the time when the nursing bed 310 was driven may be used as environmental information.

[0051] The sleep information is information that indicates the sleeping state of the person being assisted. For example, the sleep information may be detected using the sensing device 450 shown in FIG. 2D or a wristwatch-type device including a photoelectric sensor that detects the pulse rate. The sleep information includes, for example, the sleep start time, wake-up time, daily sleep duration, sleep depth, number and time of awakenings, heart rate, respiratory rate, and activity level during sleep.

[0052] The excretion information includes information indicating the excretion status of the person being assisted. For example, the excretion information may be detected using the sensing device 440 shown in FIG. 2C. The sensing device 440 outputs whether the person being assisted has excreted, the type of excretion, and the timing at which excretion was determined based on, for example, the odor sensor 430. The excretion information includes, for example, information such as the number of excretions in a given span, the excretion interval, and the type of excretion. The excretion information may also include information such as captured images of the diaper after excretion and comments added by the caregiver.

[0053] The medication information is information that identifies the medication administered to the person being assisted. For example, the medication information is information that indicates the name, dosage, and time of administration of the medication taken by the person being assisted. The medication information may also include information on the prescription issued to the person being assisted.

[0054] The dietary moisture information is information that indicates the food and moisture ingested by the person receiving care. For example, the dietary moisture information includes the time of the meal, the menu, and the amount actually eaten. The dietary moisture information may also include information that specifies the ease of eating, such as the hardness and size of ingredients. The dietary moisture information also includes the time of intake of moisture, the type of moisture (water, tea, etc.), and the amount ingested.

[0055] In the learning stage, training data for creating the factor determination NN 121 is acquired by associating the correct data with the input data for a predetermined period. The predetermined period here may be a fixed period such as one day. Alternatively, the predetermined period may be a period set based on the occurrence of an abnormal behavior when the person being assisted exhibits some abnormal behavior.

[0056] The correct answer data may also be provided by an expert with specialized knowledge, such as a doctor. When a person receiving care exhibits abnormal behavior, the expert diagnoses the person and identifies the cause of the abnormal behavior. The correct answer data here is information that represents the identified cause. For example, the correct answer data indicates whether the behavior is due to dementia, environmental factors, sleep disorders, or excretory disorders. For example, if the input data corresponding to one period of one person receiving care is associated with the correct answer data to form one dataset, training data containing a large number of datasets can be obtained by increasing the number of people receiving care and the target period.

[0057] The learning unit 114 of the server system 100 acquires training data for factor determination, and performs machine learning based on the training data to create the factor determination NN 121.

[0058] FIG. 7 is a flowchart illustrating the learning process for generating the factor determination NN 121. When this process starts, first, in step S101, the learning unit 114 acquires input data for learning. The input data here is as described above and includes, for example, dementia level information, environmental information, sleep information, and excretion information. The input data may also include other information such as medication information and dietary water information.

[0059] In step S102, the learning unit 114 acquires the correct answer data associated with the input data. For example, the learning unit 114 executes the processes of steps S101 and S102 by reading out any one data set of the training data acquired in the learning stage.

[0060] In step S103, the learning unit 114 performs a process of updating the weights of the NN. Specifically, the learning unit 114 inputs the input data acquired in step S101 to the factor determination NN 121, and acquires output data by performing forward calculations using the weights at that stage. The learning unit 114 calculates an objective function based on the output data and the correct answer data. The objective function here is, for example, an error function based on the difference between the output data and the correct answer data, or a cross-entropy function based on the distribution of the output data and the distribution of the correct answer data.

[0061] For example, if the output layer of the factor determination NN121 is a known softmax layer, the output of the output layer is probability data that sums to 1. For example, the output layer includes four nodes, the first node to the fourth node. The output value of the first node represents the "likelihood that the behavior of the person being assisted is a dementia factor." The output value of the second node represents the "likelihood that the behavior of the person being assisted is an environmental factor." The output value of the third node represents the "likelihood that the behavior of the person being assisted is a sleep disorder factor." The output value of the fourth node represents the "likelihood that the behavior of the person being assisted is a toilet disorder factor." Correct answer data is data in which the value of the correct factor is 1 and the other values are 0. For example, if an expert determines that dementia is a factor, the probability of the dementia factor will be 1, and the other three data with probabilities of 0 will be used as the correct answer data.

[0062] The learning unit 114 updates the weights so that, for example, the error function decreases. The above-mentioned backpropagation method and the like are known as weight update methods, and these methods can be widely applied to this embodiment as well.

[0063] In step S104, the learning unit 114 determines whether to end the learning process. For example, multiple data sets included in the training data may be divided into learning data and validation data. The learning unit 114 may end the learning process when the weight updating process has been performed using all of the learning data, or may end the learning process when the accuracy rate based on the validation data exceeds a given threshold.

[0064] If the learning process is not to be ended, the learning unit 114 returns to step S101 and continues the process. That is, the learning unit 114 reads a new data set from the training data and performs a process of updating the weights based on the new data set.

[0065] When the learning process is terminated, the learning unit 114 stores the factor determination NN 121 at that stage as a trained model in the storage unit 120. Note that Fig. 7 is an example of the learning process, and the method of this embodiment is not limited to this. For example, methods such as batch learning are also widely known in machine learning, and these methods can be widely applied in this embodiment.

[0066] FIG. 8 is a flowchart illustrating the processing of the factor determination unit 111 in the inference stage. When this processing starts, first, in step S201, the factor determination unit 111 determines whether the person being assisted has engaged in abnormal behavior that suggests dementia. The factor determination unit 111 may automatically determine whether the behavior of the person being assisted is abnormal based on sensor information about the person being assisted. For example, the sensor group 400 may include a motion sensor 410, an image sensor 420, a microphone, etc., and the factor determination unit 111 may determine whether the person being assisted has engaged in abnormal behavior by detecting the movements and vocalizations of the person being assisted. Alternatively, the caregiver may observe the movements of the person being assisted and input the observation results using the caregiver device 200, etc. In this case, the factor determination unit 111 executes the processing of step S201 based on the input from the caregiver. If it is determined that the person being assisted has not engaged in abnormal behavior, the factor determination unit 111 ends the processing without performing step S202 and subsequent steps.

[0067] If it is determined that the person being assisted has exhibited abnormal behavior, in step S202, the factor determination unit 111 acquires input data related to the person being assisted. For example, the storage unit 120 acquires and stores, via the communication unit 130, dementia level information related to the person being assisted, sensor information collected by the sensor group 400, and the like. The dementia level information may be acquired, for example, at a care facility or the like, and transmitted from a device at the care facility or the like to the server system 100. The factor determination unit 111 performs processing to read, from the collected data, data related to the target person being assisted, such as dementia level information, environmental information, sleep information, excretion information, etc. corresponding to a predetermined period, as input data.

[0068] In step S203, the factor determination unit 111 reads out the factor determination NN 121 from the storage unit 120. Then, the input data acquired in step S202 is input to the factor determination NN 121, and output data is obtained by performing forward calculations. The output data of the factor determination NN 121 are, for example, four probability values representing the likelihood of each factor, as described above. The factor determination unit 111 determines, for example, the factor with the largest probability value as the factor of the abnormal behavior of the person being assisted. For example, if the value representing the likelihood of the dementia factor is greater than the likelihoods of the other three factors, the factor determination unit 111 determines that the abnormal behavior is the dementia factor. The output of the factor determination unit 111 is not limited to this, and may be the four probability values themselves, or a value calculated based on them.

[0069] The factor determination unit 111, for example, periodically executes the process shown in Fig. 8. The frequency of the process is arbitrary, but may be, for example, about once a day. In this way, it is possible to periodically determine whether the person being assisted is exhibiting abnormal behavior, and the cause of abnormal behavior if such behavior is detected. For example, the factor determination unit 111 may execute the process of Fig. 8 every morning, and determine the assistance policy for that day based on the processing result. Furthermore, various modifications of the process of the factor determination unit 111 are possible, such as executing the process of Fig. 8 without waiting for the next processing timing if abnormal behavior is observed in the person being assisted.

[0070] In the above, an example has been described in which it is determined whether the behavior of the person being assisted is abnormal or not (see step S201 in FIG. 8) as a process separate from the process using the factor determination NN 121. However, an NN may be created that also performs determinations on whether the behavior is abnormal or not.

[0071] For example, in addition to the input data shown in FIG. 6, the factor determination NN 121 may also receive input of sensor information or the like that indicates the behavior of the person being assisted. The factor determination NN 121 may include a node that outputs the "likelihood that the behavior of the person being assisted is normal" in addition to the nodes that output the likelihood of the four factors shown in FIG. 6. In the learning stage, training data is created using data when abnormal behavior is present as well as data when abnormal behavior is absent. Specifically, the correct answer data associated with the input data includes data that indicates "no abnormal behavior." In this case, the factor determination unit 111 can estimate the presence or absence of abnormal behavior and, if abnormal behavior is present, the cause by inputting the input data to the factor determination NN 121.

[0072] 2.3 Assistance support 2.3.1 Input and Output 9 is a diagram illustrating an example of general input data of the support information output NN 122 used to output support information. As shown in FIG. 9, the input data may include sensor information. The sensor information includes information obtained by sensing the person being assisted or information obtained by sensing the caregiver. The sensor information is output, for example, from a sensor included in the sensor group 400.

[0073] The sensor information may also include information obtained by sensing the living environment of the person being assisted. In this case, the sensor information corresponds to, for example, the environmental information described above. For example, the sensor information may include outputs from a temperature sensor, a humidity sensor, an illuminance sensor, an air pressure sensor, a microphone, etc.

[0074] The input data may also include attribute data of the person being assisted and physical evaluation data that indicates a physical evaluation. The attribute data of the person being assisted includes information such as the person's age, sex, height, weight, medical history, medication history, etc. The physical evaluation data includes information such as the evaluation score of ADL, rehabilitation history, fall risk, and pressure ulcer risk.

[0075] The input data may also include attribute data of the caregiver and data related to the care facility. The attribute data of the caregiver includes the caregiver's age, sex, height, weight, care experience, qualifications held, etc. The information about the nursing care facility includes information about the nursing care schedule at the nursing care facility, the number and usage status of nursing care devices 300, the number of people being assisted, statistical data on the level of care required, etc.

[0076] 28 to 42 are diagrams illustrating details of data used as input when a caregiver supports the care recipient in this embodiment, and in a narrow sense, are diagrams illustrating examples of input data for the support information output NN 122. As shown in FIGS. 28 to 42, various information can be used as input data in this embodiment. It is not necessary to acquire all of the input data shown in FIGS. 28 to 42, and some information may be omitted. Furthermore, other information not shown in FIGS. 28 to 42 may be added.

[0077] Furthermore, the output data of the support information output NN 122 is information used to support the execution of each assistance action when the assistance of the care recipient by the caregiver is subdivided into multiple assistance actions. For example, the output data of the support information output NN 122 is support information for determining the timing to start assistance, the movements and vocalizations during assistance, the type and amount of items to provide to the care recipient, etc.

