Bed equipment

Abstract

To provide a bed device, etc., that can perform appropriate processing according to the user's condition on the bed device. [Solution] A bed device comprising a detection unit for detecting the load of a user on the bed device and a control unit, wherein the control unit sets a first region and a second region on the surface on the bed device where the user is located, determines the current center of gravity position of the user on the bed device from the load, calculates the amount of movement between the current center of gravity position and the past center of gravity position, predicts that the user will assume a seated position based on the current center of gravity position and the amount of movement when the current center of gravity position is in the first region, and predicts that the user will assume a seated position based on the current center of gravity position when the current center of gravity position is in the second region.

Description

Technical Field

[0001] This embodiment relates to a bed device and the like.

Background Art

[0002] Generally, a system for acquiring the behavior of a user on a bed device and the state of the user is known.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present disclosure is to provide a bed device and the like that can perform appropriate processing according to the state of a user on the bed device.

Means for Solving the Problems

[0005] The bed device of the present disclosure is a bed device including a detection unit that detects the load of a user on the bed device and a control unit, wherein the control unit sets a first region and a second region on the surface where the user is on the bed device, determines the current center of gravity position of the user on the bed device from the load, calculates the movement amount between the current center of gravity position and the past center of gravity position, and when the current center of gravity position is in the first region, predicts that the user is in a sitting position based on the current center of gravity position and the movement amount, and when the current center of gravity position is in the second region, predicts that the user is in a sitting position based on the current center of gravity position.

Effects of the Invention

[0006] According to this disclosure, it will be possible to provide a bed device, etc., that can perform appropriate processing according to the user's condition on the bed device. [Brief explanation of the drawing]

[0007] [Figure 1] This is a diagram showing the entire system in the first embodiment. [Figure 2] This figure shows (a) an example of the configuration of the bed device and (b) an example of the arrangement of the load detection device in the first embodiment. [Figure 3] This figure shows an example of the hardware configuration of the bed device in the first embodiment. [Figure 4] This figure shows an example of the hardware configuration of the server device in the first embodiment. [Figure 5] This figure shows an example of the hardware configuration of the terminal device in the first embodiment. [Figure 6] This figure shows an example of the software configuration in the first embodiment. [Figure 7] This figure shows the processing flow for edge seating position prediction processing in the first embodiment. [Figure 8] This figure illustrates the region on the bed device in the first embodiment. [Figure 9] This figure illustrates (a) a region on the bed device and (b) a region on the bed device in the first embodiment. [Figure 10] This figure illustrates (a) the relationship between the center of gravity position and the determination process in the first embodiment, (b) the relationship between the center of gravity position and the determination process, and (c) the relationship between the center of gravity position and the determination process. [Figure 11] This figure shows the processing flow of the first prediction process in the first embodiment. [Figure 12] This figure shows the processing flow of the second prediction process in the first embodiment. [Figure 13] This figure shows the processing flow of the threshold change process in the first embodiment. [Figure 14]A diagram showing an example of a priority table in the first embodiment. [Figure 15] A diagram showing (a) the relationship between a threshold value and a determination value, (b) the relationship between a threshold value and a determination value, and (c) the relationship between a threshold value and a determination value in the first embodiment. [Figure 16] A diagram showing (a) the relationship between a threshold value and a determination value, (b) the relationship between a threshold value and a determination value, and (c) the relationship between a threshold value and a determination value in the first embodiment. [Figure 17] A diagram showing (a) the relationship between a threshold value and a determination value, and (b) the relationship between a threshold value and a determination value in the first embodiment. [Figure 18] A diagram showing (a) an example of a display screen, (b) an example of a display screen, and (c) an example of a display screen in the first embodiment. [Figure 19] A diagram showing the processing flow of weight measurement processing in the first embodiment. [Figure 20] A diagram for explaining the area on the bed device in the first embodiment. [Figure 21] A diagram showing the processing flow of measurement condition setting processing in the first embodiment. [Figure 22] A diagram showing an example of a measurement condition setting table in the first embodiment. [Figure 23] A diagram showing an example of an abnormality determination table in the first embodiment. [Figure 24] A diagram showing the processing flow of notification processing in the first embodiment. [Figure 25] A diagram showing an example of a display screen in the first embodiment. [Figure 26] A diagram showing an example of a display screen in the first embodiment. [Figure 27] A diagram showing (a) an example of a display screen, (b) an example of a display screen, and (c) an example of a display screen in the first embodiment. [Figure 28] A diagram showing (a) an example of a display screen and (b) an example of a display screen in the first embodiment. [Figure 29]This figure shows (a) an example of a display screen, (b) an example of a display screen, and (c) an example of a display screen in the first embodiment. [Figure 30] This figure shows (a) an example of a display screen and (b) an example of a display screen in the first embodiment. [Figure 31] This figure shows the processing flow of the automatic memory control process in the second embodiment. [Figure 32] This figure shows the processing flow of the memory information update process in the second embodiment. [Figure 33] This figure shows the processing flow of the m-minute prior n-minute average update process in the second embodiment. [Figure 34] This figure shows the processing flow of the load change determination process in the second embodiment. [Figure 35] This figure shows the processing flow for determining the center of gravity position in the second embodiment. [Figure 36] This figure shows the processing flow of the memory type discrimination process in the second embodiment. [Figure 37] This figure shows the processing flow of the memory type discrimination process in the second embodiment. [Modes for carrying out the invention]

[0008] The following describes one possible implementation of the system, bed device, terminal device, processing device, etc., of this disclosure with reference to the drawings. It should be noted that the contents of this disclosure are merely one example of an implementation and are not limited to the disclosed numerical values ​​or configurations, but also include equivalents that a person skilled in the art could conceive.

[0009] Technologies are known that provide various notifications based on the user's condition while they are in a bed. Firstly, a system is known that estimates the user's behavior based on the user's movements, detects premonitory movements that may lead to sitting on the edge of the bed or getting out of bed, and notifies medical professionals such as doctors and nurses, caregivers, and family members providing care (hereinafter collectively referred to as "staff").