[0078] Figures 43 to 45 are figures illustrating details of data used in this embodiment when supporting an assistant in assisting an assisted person, and in a narrow sense, are figures illustrating examples of support information that is output data of NN122 for outputting support information.

[0079] Figure 43 shows an example of support information output during meal assistance, in which a caregiver assists a person receiving care with eating. For example, during meal assistance, the caregiver understands the characteristics of the person receiving care and explains them to the person receiving care in an easy-to-understand manner, thereby facilitating the smooth execution of the meal. For example, if the person receiving care has poor chewing ability, if the caregiver is aware of this, they can take measures to prevent aspiration. It is also useful to provide guidance to the person receiving care, such as, "The rice has been softened, so chew it well." The output data of Number 1 in Figure 43 is support information for "communicating the user's characteristics" to the caregiver. This may be data that directly represents the characteristics of the person receiving care, or it may be information converted to make it easier for the caregiver to understand. As mentioned above, the caregiver may communicate the characteristics of the person receiving care to the person receiving care, and the output data of Number 1 in Figure 43 may include data for this purpose. The same applies to Number 2 and beyond. The output data shown in Figure 43 includes information to support the various actions of the caregiver during meal assistance.

[0080] Figure 44 shows an example of support information output when assisting a person receiving assistance with excretion. The excretion assistance may be performed in a toilet or using a diaper. Numbers 66-72 represent output data when assisting in excretion in a toilet, and Numbers 73-75 represent output data when assisting in excretion using a diaper.

[0081] Figure 45 is an example of support information output for transfer assistance and mobility assistance, which assists the transfer or movement of a person being assisted. Note that the presence or type of equipment used for transfer and mobility assistance varies depending on the condition of the person being assisted and the availability of lifts, etc. In the example of Figure 45, Numbers 92-103 represent output data for assistance using a wheelchair, Numbers 104-107 represent output data for assistance using a cane, and Numbers 108-112 represent output data for assistance using a lift.

[0082] As shown in Figures 43 to 45, the support information may include information supporting at least one of meal assistance, toilet assistance, and transfer / movement assistance. This makes it possible to appropriately support highly necessary assistance in nursing care facilities, etc. For example, supporting meal assistance can reduce incidents such as aspiration and improve the nutritional status of the person being assisted. Supporting toilet assistance can reduce excretion leakage, reduce the amount of work required to deal with excretion leakage and the risks that arise from it, mitigate excretion disorders, and mitigate the risk of falls. Furthermore, supporting transfer / movement assistance can reduce the risk of falls and enable advance preparation by necessary caregivers.

[0083] 2.3.2 Example of NN configuration for support information output 10 to 12 are diagrams showing more specific configuration examples of the support information output NN 122 shown in FIG. 9. As shown in FIG. 10, the support information output NN 122 may be a collection of multiple NNs, each of which outputs one piece of support information. Support information 1 in FIG. 10 corresponds to any one of the support information shown in FIGS. 43 to 45. Input data group 1 refers to one or more pieces of input data required to output support information 1, out of the multiple pieces of input data shown in FIGS. 28 to 42. The same applies to support information 2 and onwards.

[0084] 11, the support information output NN 122 may be a collection of multiple NNs that can collectively output multiple related output data. In the example of Fig. 11, the support information output NN 122 includes an NN for outputting eating assistance support information, an NN for outputting excretion assistance support information, and an NN for outputting transfer assistance support information.

[0085] For example, the NN for outputting meal assistance support information outputs a plurality of pieces of meal assistance support information. The output data of the NN for outputting eating assistance support information corresponds to the plurality of pieces of support information shown in Fig. 43. The input data of the NN for outputting eating assistance support information represents the plurality of pieces of input data necessary for outputting eating assistance support information, out of the plurality of pieces of input data shown in Figs. 28 to 42. The output of the NN for outputting excretion assistance support information corresponds to the plurality of support information shown in Fig. 44. The output of the NN for outputting transfer assistance support information corresponds to the plurality of support information shown in Fig. 45.

[0086] Furthermore, as shown in Fig. 12, the support information output NN 122 may be a single NN. The input data to the NN in Fig. 12 is a collection of all the data shown in Figs. 28 to 42, and the output data is a collection of all the support information shown in Figs. 43 to 45.

[0087] Furthermore, the configuration of the NN 122 for outputting support information is not limited to that shown in Figures 10 to 12. For example, an intermediate configuration between Figures 10 and 11 may be used by dividing the NN for outputting meal assistance support information into several parts. In addition, the specific configuration of the NN 122 for outputting support information can be modified in various ways.

[0088] Fig. 13 is a diagram showing an example of the relationship between the factor determination NN 121 and the support information output NN 122. The input data for factor determination in Fig. 13 is the input in Fig. 6, and includes dementia level information and the like. The input data for support information output 122 is the input in Fig. 9, and specifically, the data shown in Figs. 28 to 42. Note that the input data for factor determination and the input data for support information output may partially overlap.

[0089] In the example shown in FIG. 13 , the output data of the factor determination NN 121 is used as part of the input data of the support information output NN 122. The output data of the factor determination NN 121 may be information specifying one factor, which is the determination result, as described above, or may be information based on multiple probability values. When the support information output NN 122 includes multiple NNs, as in FIG. 10 or 11 , the output data of the factor determination NN 121 may be input to all or some of the NNs. In this way, it becomes possible to output support information based on the result of the factor determination in the factor determination unit 111. As a result, it becomes possible for the caregiver to understand the degree of progression of dementia of the person being assisted and to provide various types of assistance according to the degree of progression.

[0090] If no abnormal behavior is observed in the person being assisted, the output of the factor determination NN 121 may be treated as 0. As described above, the factor determination NN 121 may be capable of outputting information indicating "no abnormal behavior."

[0091] However, in the method of this embodiment, it is sufficient that the determination result by the factor determination unit 111 is used to output support information, and the specific method is not limited to the example of FIG.

[0092] 2.3.3 Learning and inference processes The flow of the learning process of the NN 122 for outputting support information in the learning unit 114 is the same as when creating the NN 121 for determining factor. The training data used when creating the NN 122 for outputting support information includes a data set in which correct answer data representing the results of assistance provided by a skilled caregiver using tacit knowledge is associated with the input data.

[0093] For example, when assisting a person receiving assistance with eating, at least the sensors necessary for the meal assistance among the sensor group 400 are turned on. As a result, of the input data shown in Figures 28 to 42, data related to meal assistance is acquired by the sensor group 400 and stored in the storage unit 120 of the server system 100. In addition, data indicating the results of the assistance by the skilled caregiver, such as the posture that the skilled caregiver had the person receiving assistance assume (corresponding to Numbers 9-12 in Figure 43, etc.), the timing at which the food was served with a spoon (corresponding to Number 26 in Figure 43), and the amount served per mouthful (corresponding to Number 25 in Figure 43), are stored in the storage unit 120 as correct answer data.

[0094] The learning unit 114 inputs input data from the training data to the NN 122 for outputting support information, and performs forward calculations using the weights used at that time to obtain output data. The learning unit 114 also obtains an objective function (e.g., an error function such as a mean square error function) based on the output data and the correct answer data, and updates the weights to reduce the error using an error backpropagation method or the like. When learning is complete, the NN 122 for outputting support information is stored in the storage unit 120 as a trained model.

[0095] 13, the output of the factor determination NN 121 may be included in the input of the support information output NN 122. In this case, the input data of the training data includes information indicating the cause of the abnormal behavior of the person being assisted. For example, as described in the learning process of the factor determination NN 121, correct answer data assigned by an expert such as a doctor may be used as one of the input data in the training data. Alternatively, if the learning of the factor determination NN 121 has been completed first, an inference process may be performed using the factor determination NN 121, and the result may be used as one of the input data in the training data, as shown in FIG. 8.

[0096] As in the example above, the correct answer data is information that represents the results of assistance provided by an experienced caregiver using tacit knowledge. The experienced caregiver can naturally provide assistance that is appropriate for the person being assisted, taking into account the degree of progression of dementia of the person being assisted. In other words, by using the assistance results of the experienced caregiver as correct answer data, it is possible to machine-learn appropriate assistance according to the cause of abnormal behavior. The processing after the training data is acquired is similar in this case: the learning unit 114 performs forward calculations using the input data from the training data, calculates an error function from the output data and the correct answer data, and updates the weights to minimize the error.

[0097] The support information output unit 112 of the server system 100 acquires the input data shown in Figures 28 to 42 in the inference stage. Note that the input data here only needs to include data from which desired support information can be output, and it is not necessary to acquire all of the input data shown in Figures 28 to 42. The support information output unit 112 also acquires the determination result of the factor determination unit 111 as one piece of input data. The support information output unit 112 reads out the trained NN 122 for outputting support information from the storage unit 120, and inputs the input data to the NN 122 for outputting support information. 11 and 12, when using an NN that can output multiple pieces of support information and it is required to output only some of the support information, it is possible that some of the input data has not been acquired. In this case, the support information output unit 112 may, for example, set the value of the input data that has not been acquired to 0. The support information output unit 112 performs a forward calculation to obtain the support information as output data.

[0098] 3. Processing flow Next, a specific process flow when a caregiver assists a care recipient in a care facility or the like will be described.

[0099] 3.1 Estimating the causes of behavior First, apart from the process of starting and executing a specific assistance sequence, the server system 100 determines whether the person being assisted is exhibiting abnormal behavior, and if abnormal behavior is observed, what the cause of the abnormal behavior is.

[0100] For example, the factor determination unit 111 periodically performs the process described above with reference to Fig. 8. In this way, the presence or absence of abnormal behavior and the cause of the abnormal behavior can be determined for each of a plurality of assistance recipients who are the target of assistance. In the following, the explanation will be given assuming that the result of the factor determination by the factor determination unit 111 has been acquired.

[0101] 3.2 Assistance support 3.2.1 User Settings Examples of support information in this embodiment are as shown in FIGS. 43 to 45. The support information output unit 112 may output all of this support information. However, if the amount of information to be notified is too much, an inexperienced caregiver may not be able to grasp the content or recognize the difference in importance of each step. Furthermore, a caregiver with a certain degree of experience may be able to properly perform a given care without support, and may find the notification of support information annoying. Therefore, in this embodiment, the user who is the caregiver may be able to set the support information to be output.

[0102] The storage unit 120 of the server system 100 may store the first association information 123. Fig. 14 is a specific example of the first association information 123. As shown in Fig. 14, the first association information 123 is information that associates an assistant ID that identifies an assistant, support information, and information that indicates the output setting of the support information.

[0103] The output setting includes active and inactive. When given support information is set to active, the support information output unit 112 outputs the support information to the corresponding caregiver. When given support information is set to inactive, the support information output unit 112 does not output the support information to the corresponding caregiver. In this way, the support information to be output can be flexibly set for each caregiver.

[0104] However, as shown in Figures 28 to 42, the types of input data assumed in this embodiment are very large, and it is not always possible for a nursing care facility to acquire all of the input data. For example, due to constraints such as budget or the structure of the nursing care facility, it may not be possible to arrange sensors necessary to acquire given input data. In this case, the lack of input data may make it impossible to obtain given support information with sufficient accuracy.

[0105] Therefore, the support information output setting may include "output disabled" in addition to "active / inactive." "Output disabled" indicates a setting in which support information is not output because the necessary input data cannot be acquired. "Inactive" indicates a setting in which the necessary input data can be acquired but support information is not output, and is therefore different from "output disabled."

[0106] For example, the storage unit 120 of the server system 100 may store second association information 124 and third association information 125. FIG. As shown in FIG. 15, the second association information 124 includes support information and a required input data group that is required to output the support information. The support information is any one of the multiple data shown in FIGS. 43 to 45. The required input data group is one or more of the data shown in FIGS. 28 to 42. The required input data group may be data specified by the user, for example. Alternatively, a support information output NN 122 may be created for each of multiple candidate input data groups, and the candidate input data group with the highest accuracy rate using validation data may be selected as the required input data group. Furthermore, the required input data group is not limited to one set, and multiple candidate input data groups with an accuracy rate equal to or higher than a predetermined threshold may be used as the required input data group.

[0107] 16 is a specific example of the third association information 125. The third association information 125 is information that associates nursing care facilities with input data that can be acquired at the nursing care facilities. For example, a person in charge of the nursing care facility may select input data that can be acquired at the nursing care facility and transmit the selection result to the server system 100. Alternatively, the sensor group 400 installed at the nursing care facility may associate information that identifies the nursing care facility with sensor information and transmit the associated information to the server system 100. The processing unit 110 of the server system 100 may create the third association information 125 based on the acquisition history of the sensor information.

[0108] The setting unit 113 of the server system 100 determines whether or not to output each piece of support information for each nursing care facility based on the second association information 124 and the third association information 125. Specifically, the setting unit 113 determines whether or not to output the support information based on whether or not a required input data group required to output the support information is included in an input data group that can be acquired at the target nursing care facility.

[0109] Furthermore, there is a correspondence between input data and the sensor used to acquire the input data. Therefore, the storage unit 120 may store fourth association information that associates input data with one or more sensors used to acquire the input data. By using the fourth association information in addition to the second association information 124 and the third association information 125, it becomes possible to determine whether or not to output support information for each sensor. Alternatively, instead of separately providing the fourth association information, the input data in the second association information 124 and the third association information 125 may be replaced with sensor information.

[0110] Furthermore, the number of devices including a given sensor is not limited to one. For example, if a motion sensor 410 and an image sensor 420 are required, a device such as a smartphone including both a camera and an acceleration sensor may be used, or two separate devices may be used. Furthermore, multiple camera models with different resolutions, magnifications, etc. may be available. Therefore, the storage unit 120 may store fifth association information that associates a sensor with a device including the sensor. In this case, data can be managed on a device-by-device basis. For example, if a nursing care facility specifies an installed device, the server system 100 identifies the sensor included in the device and the input data that can be acquired using the sensor. Since nursing care facility staff and caregivers do not need to know the sensor included in the device or the input data that can be acquired by the device, user convenience can be improved.

[0111] In the above, an example has been described in which it is determined whether or not support information can be output for each care facility (see, for example, FIG. 16), but the method of this embodiment is not limited to this. For example, if a nursing care facility has a first space for residents with a high level of care needs and a second space for residents with a low level of care needs, it is conceivable that the first space will have many sensors and the second space will have fewer sensors. In this case, the server system 100 may separately manage the support information that can be output in the first space and the support information that can be output in the second space. In addition, various modifications of the specific method are possible, such as managing whether or not support information can be output for each person receiving care.

[0112] FIG. 17 shows an example of a setting screen for setting support information to be output. The process described below is realized, for example, by storing a Web application program in the storage unit 212 of the mobile terminal device 210 that communicates with the server system 100 and having the processing unit 211 operate in accordance with the Web application program. For example, displaying the display screen and accepting user operations are performed using the display unit 214 and the operation unit 215 in accordance with the Web application program. Furthermore, the generation and updating of the display screen and database control in accordance with user operations are performed by the setting unit 113 of the server system 100. However, the method of this embodiment is not limited to using a Web application program, and various modifications are possible, such as using a so-called native app. Although FIG. 17 shows an example in which a setting screen is displayed on the display unit 214 of the mobile terminal device 210, the setting screen may also be displayed on another caregiver device 200.

[0113] For example, the setting screen is a screen where it is possible to select active, inactive, or output disabled for each of a plurality of pieces of support information. Fig. 17 shows an example of a setting screen including objects OB1 to OB3 corresponding to three pieces of support information: "Timing for changing diapers," which is support information that supports assistance with excretion, and "Amount of food served with a spoon," and "Timing for serving food with a spoon," which are support information that supports assistance with eating.

[0114] For example, if the corresponding support information is active, the object is displayed in a first manner. If the corresponding support information is inactive, the object is displayed in a second manner. If the corresponding support information cannot be output, the object is displayed in a third manner. Note that the display manner here may be controlled using the size, shape, and color of the object, or may be controlled using the size, font, color, etc. of the text included in the object. Various other variations on the specific display manner are possible.

[0115] FIG. 17 shows an example in which objects OB1 to OB3 are buttons, and the colors of the buttons vary depending on whether they are active, inactive, or not outputtable. For example, "when to change a diaper" is active, "amount served with a spoon" is not outputtable, and "when to serve food with a spoon" is not active. In this case, the support information output unit 112 outputs support information indicating "when to change a diaper" and does not output support information indicating "when to serve food with a spoon." Furthermore, since the target nursing care facility may have difficulty accurately determining the "amount served with a spoon" due to a lack of sensors, output of the "amount served with a spoon" is not permitted. By displaying objects OB1 to OB3 in different ways, the current settings can be presented to the caregiver in an easy-to-understand manner.

[0116] The caregiver can switch between active and inactive by operating the operation unit 215 of the mobile terminal device 210. For example, when the caregiver performs an operation to select "diaper change timing," information indicating this is sent to the server system 100. The setting unit 113 performs a process to update the output setting corresponding to "diaper change timing" of the target caregiver ID in the first association information 123 to inactive. The setting unit 113 also generates a display screen in which the corresponding object OB1 is displayed in a second mode corresponding to inactive, and sends this to the mobile terminal device 210 via the communication unit 130. The display unit 214 displays the display screen.

[0117] Similarly, when a selection operation is performed on an object corresponding to inactive support information, the setting unit 113 updates the output settings corresponding to the target caregiver and support information to active, and the display unit 214 changes the display mode of the selected object to the first mode.

[0118] On the other hand, even if a selection operation is performed on an object corresponding to support information that cannot be output, display unit 214 maintains the display in the third mode indicating that output is not possible. In this case, setting unit 113 does not perform the update process of first association information 123.

[0119] Furthermore, when a selection operation is performed on an object corresponding to support information that cannot be output, input data required to output the support information may be suggested. For example, the server system 100 may identify the required input data based on the second association information 124 and perform processing to display the input data on the display unit 214 of the mobile terminal device 210. As described above, the input data here may be replaced with a sensor or a device. For example, the setting unit 113 may identify a sensor or device required to output the support information selected by the user and perform processing to display the sensor or device on the display unit 214 of the mobile terminal device 210.

[0120] 18 is a flowchart illustrating the setting process. First, the caregiver executes a setting change operation using his / her own caregiver device 200. In step S301, the setting unit 113 of the server system 100 accepts the setting change operation via the network NW.

[0121] In step S302, the setting unit 113 performs a process of displaying a setting screen on the caregiver device 200 based on the first association information 123 at that time and the caregiver ID indicating the caregiver who performed the setting change operation. The process of step S302 may be a process of creating an image corresponding to the setting screen and transmitting the image to the caregiver device 200, or a process of transmitting information for generating the setting screen to the caregiver device 200. The information for generating the setting screen may be an extraction result obtained by extracting part of the data corresponding to the caregiver ID from the first association information 123. Alternatively, the information for generating the setting screen may be a processing result obtained by performing some processing on the extraction result. As a result, for example, a screen corresponding to FIG. 17 is displayed on the display unit 214 of the mobile terminal device 210.

[0122] In step S303, the setting unit 113 determines a user operation executed on the caregiver device 200. If no operation to change the settings is detected, the setting unit 113 ends the process.

[0123] Furthermore, when an operation to deactivate active support information or an operation to activate inactive support information is performed, the setting unit 113 reflects the setting change in step S304. Specifically, the setting unit 113 performs a process to update the first association information 123 based on information from the caregiver device 200.

[0124] Furthermore, if a selection operation for support information that cannot be output is performed, in step S305, the setting unit 113 identifies input data, sensors, or devices that are insufficient for outputting the support information. In step S306, the setting unit 113 performs processing to present the identified input data, sensors, or devices to the caregiver. The processing of step S306 may be processing to transmit the display image itself, as in step S302, or processing to transmit information used to generate the display image. Furthermore, the presentation here is not limited to display, and presentation processing using audio, etc. may also be performed.

[0125] 3.2.2 Support information output processing 19 is a flowchart illustrating the support information output process performed by support information output unit 112. In step S401, support information output unit 112 acquires input data corresponding to the support information to be output. Specifically, storage unit 120 of server system 100 stores one or more pieces of input data for outputting the target support information, from among the plurality of input data shown in FIGS. 28 to 42. The support information and the input data are associated with each other using, for example, second association information 124 described above.

[0126] In step S402, the support information output unit 112 obtains support information by inputting necessary input data into the support information output NN 122. In step S403, the support information output unit 112 determines whether or not a notification based on the support information is necessary. If a notification is necessary, in step S404, the support information output unit 112 performs notification processing. The notification may be an audio notification using earphones of a headset or the like, a display using the display unit 214 of the mobile terminal device 210, or some other notification. If a notification is not necessary, or after performing notification processing, the support information output unit 112 ends processing.

[0127] As mentioned above, the number of pieces of support information to be output can be changed depending on the type of sensors installed in the nursing care facility and the settings of the caregiver. However, in any case, the process flow shown in Fig. 19 is the same for each piece of support information to be output, including identifying input data, performing calculations using a neural network, and notifying as needed.

[0128] If the processing performance of the server system 100 is sufficient, the support information output unit 112 may always perform the processing shown in Figure 19 for all support information set as the output target, and may perform notification processing as appropriate for information that is determined to require notification.

[0129] Furthermore, in consideration of reducing the processing load, the process shown in FIG. 19 may be executed by limiting the support information required at that time. For example, as shown in FIGS. 43 to 45, support information can be classified into situations in which it is required, such as support information required for meal assistance and support information required for excretion assistance. Therefore, the support information output unit 112 may identify support information required in the current situation and execute the process shown in FIG. 19 for the identified support information. For example, the support information output unit 112 may determine whether or not to start assistance for each of meal assistance, excretion assistance, and transfer / movement assistance. The support information output unit 112 executes the process shown in FIG. 19 for support information related to assistance that has been determined to be started. The start determination will be described later with reference to FIG. 20.