[0010] Generally, systems that detect a user's premonitory movements are based on the position of the center of gravity on the bed device. Possible methods for determining these premonitory movements include using both the relative and absolute changes in the center of gravity position.

[0011] One possible method of detection using absolute change is to determine if a premonitory movement occurred when the center of gravity falls within a specific range, such as the edge of the bed device. Another method of detection using relative change is to calculate the amount of movement, velocity, and direction from the amount of change after n seconds, and determine if a premonitory movement occurred when these values ​​exceed a threshold.

[0012] However, simply using each judgment method may not always be sufficient for accurate determination. For example, when trying to anticipate potential dangers to users and identify potentially dangerous behaviors as quickly as possible, changing the threshold to make the detection range for predictive behaviors more sensitive makes it easier to detect movements that should be suppressed, but it also detects movements that should be mitigated, leading to increased workload for staff.

[0013] Secondly, when measuring a user's weight on a bed, the general method is the same as with commercially available scales: the system is initially set to its baseline in an unloaded state before the user lies on the bed, and the increased weight after the user lies on the bed is then measured as their weight.

[0014] However, in environments like nursing homes, many residents get on and off scales, so it is the staff, not the residents themselves, who check the results. As a result, the same staff member cannot check the measurement results for the same resident each time, making it difficult to distinguish whether the weight change is due to different conditions or timing of measurement, or to a change in the resident's physical condition.

[0015] Thus, when staff checked the records of changes in users' weight, they sometimes mistakenly set a menu with lower nutritional value because, for example, the weight of a user who was nutritionally adequate appeared higher than the previous time. Also, the inability to obtain accurate weight data sometimes affected the accuracy of nutritional care indicators in the care setting. Furthermore, the frequent inclusion of such noise in the weight data added a burden to staff's work, as they had to revise nutrition care plans more than necessary, even though the measured weight was inaccurate.

[0016] In the following embodiments, a system including a bed device for solving one or more of the above-mentioned problems will be described with reference to the figures. Note that the following embodiments are merely examples of the invention described in the claims, and the technical scope of the present invention is not limited to the following embodiments. Furthermore, the following embodiments describe the application to a bed device having one or more bottoms as a device for measuring the user's weight, but the invention is not limited to such a bed device. For example, the bed device of this disclosure may be applied to a stretcher or a bed device used in an ICU. Also, while this disclosure describes a bed device as an example, any device capable of measuring the user's weight using a load sensor is acceptable, and it can be applied to devices such as wheelchairs and examination tables.

[0017] [1. First Embodiment] The first embodiment will be described below.

[0018] [1.1 About the System] [1.1.1 System Overview] Figure 1 is a diagram illustrating the overview of System 1. For example, User System 2 includes equipment used by the user. For example, it includes a mattress M used by user U and a bed device 10 on which the mattress M is placed. The bed device 10 is equipped with a general control device (communication box), etc., and may be connected to a network NW via the control device.

[0019] Furthermore, user U can use, for example, terminal device 30. Also, the bed device 10 and terminal device 30 can communicate with server device 20 via network NW.

[0020] Furthermore, the staff system 3 includes a processing unit 40 and / or terminal device 50 for use by the staff. There may be one or more processing units 40. Also, there may be, for example, one terminal device 50 for each staff member.

[0021] In the case of a facility or hospital, for example, multiple user systems may be connected to a network NW. The staff system 3 may have one or more processing units 40 and one or more terminal devices 50.

[0022] In this embodiment, the processing unit 40 or terminal device 50 may implement the processing described herein by installing a dedicated application, or it may implement the processing via a user interface (e.g., a web page) provided by the server device 20.

[0023] [1.1.2 Overview of Bed Equipment] The configuration of the bed device 10 will be explained with reference to the schematic diagram in Figure 2(a). The bed device has a lifting mechanism B24 between the upper frame B20 and the middle frame B21. The lifting mechanism B24 has, for example, an actuator and can be implemented by a link mechanism. The height of the upper frame B20 is adjusted by the operation of the actuator.

[0024] Furthermore, the lifting mechanism B24 can also have the head and foot ends of the upper frame B20 at different heights. This allows the lifting mechanism B24 to tilt the upper frame B20.

[0025] Furthermore, the upper frame B20 has multiple bottoms. In Figure 2, it has a back bottom B10, a waist bottom B12, a knee bottom B14, and a foot bottom B16. Each bottom swings around a pivot point when a drive device such as an actuator is connected to it. For example, the back bottom B10 can perform back-raising and back-lowering movements. The knee bottom B14 can perform knee-raising and knee-lowering movements, and the foot bottom B16 can perform foot-raising and foot-lowering movements. The lower frame B22 is also equipped with casters B32 that can be used when moving the bed device 10.

[0026] Furthermore, this embodiment includes multiple load detection devices B30. For example, a load detection device B30 may be installed between the middle frame B21 and the lower frame B22. Alternatively, a load detection device B30 may be installed on a part of the lower frame B22. For example, by using a load detection device B30 as a load cell and installing it in a position where the distortion of the lower frame B22 can be detected, the load on the bed device 10 (the load on the lower frame B22) can be detected.

[0027] Alternatively, the load detection device B30 may be placed below the caster B32. In other words, the load detection device B30 should be installed in a location where it can detect the load of the user U on the bed device 10 and the upper frame B20 of the bed device 10 (on the mattress M).

[0028] Here, it is preferable to provide multiple load detection devices B30 in order to detect the center of gravity. For example, Figure 2(b) is a schematic diagram showing a state in which load detection devices B30 are arranged near the corners of the bed device 10, and is a view of the bed device 10 from below (lower frame B22 side). Load detection devices B30 are provided at the four corners of the lower frame B22, top, bottom, left, and right. For example, load detection device B30A is provided on the right side of the head, load detection device B30B is provided on the left side of the head, load detection device B30C is provided on the right side of the feet, and load detection device B30D is provided on the left side of the feet. By providing four load detection devices B30, the coordinates of the center of gravity can be determined.

[0029] For example, loads of M1 [kgf] were detected from load detection device B30A, M2 [kgf] from load detection device B30B, M3 [kgf] from load detection device B30C, and M4 [kgf] from load detection device B30D. Here, if the center of gravity is (XG, YG) [mm] and the distance from the center of gravity to the load detection device is (a, b) [mm], then the center of gravity is: XG=a×(-M1-M2+M3+M4) / (M1+M2+M3+M4) YG=b×(-M1+M2-M3+M4) / (M1+M2+M3+M4) It can be calculated using this method.

[0030] [1.1.3 Terminology] Hereinafter, the terms used in this specification are defined as follows:

[0031] A user is a person who is lying down, sitting, or otherwise in bed on the bed device 10 (the mattress placed on the bed device 10). For example, in a nursing facility, it would refer to a person receiving care, and in a hospital, it would refer to a patient. If used at home, it would refer to a person who is in bed on the bed device 10 at home. Users may be, for example, sick people or the elderly, but they may also be children, young people, or healthy individuals.

[0032] Staff members are those who assist, treat, and support users. For example, in a hospital, this includes medical professionals such as doctors, nurses, physical therapists, and occupational therapists; in a facility, it includes care managers, certified care workers, and home helpers; and in a home setting, it includes family members who provide care. In short, staff members are those who support users.

[0033] [1.2. Hardware Configuration] [1.2.1 Bed Equipment] Figure 3 is a diagram illustrating the hardware configuration of the bed device 10.

[0034] The control unit 100 controls the entire bed device 10. The control unit 100 realizes various functions by reading and executing various programs stored in the memory unit 110 (e.g., ROM 110A, storage 110C). The control unit 100 may be implemented by one or more control devices / arithmetic units (CPU (Central Processing Unit), SoC (System on a Chip)). Alternatively, the control unit 100 may be composed of a control circuit.

[0035] The memory unit 110 is one or more storage devices that store necessary data or programs. The memory unit 110 temporarily or permanently stores data and programs. For example, the memory unit 110 includes a ROM 110A, a RAM 110B, and a storage device 110C.

[0036] ROM110A is a non-volatile memory that can retain programs and data even when the power is turned off.

[0037] RAM110B is the main memory primarily used by the control unit 100 during processing. RAM110B is a rewritable memory that temporarily holds data including programs read from ROM110A and storage 110C, as well as execution results.

[0038] Storage 110C is a non-volatile storage device capable of storing programs and data. For example, it may consist of storage devices such as HDDs (Hard Disk Drives) or SSDs (Solid State Drives). Alternatively, Storage 110C may be configured as an externally connectable USB memory stick. Furthermore, Storage 110C may be, for example, a storage area located in the cloud.

[0039] The display unit 120 is a display device capable of showing the user's weight and the status of the bed equipment. The display unit 120 may be, for example, a liquid crystal display (LCD) or an organic electroluminescent (OLED) display, or other device capable of displaying images. Alternatively, the display unit 120 may be a light-emitting element such as an LED.

[0040] The operation unit 130 receives operations from the user, issues operation instructions to each functional unit, and notifies the control unit 100 of operation signals corresponding to the received operations. For example, the operation unit 130 receives operation signals from a remote control or the like and operates the bed device 10 according to the received operation signals. The operation unit 130 may also perform control based on operations received using, for example, operation buttons on the bed device 10 or software keys using a touch panel, or it may be an external operation device (remote control device). Furthermore, the operation unit 130 may be implemented by an application built into the terminal device 30.

[0041] The notification unit 140 provides notifications to users and staff. For example, the notification unit 140 may be a speaker that outputs sound, a buzzer, or a light-emitting element that provides notifications with light. Alternatively, the notification unit 140 may provide any kind of notification. For example, the notification unit 140 may be implemented by an external device (e.g., a smart speaker), and notifications may be provided via the external device.

[0042] The drive control unit 150 controls the drive unit 155 of the bed device 10. For example, the drive unit 155 is an actuator provided on the bottom of the bed device 10 or on the link mechanism. By controlling the actuator, the drive control unit 150 can raise or lower the height of the bed device 10. In addition, by controlling the drive unit 155 connected to the bottom, the drive control unit 150 can change the shape of the bed device 10.

[0043] The detection unit 160 detects the state of the bed device 10. For example, in this embodiment, the weight (body weight) of an object (e.g., a user) on the bed device 10 is detected based on the load detected by the load detection device 165 (load detection device B30 described in Figure 2). The control unit 100 can also determine the center of gravity of the object (user) based on the loads detected by the multiple load detection devices 165 acquired by the detection unit 160. In order for the control unit 100 to correctly determine the center of gravity on the bed device 10, it is preferable to provide at least three load detection devices 165.

[0044] The communication unit 190 is a communication interface for communicating with other devices. For example, the communication unit 190 may be a network interface that can connect to a wireless LAN, or a network interface that can connect to Ethernet (registered trademark) via a wired connection. Alternatively, the communication unit 190 may be a communication device that can connect to a mobile communication network such as LTE / 4G / 5G / 6G.

[0045] Furthermore, the configuration shown in Figure 3 may be limited to the configurations necessary for the embodiment. For example, if a notification function is not required, the notification unit 140 may be omitted. Also, the bed device 10 may further include a camera device (shooting unit) when taking pictures of the user's condition.

[0046] [1.2.2 Server Equipment] Figure 4 is a diagram illustrating the hardware configuration of the server device 20.

[0047] The control unit 200 controls the entire server device 20. The control unit 200 implements various functions by reading and executing various programs stored in the memory unit 210 (e.g., ROM 210A, storage 210C). The control unit 200 may be implemented by one or more control devices / arithmetic units (CPU (Central Processing Unit), SoC (System on a Chip)). Alternatively, the control unit 200 may be composed of control circuits.