[0130] Furthermore, meal assistance can be chronologically classified into assistance provided before eating, assistance provided during eating, assistance provided after eating, etc. Therefore, the support information output unit 112 can define the order in which to execute the processes shown in Fig. 19 among multiple pieces of support information. Also, depending on the assistance, there may be restrictions on the execution order or necessity of execution between the assistances, such as the second assistance being required only when the first assistance has been performed.

[0131] Therefore, the assistance provided by the information processing system 10 of this embodiment may be performed according to an assistance sequence that combines multiple types of assistance. Specifically, the support information output unit 112 sequentially outputs multiple types of support information according to the assistance sequence, thereby supporting the assistance provided by the caregiver to the person being assisted.

[0132] Below, examples of assistance sequences for meal assistance, excretion assistance, and transfer / movement assistance will be explained. Specifically, first, a start determination for each assistance sequence will be made, and then the specific flow of each assistance sequence will be explained.

[0133] 21 to 23, for the sake of convenience, only some of the support information shown in Figures 43 to 45 is output in the assistance sequences described below. However, it will be readily apparent to those skilled in the art that modifications are possible in each assistance sequence described below, such as omitting the output of some of the support information or adding the output of other support information shown in Figures 43 to 45.

[0134] 3.2.3 Start determination In this embodiment, the assistance may include meal assistance, toilet assistance, and transfer / movement assistance. However, these assistances do not need to be provided all the time, and a specific assistance sequence is executed when the person being assisted needs the assistance and an assistant who can provide the assistance is available. That is, in this embodiment, a determination is first made as to whether to start the assistance sequence, and then, depending on the result of the determination, a decision may be made as to whether to start or wait for the assistance sequence.

[0135] FIG. 20 is a flowchart illustrating the start determination. This process is executed periodically, for example, for each person being assisted. First, in step S501, the support information output unit 112 acquires at least a portion of the input data shown in FIGS. 28 to 42. In step S502, the support information output unit 112 obtains support information by inputting the acquired input data into the support information output NN 122. The support information here is information that specifies at least one of the start timing of meal assistance, the start timing of toilet assistance, and the start timing of transfer / movement assistance. For example, the support information output unit 112 may determine whether or not to start each assistance at the time when the process of FIG. 20 is performed. Alternatively, the support information output unit 112 may output information that specifies a specific time, such as the number of minutes after which each assistance will start.

[0136] In step S503, the support information output unit 112 determines whether the current timing is the start timing of the assistance sequence. If it is determined that the current timing is not the start timing, the support information output unit 112 ends the process and waits until the process shown in FIG. 20 is performed again.

[0137] For example, the support information output unit 112 determines the timing to start the meal assistance sequence by inputting information on the meal schedule at the nursing facility as well as the individual person being assisted's complexion, body temperature, weight, medication, past meal history, excretion history, rehabilitation history, etc.

[0138] Furthermore, there may be cases where a rough schedule for excretion assistance is set, such as five times a day. Therefore, the support information output unit 112 determines the start timing of the excretion assistance sequence by inputting data such as the amount and timing of meals for the individual person being assisted, the amount and timing of fluid intake, whether or not laxatives have been administered, past excretion history, rehabilitation records, and the state of bedsores, in addition to information on the excretion assistance schedule at the nursing facility.

[0139] In addition, the support information output unit 112 determines the timing to start an assistance sequence for transfer and movement assistance by inputting the ADL, medical history, etc. of the person being assisted, as well as whether or not an event that requires the person being assisted to move, such as eating or recreation, has occurred.

[0140] If it is determined that the current timing is the start timing of the assistance sequence, the support information output unit 112 performs a process of determining an assistant who will assist the target person being assisted in step S504. For example, the support information output unit 112 may store information such as the work shifts of assistants at the nursing facility and the allocation of responsibilities for the people being assisted, and may determine an assistant based on this information.

[0141] In step S505, the support information output unit 112 performs a notification process to instruct the caregiver device 200 of the determined caregiver to start an assistance sequence. For example, the support information output unit 112 may perform a process to play back a voice message such as "Please start assisting Mr. A with his meal" on the wearable device 220, such as a headset. The support information output unit 112 may also perform a process to display similar text on the display unit 214 of the mobile terminal device 210.

[0142] In step S506, the support information output unit 112 determines the response of the caregiver to the notification process. For example, three responses may be set as the response by the caregiver: "OK," "later," and "transfer." The response by the caregiver may be made by voice. For example, the response of the caregiver may be acquired based on the detection result of a microphone in a headset, or may be realized in other ways, such as by text input.

[0143] "OK" is a response indicating that the instructed assistance sequence can be started. In this case, in step S507, the support information output unit 112 proceeds to a specific assistance sequence. For example, the support information output unit 112 starts the processing of Figures 21, 22, 23, etc.

[0144] "Later" is a response indicating that the assistance sequence cannot be started immediately, but may be able to be started after a predetermined time has passed. For example, this corresponds to a case where the person is currently performing another task, but the instructed assistance sequence can be started once that task is completed. In this case, in step S508, the support information output unit 112 waits for a predetermined time, and after waiting, returns to step S505 and executes the notification process again for the same caregiver.

[0145] "Transfer" is a response indicating that it is difficult to execute the assistance sequence and that a request is made to another assistant. In this case, the support information output unit 112 returns to step S504 and selects another assistant. The same process is carried out from step S505 onwards.

[0146] However, the processing when "transfer" is selected is not limited to this. For example, when a given caregiver selects "transfer," the support information output unit 112 may notify multiple caregivers at once. Then, it may select an assistant who responds "OK" from among the multiple caregivers, and start a specific assistance sequence for that assistant.

[0147] 3.2.4 Meal assistance FIG. 21 is a flowchart illustrating a specific assistance sequence for providing meal assistance. First, when the meal assistance sequence is started, in step S601, the support information output unit 112 controls the turning on of sensors necessary for supporting meal assistance among the sensor group 400 arranged in the nursing facility or the like. In step S601, the support information output unit 112 may remotely control the on / off of sensors included in the sensor group 400. Alternatively, the support information output unit 112 may instruct a device in the nursing facility, such as the caregiver device 200, which sensors or devices to turn on, and the caregiver may turn on the sensors in accordance with the instruction. From this point on, although not explicitly stated in the flowchart, it is assumed that the sensor group 400 periodically transmits sensor information to the server system 100, and the support information output unit 112 is capable of acquiring input data necessary for outputting support information.

[0148] In step S602, the support information output unit 112 outputs support information for providing a meal appropriate for the person being assisted, based on the support information output NN 122. For example, in step S602, the support information output unit 112 outputs support information instructing a meal appropriate for the allergies of the person being assisted and a medication appropriate for the person's condition.

[0149] Next, in step S603, the support information output unit 112 determines whether the person being assisted and the assistant have moved to a position where they will eat. The meal may take place in the person being assisted's room, or in a dining room or the like. The processing of step S603 is performed by using information capable of identifying the positions of the person being assisted and the assistant, such as a camera or RFID (radio frequency identifier), as input data. Note that in the processing of step S603, the support information output unit 112 may determine that the person being assisted and the assistant have moved to a position where they will eat, for example, when an image of the person being assisted and the meal is captured on the screen of a camera carried by the assistant.

[0150] If at least one of the person being assisted and the caregiver is not in position, in step S604, the support information output unit 112 waits for a certain period of time and then performs the process of step S603 again.

[0151] When the person being assisted and the caregiver are in position, in step S605, the support information output unit 112 calculates the minimum amount of food to be provided. Here, the minimum amount to be provided may be an amount less than the amount that was served. In other words, the caregiver does not need to force the person to eat all of the food that was served, and once the minimum amount has been served, they do not need to force themselves to serve more. The processing of step S605 is performed using, for example, the facial color of the person being assisted, care records, weight changes, meal schedule, etc. as input data.

[0152] In step S606, the support information output unit 112 notifies the caregiver of the calculated minimum amount to be provided. The notification may be by voice using earphones such as a headset, or may be displayed on the display unit 214 of the mobile terminal device 210.

[0153] In step S607, the support information output unit 112 determines the timing of spooning food and the amount of food to be served. The timing of spooning food refers to the timing at which a mouthful of food on the spoon is placed in the mouth of the person being assisted. The amount of food served refers to the amount of food in one mouthful. The processing of step S607 is performed, for example, by using input data related to the chewing state of the person being assisted. The input data related to the chewing state may be, for example, information related to the state of the person being assisted's mouth, throat, facial expression, complexion, posture, changes in eating in response to verbal prompts, swallowing timing, time spent holding food in the mouth, eating rhythm, etc., and may be, for example, a captured image of the person being assisted. The input data related to the chewing state may also be information related to jaw movement, cheek movement, overall facial movement, and body movement, and may be, for example, sensor information from the motion sensor 410. The input data relating to the chewing state may also include voice data representing the quality and volume of the voice in response to prompts during meals, and information representing differences in timing and amount of past meals, seasonal differences, and differences due to physical condition. The information representing the eating rhythm may be captured images or sensor information from the motion sensor 410. Various modifications of the sensors used to acquire the above information are possible.

[0154] In step S608, the support information output unit 112 notifies the caregiver of the determined timing for serving food with a spoon and the amount to be served with the spoon. For example, in step S607, the support information output unit 112 determines whether the current timing is the timing for serving food with a spoon. If it is determined that it is the timing for serving food, the support information output unit 112 notifies that fact in step S608, and if it is determined that it is not the timing for serving food, the support information output unit 112 may notify that it is not the timing for serving food when the caregiver tries to serve food to the person being assisted if it is determined that it is not the timing for serving food.

[0155] Furthermore, for example, when it is determined that it is time to serve food, the support information output unit 112 may calculate the amount of food to be served on the spoon in step S607, and notify the caregiver of the calculated amount to be served in grams or in stages such as more, normal, or less in step S608. Alternatively, the support information output unit 112 may calculate support information indicating whether the amount of food served on the spoon is appropriate by using input data indicating the amount of food actually placed on the spoon in step S607. The input data in this case includes, for example, output from a camera capturing an image of the caregiver's hands. If the amount of food on the spoon is too much or too little, the support information output unit 112 may issue a notification in step S608 urging the caregiver to change the amount of food placed on the spoon.

[0156] The facial expression of the person being assisted may be used to determine whether the assistance provided by the caregiver is correct. For example, the support information output unit 112 may output no particular instruction when the person being assisted is smiling, assuming that the assistance is correct, but may output an instruction when the person being assisted has an unpleasant expression. For example, the support information output unit 112 may use, as input data in step S607, an image of the person being assisted's face or the results of facial expression determination processing based on the image. In this way, it is possible to determine whether the pace of eating is appropriate based on the facial expression of the person being assisted. Alternatively, the support information output unit 112 may determine the level of relaxation based on heart rate (pulse rate) analysis. The support information output unit 112 may output no particular instruction when the person being assisted has a high level of relaxation, assuming that the assistance is correct, but may output an instruction when the person being assisted has a low level of relaxation. For example, the support information output unit 112 may use, as input data in step S607, the heart rate, pulse rate, or information representing the analysis results thereof. Furthermore, the fact that facial expressions and relaxation levels may be used to determine whether assistance is appropriate also applies to steps other than S607 in FIG. 21 and to FIGS. 22 and 23 described later.