[0048] The memory unit 210 is one or more storage devices that store necessary data or programs. The memory unit 110 temporarily or permanently stores data and programs. For example, the memory unit 210 includes a ROM 210A, a RAM 210B, and a storage 210C.

[0049] ROM210A is a non-volatile memory that can retain programs and data even when the power is turned off.

[0050] RAM210B is the main memory primarily used by the control unit 200 during processing. RAM210B is a rewritable memory that temporarily holds data including programs read from ROM210A and storage 210C, as well as execution results.

[0051] Storage 210C is a non-volatile storage device capable of storing programs and data. For example, it may consist of storage devices such as HDDs (Hard Disk Drives) or SSDs (Solid State Drives). Alternatively, Storage 210C may be configured as an externally connectable USB memory stick. Furthermore, Storage 210C may be, for example, a storage area located in the cloud.

[0052] The communication unit 290 is a communication interface for communicating with other devices. For example, the communication unit 290 may be a network interface that can connect to a wireless LAN, or a network interface that can connect to Ethernet (registered trademark) via a wired connection. Alternatively, the communication unit 290 may be a communication device that can connect to a mobile communication network such as LTE / 4G / 5G / 6G.

[0053] Note that the server device 20 does not necessarily have to be installed in system 1. For example, the processing of this embodiment may be implemented using an external service (e.g., a SaaS service, a PaaS service).

[0054] [1.2.3 Terminal Devices / Processing Devices] Figure 5 illustrates the hardware configuration of the terminal device 30 or the processing device 40. Note that both the terminal device 30 and the processing device 40 are applications of an information processing device, and their hardware configurations are almost identical. The following explanation will use the terminal device 30 as an example. Furthermore, since the hardware configuration of the terminal device 50 is identical to that of the terminal device 30, its explanation will be omitted.

[0055] The control unit 300 controls the entire terminal device 30. The control unit 300 realizes various functions by reading and executing various programs stored in the memory unit 310 (e.g., ROM 310A, storage 310C). The control unit 300 may be realized by one or more control devices / arithmetic units (CPU (Central Processing Unit), SoC (System on a Chip)). Alternatively, the control unit 300 may be composed of a control circuit.

[0056] The memory unit 310 is one or more storage devices that store necessary data or programs. The memory unit 310 temporarily or permanently stores data and programs. For example, the memory unit 310 includes a ROM 310A, a RAM 310B, and a storage 310C.

[0057] ROM310A is a non-volatile memory that can retain programs and data even when the power is turned off.

[0058] RAM310B is the main memory primarily used by the control unit 300 during processing. RAM310B is a rewritable memory that temporarily holds data including programs read from ROM310A and storage 310C, as well as execution results.

[0059] Storage 310C is a non-volatile storage device capable of storing programs and data. For example, it may consist of storage devices such as HDDs (Hard Disk Drives) or SSDs (Solid State Drives). Alternatively, Storage 310C may be configured as an externally connectable USB memory stick. Furthermore, Storage 310C may be, for example, a storage area located in the cloud.

[0060] The display unit 320 is a display device capable of displaying applications, user interface screens created by the server device 20, and various information. The display unit 320 may be, for example, a liquid crystal display (LCD) or an organic electroluminescent (OLED) display, or other device capable of displaying images. Alternatively, the display unit 320 may be an external display device that can be connected via, for example, HDMI®.

[0061] The operation unit 330 receives operations from the user, issues operation instructions to each function unit, and notifies the control unit 300 of operation signals corresponding to the received operations. For example, the operation unit 330 may be an operating device such as a keyboard or mouse, or it may be a software key displayed on a touch panel.

[0062] The notification unit 340 provides notifications to users and staff. For example, the notification unit 340 may be a speaker that outputs sound, a buzzer, or a light-emitting element that provides notifications with light. Alternatively, the notification unit 340 can provide any kind of notification. For example, the notification unit 340 may be implemented by an external device (e.g., a smart speaker), and notifications may be provided via the external device.

[0063] The communication unit 390 is a communication interface for communicating with other devices. For example, the communication unit 390 may be a network interface that can connect to a wireless LAN, or a network interface that can connect to Ethernet (registered trademark) via a wired connection. Alternatively, the communication unit 390 may be a communication device that can connect to a mobile communication network such as LTE / 4G / 5G / 6G.

[0064] Although the explanation uses terminal device 30 as an example, the same applies to processing unit 40 and terminal device 50. For example, the control unit 300 in terminal device 30 is the same as the control unit 400 in processing unit 40. Similarly, the memory unit 310 (ROM 310A, RAM 310B, storage 310C) is replaced by the memory unit 410 (ROM 410A, RAM 410B, storage 410C), the display unit 320 is replaced by the display unit 420, the operation unit 330 is replaced by the operation unit 430, the notification unit 340 is replaced by the notification unit 440, and the communication unit 390 is replaced by the communication unit 490.

[0065] Furthermore, the configuration shown in Figure 3 may be limited to the configurations necessary for the embodiment. For example, if a notification function is not required, the notification unit 340 may be omitted. Also, the terminal device 30 may further include a camera device (shooting unit) or an audio input / output unit when capturing images of the user's state.

[0066] [1.3 Software Configuration] Figure 6 is a diagram illustrating the software configuration. For example, in the bed device 10, the control unit 100 executes a program stored in the memory unit 110, which then functions as the center of gravity position determination unit 102, the user notification unit 104, and the weight measurement unit 106.

[0067] The center of gravity position determination unit 102 determines the position of the user's center of gravity while they are lying on the bed device 10. Here, "on the bed device 10" includes the mattress placed on the bed device 10. The method by which the center of gravity position determination unit 102 determines the position of the user's center of gravity will be described later.