[0157] In step S609, the support information output unit 112 determines whether the person being assisted has finished eating. If not, the process returns to step S607. By repeating the processes of steps S607 and S608 in this way, it becomes possible to present to the caregiver the timing and amount of each bite of food to be served. As a result, it becomes possible for the person being assisted to eat at an appropriate pace.

[0158] If it is determined that the meal is finished, in step S610, the support information output unit 112 instructs the caregiver to record the meal result. For example, a captured image of the leftover food is acquired as the meal result. The instruction to record may instruct the caregiver to take an image using the mobile terminal device 210 or the like, or may be to automatically take an image by remotely controlling a camera placed in an appropriate position.

[0159] In step S611, the support information output unit 112 obtains support information indicating whether the person being assisted needs hydration. If hydration is necessary, in step S612, the support information output unit 112 issues a notification instructing the caregiver to hydrate. Note that the support information output unit 112 may obtain support information indicating a specific amount of hydration in step S611 and notify the amount of hydration in step S612.

[0160] If hydration is not required, or after the processing of step S612, the meal assistance sequence is ended.

[0161] 21 is an example of a meal assistance sequence, and various modifications of the specific sequence are possible. For example, when meal assistance is provided using a nursing bed 310, a control may be added to switch the nursing bed 310 to a meal mode suitable for eating (e.g., a mode in which the back bottom is raised to a set angle in the range of 30 to 90 degrees, a mode in which the knee bottom is raised to a set angle in the range of 0 to 30 degrees, a mode in which the foot bottom is lowered to a set angle in the range of 0 to 90 degrees, or a mode in which the bed inclination angle is set to a set angle in the range of 0 to 20 degrees so that the head side is higher). For example, the support information output unit 112 may obtain support information indicating whether the person being assisted and the served meal are in a state suitable for starting a meal by using the output of a camera or the like as input data. If it is determined that the person being assisted and the meal are appropriately set, the support information output unit 112 performs a notification process to inquire of the caregiver whether it is okay to move the nursing bed 310. If the caregiver answers "OK," the nursing bed 310 may be changed to the meal mode.

[0162] Furthermore, the support information output unit 112 can output various support information related to meal assistance to the caregiver or cook before the meal is even completed at the dining area.

[0163] 3.2.5 Toilet assistance 22 is a flowchart illustrating a specific assistance sequence for providing excretion assistance. When the excretion assistance sequence is started, in step S701, the support information output unit 112 performs control to turn on sensors necessary for supporting excretion assistance from among the sensor group 400 installed in the care facility or the like.

[0164] Next, in step S702, the support information output unit 112 determines whether the caregiver has moved to a position where excretion assistance can be provided. For example, if the person being assisted excretes in a diaper on the nursing bed 310, it is assumed that excretion assistance will be provided in the person's room. In this case, the processing of step S702 is performed by using, for example, output from a camera installed in the room, or output from a camera of the mobile terminal device 210 carried by the caregiver, output from an RFID tag, or the like as input data.

[0165] If the caregiver is not in position, in step S703, the support information output unit 112 waits for a certain period of time and then performs the process of step S702 again. If the caregiver is in position, in step S704, the support information output unit 112 requests support information regarding diaper removal. In step S705, the support information output unit 112 performs a notification process for the requested support information.

[0166] For example, if the posture of the person being assisted, the posture of the caregiver, or the direction in which the diaper is removed is inappropriate when removing the diaper, feces may stick to the clothing or sheets of the person being assisted, which is undesirable. Therefore, for example, in step S704, the support information output unit 112 may determine whether the movements of the caregiver when removing the diaper are appropriate. For example, the support information output unit 112 may obtain the correct movements and compare these movements with the actual movements of the caregiver. If the movements are determined to be inappropriate, in step S705, the support information output unit 112 may notify the caregiver that the movements are inappropriate or may specifically instruct the caregiver to make appropriate movements.

[0167] In step S706, the support information output unit 112 obtains support information related to wearing a diaper. In step S707, the support information output unit 112 performs a notification process of the obtained support information.

[0168] For example, in step S706, the support information output unit 112 may determine whether the caregiver's movements when putting on a new diaper are appropriate. For example, the support information output unit 112 may obtain the correct movements and compare the correct movements with the actual movements of the caregiver. If the movements are determined to be inappropriate, in step S707, the support information output unit 112 may notify the user that the movements are inappropriate or may specifically instruct the user to make appropriate movements.

[0169] While soiling sheets and other items when removing a diaper is a problem, since the caregiver is nearby, the caregiver can easily notice the stain and deal with it relatively easily. On the other hand, if fecal leakage occurs due to improper diaper placement, the caregiver may not be nearby when the leakage occurs. Furthermore, considering the burden on the caregiver, excessively increasing the frequency of excretion assistance is not easy, and there is a risk that fecal leakage will be left untreated for a long time. In light of the above, the support information output unit 112 may set conditions to make the notification in step S707 more likely than in step S705. For example, if the notification in steps S705 and S707 is to be issued when the degree of deviation between the correct answer and the actual movement exceeds a threshold, the threshold in step S707 is set smaller than the threshold in step S705.

[0170] Alternatively, in step S706, sensor information from a sensor provided in the diaper may be used as input data to determine in detail whether the diaper is being worn properly. In this case, too, it becomes possible to provide assistance that places more emphasis on putting on the diaper.

[0171] In step S708, the support information output unit 112 instructs to record the excretion state. Specifically, the support information output unit 112 instructs to capture an image of the state of urine and feces and to measure the weight of the urine and feces. Note that the weight measurement may be the weight measurement of the diaper, or the weight measurement of the waste if a waste bin is being carried. The recording instruction here may be to have an assistant capture the image or measure the weight, or may be to remotely control a camera or sensor.

[0172] 22 is an example of an excretion assistance sequence, and the specific sequence can be modified in various ways. For example, when excretion assistance is performed on a nursing bed 310, a control may be added to change the height of the nursing bed 310 to a height suitable for excretion assistance (for example, a height from the floor to the top surface of the bottom of 50 mm to 100 mm so that the caregiver does not need to bend over). For example, by changing the height of the nursing bed 310 when the caregiver responds "OK" in step S506 of Fig. 20, a state is realized in which it is easy to assist with excretion when the caregiver arrives. If the nursing bed 310 has a speaker, the support information output unit 112 may perform control to output a sound to explain the purpose of the height change to the person being assisted before the height change. The support information output unit 112 may also notify the caregiver that the height change of the nursing bed 310 has been completed.

[0173] 3.2.6 Transfer or mobility assistance 23 is a flowchart illustrating a specific assistance sequence for providing transfer assistance or movement assistance. When the transfer / movement assistance sequence is started, in step S801, the support information output unit 112 performs control to turn on sensors necessary for supporting transfer / movement assistance from among the sensor group 400 installed in the care facility or the like.

[0174] Next, in step S802, the support information output unit 112 determines whether a lift is necessary for assisting the person being assisted to transfer or move. The processing of step S802 is performed by inputting data such as the difference in physique between the caregiver and the person being assisted, the ADL of the person being assisted, the time required for transfer, and the inventory of lifts at the nursing facility.

[0175] If a lift is not required, the caregiver manually transfers the person being assisted into a wheelchair. In step S803, the support information output unit 112 obtains support information related to manual transfer. In step S804, the support information output unit 112 performs notification processing for the obtained support information. The support information output unit 112 may also issue a notification inquiring the caregiver as to whether or not it is necessary to lock the wheelchair before transfer. If the caregiver answers that it is necessary, the support information output unit 112 controls to lock the wheelchair. Alternatively, the support information output unit 112 may automatically determine whether or not locking is necessary, and if it is determined that it is necessary, may instruct the caregiver to lock the wheelchair before the processing of step S803.

[0176] For example, in step S803, the support information output unit 112 may determine whether the caregiver's use of their body during manual transfer is appropriate. For example, the support information output unit 112 may acquire correct movements, such as the posture of the caregiver and the position at which the person being assisted places their feet, and compare these movements with the actual movements of the caregiver. The movements of the caregiver may be detected using the motion sensor 410 or the image sensor 420. Furthermore, because the positional relationship between the person being assisted and the caregiver is important in transfer and mobility assistance, these sensors may detect the movements and posture of the person being assisted in addition to the movements of the caregiver. If the movements are determined to be inappropriate, in step S804, the support information output unit 112 may notify the user that the movements are inappropriate or may specifically instruct the user to make appropriate movements.

[0177] If a lift is needed, in step S805, the support information output unit 112 controls the lift to move to the person being assisted. In step S806, the support information output unit 112 obtains support information related to transfer using the lift. In step S807, the support information output unit 112 performs notification processing of the obtained support information.

[0178] For example, in step S806, the support information output unit 112 may determine whether the lift is being used appropriately. For example, the support information output unit 112 may acquire correct data, such as the state of attachment of a sling that allows the user to safely lift a person being assisted, and compare the correct data with the actual state. If the lift is determined to be inappropriate, in step S807, the support information output unit 112 may notify the user that the lift is inappropriate, or may specifically instruct the user on the appropriate attachment state.

[0179] After the person being assisted is lifted up by the lift, they may either be seated in a wheelchair or moved as is. The support information output unit 112 may determine which method to use, taking into consideration the availability of lifts, the destination, and the condition of the person being assisted, and notify the caregiver.

[0180] 23 is an example of a transfer / movement assistance sequence, and various modifications of the specific sequence are possible. For example, a control may be added to change the height of the nursing bed 310 (for example, the height from the floor to the top surface of the bottom of a nursing bed is 200 mm to 500 mm, which is a height that allows an unassisted person who can stand to firmly place their feet when sitting on the bed, a height of 200 mm to 500 mm, slightly higher than a wheelchair when transferring to a wheelchair, and a height of 200 mm to 500 mm, slightly lower than a wheelchair when transferring from a wheelchair to the bed, etc.) to a height suitable for transfer / movement assistance. The specific control is the same as that for excretion assistance, etc., so a detailed description will be omitted.

[0181] 3.2.7 Specific changes in care depending on factors Above, we have explained the specific sequences for assisting with meals, toileting, and transferring / moving. 21 to 23, each piece of support information may be obtained based on the presence or absence of abnormal behavior and the cause of the abnormal behavior. For example, as described above with reference to FIG. 13, the presence or absence of abnormal behavior and the result of determining the cause of the abnormal behavior are used as input data when obtaining support information.

[0182] For example, if the factor determination unit 111 determines that the cause is dementia, the support information output unit 112 assumes that dementia is progressing and changes the output of each assistance sequence. For example, in meal assistance, the support information output unit 112 outputs, as support information, information indicating the amount of food provided per mouthful (amount provided with a spoon) and information indicating the timing of providing each mouthful (timing of providing food with a spoon). In this case, if the support information output unit 112 determines that the behavior is abnormal behavior due to dementia, it may change at least one of the amount and the timing of providing food compared to when the behavior is determined not to be abnormal behavior due to dementia. This makes it possible to appropriately change the pace of eating for an assisted person with dementia and an assisted person without dementia. For example, the support information output unit 112 may reduce the amount of food provided or delay the timing of providing food. This makes it possible to appropriately manage the pace of eating for an assisted person who is prone to choking due to dementia.