[0068] The user notification unit 104 notifies the user's status. Here, the user notification unit 104 may notify by, for example, displaying it on the display unit 120, or by broadcasting it from the notification unit 140. Alternatively, the user notification unit 104 may notify the server device 20 via the communication unit 190, and the server device 20 may notify the processing unit 40 or the terminal device 50.

[0069] Furthermore, the user notification unit 104 can notify users of their status, such as when they get up from the bed, sit on the edge of the bed (including a prediction of sitting on the edge of the bed), get out of bed, or when they are out of bed for a predetermined period of time.

[0070] The weight measurement unit 106 measures the weight of the user who is lying on the bed device 10. The method by which the weight measurement unit 106 measures the user's weight will be described in detail later.

[0071] The timer unit 108 counts as a timer for a given time. The timer unit 108 can, for example, start a new count or continue counting if it has already started. The timer unit 108 may also operate multiple timers. For example, it is possible to count using a first timer, a second timer, and multiple other timers in parallel.

[0072] The storage unit 110 reserves storage space for a weight information storage area 112 that stores weight information. The weight information stored in the storage unit 110 may be stored at predetermined intervals. The control unit 100 also transmits the weight information stored in the weight information storage area 112 to the server device 20 as needed.

[0073] The region setting table 114 stores information about the region used by the control unit 100 when determining the position of the user's center of gravity. The region setting table 114 may also store the coordinates of the region. In addition, the region setting table 114 may store threshold values, which will be described later.

[0074] The memory unit 110 reserves a memory area for the user information memory area 116, which stores user information. User information is information about the user, and in addition to information such as the user's name and user identification information (ID), it may also store information such as height. In this embodiment, weight information is stored in the weight information memory area 112, but it may also be stored together with the user information.

[0075] The control unit 200 functions as a weight information storage unit 202, an abnormality notification unit 204, and a UI provision unit 206 by executing a program stored in the storage unit 210.

[0076] The weight information storage unit 202 stores the weight information received from the bed device 10 in the weight information storage area 212 for each user. For users for whom the measurement condition table 216 is set, the weight information storage unit 202 stores weight information that matches the measurement conditions in the weight information storage area 212. The method by which the weight information storage unit 202 stores weight information will be described later.

[0077] The abnormality notification unit 204 issues a notification when an abnormality is detected in the user's weight. The method by which the abnormality notification unit 204 detects abnormalities and the method of notification will be described later.

[0078] The memory unit 210 reserves a weight information storage area 212, which is an area for storing weight information. The memory unit 210 also stores a priority table 214 and a measurement condition table 216, which will be described later.

[0079] The memory unit 210 reserves a memory area for a user information storage area 218 that stores user information. User information may include information stored in the user information storage area 116. The user information storage area 218 may also store user information for multiple users. In the memory unit 210, weight information is stored in the weight information storage area 212, but it may also be stored together with the user information.

[0080] [1.4 Processing Flow] In this embodiment, the user notification unit 104 makes notifications according to the user's status. First, the processing of the seating position prediction will be explained.

[0081] [1.4.1 Prediction processing for sitting position] [1.4.1.1 Main Processing] The process for predicting sitting posture will now be explained. Figure 7 is an operation flow illustrating the main process in the sitting posture prediction process. The sitting posture prediction process detects the movement between the user's getting up movement and the sitting posture as a sitting posture prediction, and makes a prediction based on the sitting posture prediction. For example, the control unit 100 determines whether or not it is predicted that the user will be in a sitting posture, which is the posture before getting out of bed, within a predetermined period from the present, as a sitting posture prediction. If it is predicted that the user will be in a sitting posture, the control unit 100 will, for example, notify the user of the sitting posture prediction from the notification unit 140, or notify the processing unit 40 or the terminal device 50 of the sitting posture prediction.

[0082] Furthermore, the bed edge sitting prediction process of this bed device is different from the process of detecting the user's sitting motion itself or the process of detecting the user's movement to get out of bed itself. It may be configured to have functions for both sitting motion detection and getting out of bed detection, in addition to sitting motion prediction.

[0083] First, the control unit 100 determines that the initial state of the bed device 10 is a state with no load, i.e., an absent state (S102). Here, if the control unit 100 detects a load that is greater than or equal to a threshold (e.g., an absentness determination threshold) (e.g., 5 kgf, 10 kgf) (S104; Yes), it determines the current center of gravity position (S106). Here, the current center of gravity position is defined as the first center of gravity position.

[0084] Here, the current center of gravity position may be the position determined by the center of gravity position determination unit 102, based on the latest load. Alternatively, the current center of gravity position may be the position based on a load that has been averaged to some extent based on the latest load.

[0085] For example, when the load detection device 165 is sampling at a sampling interval (e.g., 50ms, 100ms, 200ms, etc.), the sampled load may be considered the latest load, and the current center of gravity position may be determined based on this latest load. Alternatively, the center of gravity position may be determined based on the load averaged over an averaging time (e.g., 1 second, 2 seconds, 3 seconds, etc.), and that center of gravity position may be considered the current center of gravity position. In the first embodiment, any of these center of gravity positions may be used.

[0086] In this embodiment, the first center of gravity position, which is the current center of gravity (coordinates), is determined by treating the surface on which the user is lying on the bed device 10 as a virtual XY plane (S106). Figure 8 is a schematic representation of the bed device 10. The right side of Figure 8 is the head side of the bed device 10, and the left side is the foot side of the bed device 10. Also, the upper part of Figure 7 is the right side of the bed device 10, and the lower part is the left side of the bed device 10.

[0087] If the control unit 100 has a first centroid position in the first region, it proceeds to S110 to perform the first end-position prediction processing (S108; Yes → S110). If the control unit 100 does not have a first centroid position in the first region, it performs the second end-position prediction processing (S108; No → S114).

[0088] Here, the second seating position prediction process predicts the user's seating position based on the load, and if a seating position prediction is made, it issues a seating position prediction. The control unit 100 may also execute the second seating position prediction process and return to S106. That is, the control unit 100 may switch from the second seating position prediction process to the first seating position prediction process when the first center of gravity position is included in the first region. The control unit 100 may also transition to S102 when it detects that the user has left the bed.

[0089] Furthermore, when the first centroid position is in the first region, the control unit 100 calculates the centroid position using a moving average over a certain period of time when performing the first end seating position prediction process (S110). Here, the centroid position calculated using a moving average over a certain period of time is defined as the second centroid position. The control unit 100 may use 1 minute or 3 minutes as the time for calculating the second centroid position. The control unit 100 only needs to be able to calculate the centroid position using a moving average over a predetermined period of time.

[0090] Here, the control unit 100 performs a first end-position prediction process according to the first center of gravity position and / or the second center of gravity position (S112). In the first end-position prediction process, the control unit 100 performs a first prediction process and / or a second prediction process. That is, the control unit 100 performs the first prediction process and the second prediction process based on the first center of gravity position and the second center of gravity position, respectively. At this time, the control unit 100 may perform either the first prediction process or the second prediction process, or may perform them in parallel. Furthermore, the control unit 100 may use the center of gravity positions of the first and second center of gravity positions to determine whether to perform the two processes.

[0091] Then, the control unit 100 makes a seating position forecast when it has determined that the user is seated at the edge of the chair based on the first and / or second prediction processes.

[0092] Furthermore, when the control unit 100 determines that the user has left the bed, it stops the first-end sitting position prediction process and repeats the process from S102 (S116; Yes → S102). Also, when the user has not left the bed, the control unit 100 determines the current center of gravity position (first center of gravity position) again in order to continue the first-end sitting position prediction process (S116; No → S118), and repeats the process from S1110.

[0093] In this embodiment, the control unit 100 executes the second end-sitting position prediction process until the first center of gravity position enters the first region, and once it enters the first region, it switches to the first end-sitting position prediction process and executes the process. However, the control unit 100 may switch between the two processes. In that case, if the user does not get out of bed in S116, the control unit 100 should transition to S106.

[0094] [1.4.1.2 Switching Processes] Here, the switching of processing will be explained with reference to the diagram. In the edge-sitting prediction process shown in Figure 7, the control unit 100 performs the edge-sitting prediction based on the user's center of gravity position. For example, the control unit 100 may execute the process shown in Figure 7 when the position of the center of gravity (first center of gravity position) is included in the first region R11 shown in Figure 8.

[0095] Here, the first region R11 will be explained with reference to Figure 8. Figure 8 shows a virtual region when the bed device 10 is viewed from above. In this embodiment, the region refers to a virtual region set on the surface of the bed device 10 that supports the user, and represents a part of the area where the user is lying down. This region on the bed device 10 may be the position of the bottom of the bed device 10, or the position of the mattress placed on the bed device 10. Furthermore, the position of the region will be described using virtual coordinates of the X and Y axes, with the center being (0,0). Note that the coordinates show the relative position with the center being (0,0), but the unit may also be millimeters.

[0096] As shown in Figure 8, the first region R11 has a predetermined range set when the center position is at coordinates (0,0). The first region R11 is a switching region provided to change the processing method for multiple seating position predictions, which will be described later. That is, in this embodiment, when performing a seating position prediction for a user, the seating position prediction is performed using two processing methods based on the current center of gravity position of the user in the XY plane. The control unit 100 performs a seating position prediction based on the position of the user's center of gravity by executing the first seating position prediction processing. The control unit 100 also performs a seating position prediction based on the detected load by executing the second seating position prediction processing.

[0097] The first region R11 is the region (switching region) that the control unit 100 determines in order to switch the seat position prediction process. The first region R10 is set to the range of (-350,300)-(350,-300) when the center position is (0,0) and the size of the XY plane is (-900,420)-(900,-420).

[0098] This first area R11 (switching area) may be a predetermined area, or it may be set by staff, administrators, etc.

[0099] The control unit 100 executes a process (first seating position prediction process) to predict seating position based on the center of gravity position if the center of gravity position is within the first region R11. That is, it executes a first prediction process and a second prediction process based on the user's center of gravity position (first center of gravity position, second center of gravity position).

[0100] Here, the second to fourth regions used in the first prediction process, which will be described later, are shown in Figure 9(a). Also, the fifth and sixth regions used in the second prediction process are shown in Figure 9(b). As mentioned above, the regions in the first prediction process are defined by coordinates on the bed device 10.

[0101] For example, as shown in Figure 9(a), when the center is defined as (0,0), the second region R12, the third region R13, and the fourth region R14 are defined outward from the center. Here, the second region R12 is defined as a dead zone, and the control unit 100 does not perform the first prediction process when the first centroid position, which is the current centroid position, is included in the second region R12. Also, the control unit 100 performs the first prediction process when the first centroid position is included in the third region R13 and the fourth region R14.

[0102] Here, the third region R13 is preferably offset from the end of the bed device 10 by approximately half the length of the user's shoulder width when the user is lying supine on the bed device 10, for example, in the short-arm direction (Y-axis direction). In other words, a region corresponding to half the length of the user's shoulder width is provided as a determination region at the end of the bed device 10. As a guideline for setting the determination region, it is preferable to set the determination region such that the position of the user's center of gravity when lying supine with the end of the bed device 10 is included within the third region R13, and the position of the user's center of gravity when the user takes a lateral position further outside the bed device 10 is included in the fourth region R14.

[0103] Furthermore, it is preferable that the third region R13, for example in the longitudinal direction (X-axis direction), is offset from the head side by approximately the length from the top of the user's head to the user's center of gravity when the user is lying supine on the bed device 10, and offset from the foot side by approximately the length from the soles of the user's feet to the user's center of gravity. In other words, it is preferable that a region with a length from the user's center of gravity to the end of the user's body part is provided as a determination region at the end of the bed device 10. As a guideline for setting the determination region, it is preferable that the user's center of gravity when lying supine with the end of the bed device 10 is included in the third region R13, and that when the user curls their body further toward the end of the bed device 10, the user's center of gravity is included in the fourth region R14. Also, it is preferable that the second region R12 is approximately half or one-third the size of the third region R13.