[0183] Furthermore, the support information output unit 112 outputs, as support information, information specifying the timing of excretion assistance, which is the timing to start excretion assistance. In this case, if the behavior is determined to be abnormal behavior caused by dementia, the timing of excretion assistance may be changed compared to when the behavior is determined not to be abnormal behavior caused by dementia. In this way, it becomes possible to start the excretion assistance sequence at an appropriate timing depending on whether or not the person has dementia. For example, the support information output unit 112 may advance the timing of excretion assistance. By adjusting the timing of excretion assistance in this way, it becomes easier to maintain a clean state even when the person being assisted has difficulty controlling the timing of excretion due to dementia.

[0184] Additionally, when abnormal behavior is determined to be a dementia-related factor, the support information output unit 112 may change the support information so as to provide the following meal assistance. For example, when abnormal behavior is determined to be a dementia-related factor, the support information output unit 112 may set a high priority for notifications regarding the following. For example, the support information output unit 112 may issue notifications regarding the following when the abnormal behavior is a dementia-related factor, and may not issue the notifications regarding the following when there is no abnormal behavior or when the cause is other than dementia. Alternatively, the support information output unit 112 may issue the notifications regarding the following regardless of whether the abnormal behavior is a dementia-related factor, but may execute control to make the notifications more likely when the abnormal behavior is a dementia-related factor. For example, the support information output unit 112 may increase the frequency of notifications when the abnormal behavior is a dementia-related factor, or may relax the conditions for determining whether or not a notification is necessary. · Complete your excretion before meals so that you can concentrate on eating - Lets you know if you're getting enough sleep and feeling good After serving the food, observe without assistance and understand the situation today Prepare a comfortable environment, prepare tableware that you are comfortable with, and prepare tableware that you are attached to. ·Adjust your posture to make it easier to eat If the meal is not progressing, adjust the time. · Call out to the child to make sure they understand that it is a meal and assist them with the first bite · Talk to them and teach them how to eat - Provide fluids to prevent dehydration -Adjust the amount of spoonful and the speed at which you give it to avoid choking If you are not eating well, increase your activity level and adjust your daily routine.

[0185] Furthermore, when it is determined that the abnormal behavior is a dementia-causing behavior, the support information output section 112 may change the support information so that the following excretion assistance is provided. -Notifications on whether you've had enough sleep or are feeling well Select and use appropriate pants, diapers, and pads If you are using the toilet, check to make sure it is flushed. · Take measures to prevent falls as there is a possibility of more frequent trips to the toilet -Watch when it's time to defecate and guide them to the toilet by calling out to them - Because there is a possibility of fecal contamination, guide the child to the toilet and change their diaper based on the timing of their excretion. ·Assign compatible staff

[0186] The support information output unit 112 may also output support information relating to sleep assistance. When it is determined that the abnormal behavior is a dementia-causing behavior, the support information output section 112 may change the support information so that the following sleep assistance is provided. · Regulating your daily rhythm to keep your autonomic nervous system normal Exercise to increase your activity during the day -Watch over any abnormal behavior at night

[0187] When monitoring at night, for example, sensor information from a bed exit sensor or a monitoring sensor is used as input data. For example, the support information output unit 112 instructs a caregiver who does not provide breakfast assistance to introduce a sensor while the person being assisted is eating breakfast. Also, when exercising, the support information output unit 112 may notify the caregiver to suggest recreation or rehabilitation after providing daytime toilet assistance, for example.

[0188] 6, the factor determination unit 111 may be capable of determining whether the factor of abnormal behavior is an environmental factor or an excretion disorder factor. For example, when it is determined that the abnormal behavior is caused by an excretion disorder factor, the support information output unit 112 may change the support information so that the following assistance is provided. -Notify the addition of laxatives to dinner servings · Changes to meal contents (applies to breakfast, lunch, and dinner) -Instruct them to provide fluids after meals - Proposing recreation and rehabilitation after assisting with daytime toileting

[0189] Note that the support information output unit 112 may not only simply instruct the addition of a laxative, but may also suggest a specific type of laxative and a dosage time. For example, the support information output unit 112 may notify the type of laxative by using, as input data, information indicating the number of consecutive days a laxative has been administered, information on the bowel movement interval, etc. Furthermore, when a care recipient whose condition has been determined to be due to dementia is later determined to be due to an excretion disorder, the support information output unit 112 may instruct the caregiver to remove sensors other than the excretion sensor from among those arranged to address dementia.

[0190] Furthermore, when it is determined that the abnormal behavior is caused by an environmental factor, the support information output section 112 may change the support information so that the following assistance is provided. - Automatically control the rhythm of speakers and lighting to match the data before the environmental factors

[0191] By restoring the environment to a similar state to that before the abnormal behavior occurred, it becomes possible to regulate the daily rhythm of the person being assisted. Note that the caregiver may be able to temporarily stop the application of automatic control or change the settings to not apply automatic control. Furthermore, if the person being assisted has been determined to have dementia-related symptoms, and the symptoms are later determined to be environmentally related, the support information output unit 112 may instruct the caregiver to remove the sensors placed to address the dementia.

[0192] Furthermore, when a behavior is determined to be a dementia factor, the support information output unit 112 may increase the types of support information to be output compared to when the behavior is not determined to be an abnormal behavior. For example, the support information for "arranging the environment, preparing tableware that is relaxing, and preparing tableware that the user is attached to" described above is output when the behavior is determined to be a dementia factor, but may not be output when the behavior is determined to be another factor. In this case, for example, input data from a temperature sensor, humidity sensor, illuminance sensor, barometric pressure sensor, etc. may be used to determine a favorable environment for the person being assisted.

[0193] Therefore, when a behavior is determined to be a dementia cause, the support information output unit 112 may use more types of sensor information than when the behavior is not determined to be abnormal. In this way, the number of types of input data increases, making it possible to accurately obtain support information for providing assistance appropriate for dementia.

[0194] The support information output unit 112 may also determine whether a new sensor needs to be added based on information identifying one or more available sensors and sensor information added when a behavior is determined to be a dementia factor. The one or more available sensors are specifically sensors installed in the target nursing care facility, and are identified based on the third association information 125 in FIG. 16. As described above with reference to FIGS. 14 to 16, depending on the type of sensor installed in the nursing care facility, given support information may not be output with sufficient accuracy, and the support information may be set to "unable to output." Therefore, depending on the nursing care facility, even if the factor determination unit 111 determines that a dementia factor is present, it may be difficult to output support information appropriate for dementia. The information processing device may, for example, determine whether a sensor needs to be added and suggest adding a sensor that needs to be added or a device including the sensor. This makes it possible to appropriately output support information tailored to the factor.

[0195] As explained above, it is expected that appropriate assistance will vary depending on whether or not the person is exhibiting abnormal behavior and the cause of the abnormal behavior. According to the method of this embodiment, the results of determining the causes of the person being assisted are used when supporting the caregiver in providing assistance. As a result, it becomes possible for the caregiver to provide assistance that is more appropriate for the person being assisted.

[0196] Specifically, by digitizing the tacit knowledge of experienced caregivers, it is possible to enable less skilled caregivers to provide appropriate care. For example, since less skilled caregivers can provide care equivalent to that of skilled caregivers, the reproducibility of caregiving is improved. Furthermore, by reducing variation in care skills and facilitating organizational management, incidents such as falls by care recipients are reduced. As a result, the occurrence of empty beds due to hospitalization and overtime work associated with writing accident reports can be reduced. Furthermore, reducing incidents prevents caregivers from becoming overly sensitive to risk, which reduces stress and ultimately reduces turnover. Furthermore, by improving caregiver skills and the working environment, it is possible to increase the satisfaction of care recipients and their families and improve their quality of life (QOL).

[0197] Note that the information processing system 10, server system 100, caregiver device 200, etc. of the present embodiment may implement part or most of their processing using a program. In this case, a processor such as a CPU executes the program to implement the information processing system 10, etc. of the present embodiment. Specifically, a program stored in a non-transitory information storage medium is read, and the read program is executed by a processor such as a CPU. Here, the information storage medium (a computer-readable medium) stores programs, data, etc., and its functions can be implemented by an optical disk, a HDD, or memory (a card-type memory, a ROM, etc.). The processor such as a CPU then performs various processes of the present embodiment based on the program stored in the information storage medium. That is, the information storage medium stores a program for causing a computer to function as each part of the present embodiment.

[0198] Furthermore, the method of this embodiment can be applied to an information processing method that determines whether the behavior of the person being assisted is abnormal behavior caused by dementia based on (1) dementia level information of the person being assisted and (2) at least one of environmental information, excretion information, and sleep information of the person being assisted, and outputs support information to support the assistant in assisting the person being assisted based on the determination result and sensor information, which is the sensing result regarding the assistant providing assistance to the person being assisted or the assistant providing assistance to the person being assisted.

[0199] 4. Variations <Parallel processing of multiple assistance sequences> The assistance sequences described above with reference to Figures 21 to 23 may be executed sequentially. For example, a given caregiver may execute a sequence corresponding to one of Figures 21 to 23 by responding "OK" in step S506 of Figure 20 while in standby mode, and return to standby mode after completion. The standby mode refers to a state in which the target caregiver is not executing any assistance sequence. Then, by responding "OK" again in step S506, the sequence corresponding to one of Figures 21 to 23 is executed.

[0200] However, in nursing care facilities and the like, one caregiver may assist multiple people being assisted in parallel. For example, one caregiver may seat people being assisted A and people being assisted B close to each other, and assist them with eating at the same time. In this case, it would be inefficient to execute the meal assistance sequence of Fig. 21 for person being assisted B after completing the meal assistance sequence of Fig. 21 for person being assisted A.

[0201] Therefore, the support information output unit 112 may be able to execute multiple assistance sequences in parallel for one caregiver. In the above example, for example, the support information output unit 112 executes a meal assistance sequence for person A who is assisted and a meal assistance sequence for person B who is assisted in parallel. Note that although an example in which the ratio of caregivers to people assisted is 1:2 will be described here, one caregiver may be responsible for three or more people who are assisted at the same time.

[0202] For example, in the meal assistance sequence for person A, the support information output unit 112 performs the process of step S605 and reports the result in step S606 in a format such as "The minimum amount to be provided to person A is x grams." Similarly, in the meal assistance sequence for person B, the support information output unit 112 performs the process of step S605 and reports the result in step S606 in a format such as "The minimum amount to be provided to person B is y grams." In this way, the support information output unit 112 acquires input data for person A and person B in parallel, and outputs support information for person A and person B at the necessary timing based on the respective input data. In this way, even if there is a one-to-many relationship between the assistant and the person being assisted, it is possible for the assistant to provide the necessary assistance to each person being assisted. Note that by installing a wide-angle camera capable of simultaneously capturing images of multiple people being assisted, it is possible to share the input data for person A and person B.