[0104] Here, the control unit 100 may change the processing between the third region R13 and the fourth region R14. For example, when it is the third region R13, the control unit 100 may detect the movement of the center of gravity based on either the X or Y direction, and when it is the fourth region R14, it may detect the movement of the center of gravity in both the X and Y directions.

[0105] Furthermore, as shown in Figure 9(b), when the center is defined as (0,0), the area on the center side (inside) is defined as the fifth region R15, and the area on the outside is defined as the sixth region R16. The control unit 100 considers the fifth region R15 as a dead zone, and does not perform the second prediction process when the first centroid position is included in the fifth region R15. The control unit 100 also performs the second prediction process when the first centroid position is included in the sixth region R16.

[0106] Here, the sixth region R16 is preferably offset from the end of the bed device 10 by the length of the user's center of gravity when the user is lying on their side on the bed device 10 with their thighs outside the bed device 10, for example, in the short-arm direction (Y-axis direction). As a guideline for setting the sixth region R16, it is preferable to set the determination region such that the user's center of gravity when they are lying on their side with their thighs outside the bed device 10 is included in the sixth region R16, and when the user moves their thighs further inside the bed device 10 from that position, it is included in the fifth region R15.

[0107] Furthermore, it is preferable that the sixth region R16 is offset from the head or foot side by approximately the length to the user's center of gravity when the user is lying on the bed device 10 and in a long sitting position with their back to the board of the bed device 10, for example, in the longitudinal direction (X-axis direction). As a guideline for the determination region, it is preferable to set the determination region such that the position of the user's center of gravity when the user is in a long sitting position with their back to the board of the bed device 10 is included in the sixth region R16, and the position of the user's center of gravity when the user is in any other supine position is included in the fifth region R15.

[0108] In this way, the control unit 100 switches between executing the first prediction process and the second prediction process depending on the first center of gravity position. For example, Figure 10 is a schematic diagram showing the relationship between the first center of gravity position, which is the current center of gravity position of the user, and the process executed by the control unit 100.

[0109] In both Figure 10(a) and Figure 10(b), the first center of gravity is shown at five locations, P11 to P15. Figures 10(a) and 10(b) show positions on the same bed device 10, and their respective regions are schematically shown separately.

[0110] Based on the relationship between the first centroid position and the second region R12 to the sixth region R16, the processing performed by the control unit 100 is as shown in the table in Figure 10(c). For example, when the first centroid position is P11, it is in the fourth region R14 in Figure 10(a) and in the sixth region R16 in Figure 10(b). As a result, the control unit 100 performs the first prediction process and the second prediction process in parallel. Note that in the first prediction process, the control unit 100 only needs to use either the X or Y direction.

[0111] Similarly, for example, when the first center of gravity is at P12, it is in the third region R13 in Figure 10(a) and in the sixth region R16 in Figure 10(b). As a result, the control unit 100 executes the first prediction process and the second prediction process in parallel. Note that when P12 is the center of gravity, the control unit 100 only needs to use two directions, the X and Y directions, as the direction of movement of the center of gravity in the first prediction process, since it is in the third determination region.

[0112] Furthermore, the first centroid positions P13 to P15 are included in the fifth region R15 in Figure 10(b). Therefore, the control unit 100 disables the second prediction process and does not execute it.

[0113] The first end-position prediction processing will be explained in detail here, but for example, when the user's first center of gravity is in the third or fourth region of the bed device 10 (described later), the control unit 100 obtains the relative coordinates from the second center of gravity and determines the user's behavior based on the amount and direction of movement. At this time, the control unit 100 executes the first prediction processing (described later).

[0114] Then, when the control unit 100 is in an area away from the center of the bed device 10 (the sixth area described later), it obtains the first centroid position (i.e., the absolute coordinates of the user's position), and determines whether or not the user has entered that area, and then determines the user's behavior. At this time, the control unit 100 executes the second prediction process. The control unit 100 may execute the first prediction process and the second prediction process separately, or they may be operated in parallel. The first prediction process and the second prediction process are processed based on each area if the first centroid position is in an area where the third, fourth, and sixth areas do not overlap, and are processed in parallel if the areas interfere with each other.

[0115] Furthermore, when the control unit 100 determines the user's actions based on relative coordinates, it monitors movement in both the X and Y directions near the center (third region R13), but switches to a mode where it determines the actions based on the amount of movement in either the X or Y direction in areas away from the center of the bed (fourth region R14). The control unit 100 may monitor movement in the X direction near the ends of the long sides, but may monitor movement in the Y direction near the ends of the short sides.

[0116] For example, the control unit 100 does not make a determination for a seated position at the edge of the bed when the user turns over and their center of gravity is at the edge of the bed. However, when the user gets up from there, the control unit 100 makes a determination for a seated position at the edge of the bed because the user may move away from the bed device 10. Also, the control unit 100 does not have to make a determination for a seated position at the edge of the bed if the user simply gets up in the middle of the bed. Furthermore, the control unit 100 does not make a determination for a seated position at the edge of the bed when the user moves further towards the feet, but may make a determination for a seated position at the edge of the bed when the user moves in either the left or right Y direction (outward) from that point.

[0117] Thus, the seated position prediction process does not classify actions that do not involve leaving the bed, such as turning over or adjusting bedding on the bed, as seated position predictions. This will be explained in detail below.

[0118] [1.4.1.3 First End Seating Position Prediction Processing] In the first seating position prediction process, when determining seating position based on the user's center of gravity, the control unit 100 performs seating position prediction using two determination methods. The first determination method is a first prediction process in which the control unit 100 relatively perceives the movement of the center of gravity and makes a determination based on the relative position change (relative coordinates) of that center of gravity. The second determination method is a second prediction process in which the control unit 100 perceives the movement of the center of gravity as absolute coordinates and makes a determination based on the absolute position.