[0203] However, since there is only one caregiver, it is not easy to respond to all of the support information even if it is notified at very close intervals. For example, when the notification in step S608 is made for person A being assisted, the caregiver takes the amount of food indicated by the notification with a spoon and brings it to the mouth of person A being assisted. Even if the notification in step S608 is made for person B being assisted before this is completed, it is difficult for the caregiver to take B's food with a spoon and bring it to B's mouth.

[0204] Alternatively, when assisting multiple people with meals, it is more efficient to have everyone gather in a dining area such as a cafeteria before the people are assisted to eat. Therefore, even if it is determined that the assistant and person A are in position (Yes in step S603), if person B is not in position, it may not be desirable to start the processing of steps S607-S609 etc. for person A with assistance.

[0205] Considering these points, the support information output unit 112 may execute processing that takes into account the relationship between the multiple assistance sequences, rather than simply executing assistance sequences for multiple persons being assisted in parallel. For example, when executing multiple assistance sequences in parallel for a given caregiver, the support information output unit 112 may control the execution and suspension of each assistance sequence.

[0206] For example, when the support information output unit 112 issues a notification in step S608 for person A who is assisted, it may suspend the meal assistance sequence for person B who is assisted. Then, when the caregiver has finished providing one mouthful of food to person A who is assisted, the meal assistance sequence for person B who is assisted is resumed. Because the support information output unit 112 has determined in step S607 that it is OK to provide a meal to person B who is assisted, in step S608 it issues a notification to the caregiver to have person B who is assisted take one mouthful of food. In this case, because the caregiver is currently performing an action for person B who is assisted, the support information output unit 112 performs processing to suspend the meal assistance sequence for caregiver A until the action is completed.

[0207] Alternatively, if the support information output unit 112 determines that the assisted person A has arrived at their position (Yes in step S603), it may suspend the meal assistance sequence for the assisted person A until it determines that all other assisted persons for whom the same assistant is responsible for meal assistance have arrived at their positions.

[0208] 24A is a state transition diagram illustrating the transition of an assistance sequence for a given caregiver. For example, the support information output unit 112 executes two meal assistance sequences to support a caregiver who is assisting persons A and B with meals. In this case, the support information output unit 112 executes a state transition based on a given condition. For example, when the support information output unit 112 is executing a meal assistance sequence for person A, if it determines that one unit of assistance by the caregiver has been completed, the support information output unit 112 stops the meal assistance sequence for person A and transitions to a state in which a meal assistance sequence for person B is being executed.

[0209] Alternatively, the support information output unit 112 may determine the priority of the support information to be notified in each assistance sequence. For example, suppose the support information output unit 112 determines that a notification to store the meal result (step S610) should be made for person A who is assisted because the person A has finished eating, and that a notification to provide one bite of food (step S608) should be made for person B who is assisted because the person B has not finished eating. While recording the meal result can be performed at any time up until the time of cleaning up, providing one bite of food is not complete for person B who is assisted until the time of cleaning up. Therefore, in this case, the support information output unit 112 may prioritize the execution of the meal assistance sequence for person B who is assisted and suspend the meal assistance sequence for person A who is assisted. This also makes it possible to achieve appropriate state transitions between multiple assistance sequences for multiple people who are assisted. Note that state transitions between multiple assistance sequences can be thought of as one assistance sequence interrupting another assistance sequence that is currently being executed.

[0210] Although the above describes an example in which two meal assistance sequences are executed in parallel, the method of this embodiment is not limited to this. Fig. 24B is another diagram illustrating state transitions between assistance sequences in this embodiment.

[0211] As shown in FIG. 24B , in this embodiment, various sequences, such as a meal assistance sequence, an excretion assistance sequence, a transfer assistance sequence, and an abnormality response sequence, may be executed in parallel. In this case, the support information output unit 112 may control the transition between each assistance sequence shown in FIG. 24B . Note that while FIG. 24B shows an example in which a standby state is used when transitioning from a given type of assistance sequence to another type of assistance sequence, direct transitions may also be made between each assistance sequence. Furthermore, as shown in FIG. 24A , a meal assistance sequence may include multiple assistance sequences. Similarly, other assistance sequences, such as an excretion assistance sequence, may include multiple assistance sequences.

[0212] For example, suppose that while a given caregiver is providing a meal to person A who is assisted, person A becomes abnormal. The abnormal state may be, for example, choking. In this case, the caregiver stops assisting person A with eating and takes action to deal with the abnormality. For example, the support information output unit 112 executes in the background a determination as to whether to start an abnormality response sequence, similar to steps S501 to S503 in FIG. 20, and starts the abnormality response sequence when an abnormality in person A who is assisted is detected. Note that, although the processing of steps S505 to S506 in FIG. 20 may be executed, the processing of steps S505 to S506 may be omitted, considering that the person in charge of person A who is assisted is the same as the person in charge of meal assistance and that there is a possibility of a high urgency.

[0213] As a result, the abnormality response sequence is added to the assistance sequences to be executed. The support information output unit 112 then suspends the currently executing meal assistance sequence and starts executing the abnormality response sequence. When it is confirmed that the abnormality has been resolved by the abnormality response sequence, the support information output unit 112 transitions to another assistance sequence, such as resuming the suspended meal assistance sequence.

[0214] Alternatively, while a given caregiver is feeding person A, person A may need to go to the toilet. In this case, an excretion assistance sequence is added to the assistance sequences to be executed. Also, depending on the ADL of person A and the location of the toilet, a transfer assistance sequence may be necessary. For example, the support information output unit 112 suspends the meal assistance sequence, first executes a transfer assistance sequence to move person A to the toilet, then executes the excretion assistance sequence, and resumes the suspended meal assistance sequence after completion.

[0215] There are various possible factors that can cause changes in the required assistance, such as the initiative of the person being assisted, the physical condition of the person being assisted, illness such as dementia, medication, the environment, the season, external factors, discrepancy between the progress of care on that day and the schedule, etc. For example, the support information output unit 112 may perform processing to detect these factors and determine the assistance sequence to which the transition will be made based on the detected factors and the currently executed assistance sequence.

[0216] In this way, the support information output unit 112 can appropriately respond to various situations by executing multiple assistance sequences in parallel and controlling state transitions between the multiple assistance sequences. For example, as described above, even in a one-to-many relationship between an assistant and an assisted person, it can support the decision of the assistance to be performed and the order in which to perform the assistance. Since it is possible to reduce the burden on the assistant, it is possible to reduce the risk of incidents such as aspiration or falls by the assisted person. Furthermore, even if other assistance is suddenly required while performing a given assistance, the assistant can appropriately support the assistance that should be performed at that time, thereby reducing the burden on the assistant and the risk to the assisted person.

[0217] <Data added by user> In the above description, it is assumed that the NN 122 for outputting support information is created by the learning unit 114. For example, a provider of an information processing device may select a given nursing care facility or the like for learning in advance and create the NN 122 for outputting support information using data from the nursing care facility or the like. When a new nursing care facility that uses a service provided by an information processing device is added, for example, the existing NN 122 for outputting support information is commonly used.

[0218] However, the method of this embodiment is not limited to this, and new training data may be added by users of nursing care facilities, etc., and additional machine learning may be performed using the training data.

[0219] For example, data from multiple nursing care facilities may be used for machine learning while maintaining a common support information output NN 122. In this case, training data can be collected from multiple nursing care facilities, which has the advantage of making it easier to increase the amount of training data.

[0220] Alternatively, additional machine learning may be performed for each nursing care facility. In this case, the NN 122 for outputting support information is updated for each nursing care facility. In other words, the NN 122 for outputting support information can be specialized for the nursing care facility in question.

[0221] FIG. 25A is an example of a screen displayed on the display unit 214 of the mobile terminal device 210, for example. Compared to Fig. 17, an object OB4 for adding data has been added. When the caregiver performs a selection operation for object OB4, the screen transitions to the screen in Fig. 25B.

[0222] Figure 25B includes an area RE1 that displays the name of the support information to which training data is to be added, and an area RE2 in which the assisted person ID, the caregiver ID, and output data can be input. The assisted person ID is information that identifies the assisted person. The caregiver ID is information that identifies the caregiver. Output data is information that corresponds to the output of the NN 122 for outputting support information. Figure 25B targets the timing of diaper changes, so an example is shown in which time is used as the output data. However, the format of the output data can be modified in various ways depending on the type of support information, and may be an image, audio, numerical value, binary data representing true or false, or some other format.

[0223] The example in FIG. 25B shows that when a caregiver with the caregiver ID abcde assisted the toileting of a care recipient with the care recipient ID 12345, the caregiver determined that the time 2021 / MM / DD hh:mm:ss was appropriate for changing the diaper. Furthermore, separate from the caregiver's operation, input data corresponding to the diaper change timing was acquired at the nursing facility. That is, a data set associating the input data with the output data 2021 / MM / DD hh:mm:ss can serve as training data for the support information output NN 122, which outputs the diaper change timing.

[0224] However, in this embodiment, it is assumed that the tacit knowledge of experienced caregivers is converted into data and that appropriate care is provided regardless of the caregiver's level of proficiency. Therefore, even if the above data set is acquired through input by a given caregiver, it is unclear whether it is positive data or negative data. Positive data refers to a data set in which appropriate correct answer data is associated with the input data, and negative data refers to a data set in which inappropriate correct answer data is associated with the input data.

[0225] Therefore, the learning unit 114 may store, for example, association information that associates an assistant ID with the skill level of the assistant. The skill level may be manually input by the administrator of the nursing care facility, or may be automatically determined based on the years of experience, qualifications held, past nursing care history, etc. The learning unit 114 regards a data set of highly skilled assistants as positive data and a data set of less skilled assistants as negative data.

[0226] Alternatively, even when an expert provides care, there are cases where the expert provides care according to the manual and cases where the expert adjusts the method of care based on his or her own intuition. The expert's tacit knowledge is likely used when the expert acts according to intuition. Therefore, as shown in FIG. 25B, area RE2 on the display screen may be configured to allow input of whether or not intuition was used. For example, when determining the timing of a diaper change, the caregiver inputs whether or not intuition was used in area RE2. The learning unit 114 uses the data set in which the corresponding input is "yes" as positive data.

[0227] The learning process after acquiring the training data is the same as the example described above using Fig. 8, and therefore a detailed description will be omitted. In the example of Fig. 25B, by updating the support information output NN 122 that outputs the timing of diaper change, it becomes possible to output a more accurate timing of diaper change. Note that although an example regarding the timing of diaper change has been described above, adding training data is similarly possible for other support information.

[0228] <Custom support information> 43 to 45 have been shown above as examples of support information that can be output. However, as can be seen from the above explanation, there are many different types of support required for caregiving, and the support required may differ depending on the care facility or the caregiver. Therefore, there may be cases where support information of a type that is not included in existing support information is required. Therefore, in this embodiment, the caregiver may be able to add any custom support information.

[0229] For example, in FIG. 25B, the name of the support information displayed in area RE1 is not fixed, but may be editable by the caregiver. The caregiver inputs the name of the desired custom support information using text such as "When to do XXXX." "XXXX" is text that represents, for example, the specific assistance action that the caregiver will perform. The caregiver also inputs the caregiver ID, the assisted person ID, output data, whether or not they used intuition, and so on, when they perform the assistance action corresponding to "XXXX." As a result, the output data and information indicating whether the output data is positive or negative are acquired as part of the training data for the support information output NN122 that outputs "When to do XXXX."

[0230] Furthermore, the information processing device may perform control to display a screen for identifying input data from the training data on the display unit 214 of the mobile terminal device 210. Fig. 25C is an example of a display screen for identifying input data. The screen shown in Fig. 25C includes an area RE3 that displays the name of the custom support information, and an area RE4 in which the names of devices already installed in the target nursing care facility and the names of input data that can be acquired by the devices can be selected.

[0231] For example, a sleep scan, such as the sensing device 450 shown in FIG. 2D, can detect heart rate, respiration rate, and activity. The caregiver selects the data available on the device to use as input data for requesting custom support information. FIG. 25C shows an example in which the caregiver selects the respiration rate from the sleep scan and images from a bedside camera as input data, but not the output of a pulse oximeter.

[0232] 25C, the name of the custom support information is associated with the input data used to output the custom support information. Association information representing this association is transmitted to the server system 100 and stored in the storage unit 120.

[0233] The memory unit 120 of the server system 100 stores time-series respiratory rates and time-series bedside camera images collected from a target nursing care facility. Therefore, the learning unit 114 extracts, as input data, the respiratory rates and camera images corresponding to the output data acquired using FIG. 25B. For example, the server system 100 stores the timing of acquiring the output data based on FIG. 25B, and reads out from the memory unit 120 the respiratory rates and camera images for a predetermined period set based on the acquisition timing. Then, the learning unit 114 performs a learning process on the support information output NN 122 for outputting customized support information, based on training data that associates the read input data with the output data.

[0234] 25C , an object OB5 for performing a learning start operation may be displayed on the display unit 214 of the mobile terminal device 210. When it is detected that the caregiver has performed a selection operation on the object OB5, the learning unit 114 performs the above-described learning process. As a result, a new support information output NN 122 that outputs custom support information is created. The learning process is the same as the example described above, and therefore a detailed description will be omitted. Furthermore, in machine learning of custom support information, it is sufficient to be able to acquire training data that associates input data with output data, and the user interface is not limited to the one described above.

[0235] Note that the structure of the NN in this case can be modified in various ways. Fig. 26 is a diagram showing an example of the structure of a general-purpose NN. The NN shown in Fig. 26 includes CNN1 that extracts features using image data as input, CNN2 that extracts features using audio data as input, a vector conversion NN that extracts features using text data as input, and CNN3 that extracts features using other sensor information as input. The NN in Fig. 26 also includes a DNN (Deep Neural Network) that accepts outputs from CNN1, CNN2, vector conversion NN, and CNN3, and outputs custom support information.

[0236] The NN shown in FIG. 26 can accept images, audio, text, and other sensor information as input. There are various possible patterns of input data for custom support information, as shown in FIG. 25C, for example, but it is possible to appropriately accept the input data in any pattern. Note that if image data is not selected as input data, the input of CNN1 is treated as 0. The same applies if audio data, text data, or other sensor information is not selected as input data, and the input of the corresponding NN among CNN2, vector conversion NN, and CNN3 is treated as 0.

[0237] FIG. 25D is an example of a screen displayed on the display unit 214 of the mobile terminal device 210 after machine learning is complete. The display screen of FIG. 25D displays, for example, the accuracy rate obtained using validation data during the learning process. In the example of FIG. 25D, the caregiver can select whether or not to output custom support information using the learning results. For example, if the caregiver selects yes in response to the question "Do you want to apply?", the custom support information can be output. For example, as in the example described above in FIG. 17, by setting the custom support information "Timing for doing XXXX" to active, the custom support information will be output. On the other hand, if the caregiver selects no, the custom support information will not be output.

[0238] In addition, the caregiver may think that the custom support information cannot be adopted as is because the accuracy rate is low, but would like to use it because the custom support information is important. In this case, it may be possible to request analysis processing from the administrator or provider of the information processing device. For example, if the caregiver selects yes in response to the question "Do you want to request analysis?", processing to change the support information output NN 122 that outputs the custom support information is executed on the server system 100 side.

[0239] For example, if the original accuracy rate is equal to or lower than a predetermined threshold, the learning unit 114 of the server system 100 may try to improve the accuracy rate by changing the structure of the NN. This is because the NN shown in Fig. 26 has a configuration that takes versatility into consideration, as described above, and therefore, by making the structure more specialized for custom support information, the accuracy rate may be improved. If the original accuracy rate exceeds a predetermined threshold, the learning unit 114 may skip the process of changing the NN 122 for outputting support information.

[0240] For example, when there are multiple NNs with different structures as the support information output NN 122 as shown in FIG. 10, the learning unit 114 may classify the multiple NNs into several classes.

[0241] FIG. 27 is a diagram illustrating the classification process of an NN. For example, the learning unit 114 obtains n-dimensional features by performing text mining processing using text representing the names of support information that is output, and performs clustering based on the n-dimensional features. For ease of explanation, FIG. 27 illustrates a two-dimensional feature plane, but n may be 3 or more. For example, when targeting an NN that outputs "when to change a diaper" among the support information output NNs 122, words such as "diaper," "change," and "timing" are extracted, and n-dimensional features of the NN that outputs "when to change a diaper" are obtained based on the extraction results.

[0242] Furthermore, the clustering method is not limited to text mining processing, and the learning unit 114 may cluster multiple NNs by performing analysis processing such as logistic regression analysis. Furthermore, the learning unit 114 may manually assign clustering results to some of the multiple NNs shown in Fig. 10 and use the results to cluster the remaining NNs. In this way, the accuracy of the clustering process can be improved.

[0243] In the example of FIG. 27, of the multiple NNs held by the server system 100, NN1 to NN3 are classified into class 1, NN4 to NN7 are classified into class 2, and NN8 to NN10 are classified into class 3. The learning unit 114 similarly calculates n-dimensional features based on the name of the custom support information to determine which class the information belongs to. For example, the learning unit 114 extracts words such as "XXXX" and "timing" from the name of the custom support information "Timing to do XXXX," and calculates n-dimensional features corresponding to the custom support information based on the extraction results. The learning unit 114 determines the structure of the NN to be used for learning based on the clustering results of the custom support information.

[0244] For example, as shown in FIG. 27, assume that the custom support information is classified into class 1. In this case, the learning unit 114 selects one of NN1 to NN3 and creates an NN for the custom support information using the structure of the selected NN and the training data for the custom support information described above. In this case, only the structure of the original NN may be used, and all weights may be newly calculated. Alternatively, transfer learning may be performed, in which some of the weights of the original NN are used as is. For example, the learning unit 114 performs machine learning using the structure of each of NN1 to NN3 and the training data for the custom support information, and calculates the accuracy rate of the trained model. The learning unit 114 then presents the highest accuracy rate to the caregiver, as in FIG. 25D, and asks whether or not to apply it. If the caregiver responds yes, the corresponding NN 122 for outputting support information is stored in the storage unit 120, making it possible to output the custom support information.

[0245] When additional machine learning is performed, the relationship between the training data accumulation period, in other words, the period during which the data to be analyzed is acquired, and the ADL of the person receiving care becomes important. For example, suppose a person receiving care who was previously able to move independently falls and breaks a bone, requiring assistance in a wheelchair. When ADL changes significantly in this way, the assistance appropriate for that person before and after the change will differ significantly. Therefore, for example, learning results based on training data before the ADL change may not be useful after the ADL change.

[0246] Therefore, although not shown in FIG. 25C , when performing a learning start operation, it may be possible to input not only the type of input data but also the analysis period. The caregiver specifies a period during which the ADL of the target person being assisted is considered to be at the same level as the current level. In this way, the NN 122 for outputting support information, which is the learning result, will have content corresponding to the current ADL of the person being assisted, making it possible to provide appropriate assistance. Furthermore, it is assumed that the server system 100 collects the ADL of the person being assisted, for example, as one of the input data. Therefore, when a learning start operation is performed, the learning unit 114 may acquire time-series changes in the ADL of the target person being assisted, and automatically set the analysis period based on the time-series changes in the ADL.

[0247] Although the present embodiment has been described in detail above, those skilled in the art will readily understand that many modifications are possible without substantially departing from the novel features and advantages of the present embodiment. Therefore, all such modifications are intended to be included within the scope of the present disclosure. For example, a term described at least once in the specification or drawings together with a different term having a broader or equivalent meaning may be replaced with that different term anywhere in the specification or drawings. Furthermore, all combinations of the present embodiment and modifications are also intended to be included within the scope of the present disclosure. Furthermore, the configurations and operations of information processing systems, server systems, mobile terminal devices, and the like are not limited to those described in the present embodiment, and various modifications are possible.

[0248] [Additional Notes] The information processing device of this embodiment includes a factor determination unit that determines whether the behavior of the person being assisted is abnormal behavior caused by dementia based on (1) dementia level information of the person being assisted and (2) at least one of environmental information, excretion information, and sleep information of the person being assisted, and a support information output unit that outputs support information to support the assistant in assisting the person being assisted based on the determination result of the factor determination unit and sensor information that is a sensing result regarding the assistant assisting the person being assisted or the person being assisted. [Explanation of symbols]

[0249] 10...information processing system, 100...server system, 110...processing unit, 111...factor determination unit, 112...support information output unit, 113...setting unit, 114...learning unit, 120...storage unit, 121...NN for factor determination, 122...NN for support information output, 123...first association information, 124...second association information, 125...third association information, 130...communication unit, 200...caregiver device, 210...portable terminal device device, 211...processing unit, 212...storage unit, 213...communication unit, 214...display unit, 215...operation unit, 220...wearable device, 300...care device, 310...care bed, 320...lift, 400...sensor group, 410...motion sensor, 420...imaging sensor, 430...odor sensor, 440, 450...sensing device, NW...network, OB1-OB5...object, RE1-RE4...area

Claims

1. a factor determination unit that, when the behavior of the person being assisted is determined to be abnormal, determines whether the abnormal behavior is a dementia factor and whether the abnormal behavior is an excretion disorder factor based on (1) dementia level information of the person being assisted and (2) at least one of environmental information, excretion information, and sleep information of the person being assisted; a support information output unit that outputs support information to support the caregiver in assisting the person being assisted, based on the determination result of the factor determination unit and sensor information that is a sensing result related to the caregiver assisting the person being assisted or the person being assisted; An information processing device comprising:

2. In claim 1, The support information is an information processing device that includes information to support at least one of meal assistance to assist the assisted person with eating, excretion assistance to assist the assisted person with excretion, and transfer / movement assistance to assist the assisted person with transferring or moving.

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