[0119] It is preferable that the control unit 100 performs both the first prediction process and the second prediction process to provide seating position prediction. The control unit 100 may perform only one of the processes, either the first prediction process or the second prediction process.

[0120] [1.4.1.3.1 First Prediction Processing] The first prediction process will be explained with reference to Figure 11. The first prediction process compares the first center of gravity position with the second center of gravity position and determines the movement of the center of gravity based on the relative position change (relative coordinates).

[0121] The control unit 100 does not perform any special determination processing when the first center of gravity is in the second region R12, because it is a dead zone (S202; Yes).

[0122] The control unit 100 determines whether the first center of gravity is within the third region R13 when the first center of gravity is outside the second region R12 (S204). If the first center of gravity is within the third region R13 (S204; Yes), the control unit 100 determines whether the amount of movement of the center of gravity in the X and Y directions is greater than or equal to a first threshold (for example, a movement threshold) (S206). Here, the amount of movement of the center of gravity refers to the relative change in position. In other words, it represents the user's coordinates as relative coordinates with respect to the coordinates at a certain time.

[0123] Here, the control unit 100 makes a seating position prediction if the amount of movement of the first center of gravity position in the X and Y directions is greater than or equal to the first threshold (S206; Yes → S208).

[0124] For example, the control unit 100 calculates the amount of change between the second center of gravity position (the average center of gravity position over a predetermined time (for example, an average center of gravity determination time of 1 minute, 3 minutes, 5 minutes, etc.)) and the first center of gravity position (the current center of gravity position) as the amount of movement. At this time, the control unit 100 calculates the amount of change in the X direction and the Y direction as the amount of movement.

[0125] Here, the first threshold (movement threshold) is the threshold used to determine the difference between the first center of gravity position and the second center of gravity position. In other words, the first threshold (movement threshold) is the difference (amount of movement or speed) between the second center of gravity position and the current center of gravity position.

[0126] In this embodiment, the second centroid position is set to the average centroid position, but it may simply be the centroid position from a unit time ago. For example, the control unit 100 may set the second centroid position to the centroid position from a predetermined unit time ago, such as 1 minute ago, 2 minutes ago, or 3 minutes ago.

[0127] Here, the control unit 100 can calculate the amount of movement to be compared with the first threshold (movement threshold) using the following two methods.

[0128] (1) The difference between the first center of gravity position and the second center of gravity position For example, the amount of movement is defined as the difference between the current first center of gravity position and the average value of the first center of gravity position over a predetermined time (for example, 4 minutes as the average center of gravity determination time) (the second center of gravity position, which is the average center of gravity position). In this case, it is possible to identify actions at a constant speed while taking into account that the user's sleeping position gradually changes. That is, the control unit 100 evaluates the difference from the first center of gravity position while gradually changing the coordinates that serve as the reference for the relative position on average over a predetermined time (for example, 4 minutes). For example, method (1) is a more appropriate calculation method when the user's movement out of the bed device 10 is slow.

[0129] (2) Utilize the difference from the center of gravity position of the previous unit of time. For example, the amount of change in the position of the first center of gravity before and after sampling is taken as the amount of movement. For example, the difference between the center of gravity position at t seconds and the center of gravity position at t-1 seconds is taken as the amount of movement. That is, the second center of gravity position is simply the center of gravity position from a unit time earlier. In this case, the control unit 100 can determine whether the user's actions are abnormal based on the speed of the user's actions. In this case, the control unit 100 does not need to detect if the user's sleeping position becomes bad due to poor sleeping posture. Method (2) is a more appropriate calculation method when the user moves quickly out of the bed device 10.

[0130] The control unit 100 then performs a seating position prediction when the amount of movement of the first center of gravity in the X and Y directions exceeds the first threshold. Note that the first threshold (movement threshold) may be different for the X and Y directions.

[0131] When the control unit 100 issues a seated-on-the-edge prediction, it may, for example, notify the notification unit 140 that there is a possibility of the patient getting out of bed. The control unit 100 may also notify the server device 20 via the communication unit 190 that a seated-on-the-edge prediction has been issued. The server device 20 may, for example, notify the processing unit 40. The server device 20 may also, for example, notify a terminal device 50 held by the staff member in charge of the user.

[0132] Returning to S204, the control unit 100 determines whether the first centroid position is within the fourth region R14 if the first centroid position is not within the third region R13 (S212). Here, if the first centroid position is within the fourth region R14, it determines whether the amount of movement of the first centroid position in the X or Y direction is greater than or equal to the first threshold (movement threshold) (S212; Yes → S214).

[0133] Here, the control unit 100 determines that the first centroid position is in the fourth region R14 if the average displacement of the coordinates of the first centroid position in the X direction over a predetermined determination time (for example, 4 minutes) is calculated, and the value obtained by subtracting the average displacement over a shorter determination time (for example, 1 second) is equal to or greater than the first threshold (displacement threshold).

[0134] Furthermore, the statement that the first center of gravity is within the fourth region R14 means that the control unit 100 continues to detect the center of gravity within the sixth region R14 for a predetermined period of time.

[0135] Then, when the amount of movement of the first center of gravity position becomes greater than or equal to the first threshold (movement threshold), the control unit 100 performs a seating position prediction (S214; Yes → S208). In other words, when the center of gravity position is within the third region R13, the control unit 100 uses the X and Y directions as the amount of movement for detection determination, whereas when it is within the fourth region R14, it uses either the X or Y direction as the amount of movement of the first center of gravity position for detection determination.

[0136] Furthermore, if the first center of gravity position is not within the fourth region R14, the control unit 100 may determine that the position of the center of gravity can no longer be detected and issue a notification of an abnormality (S212; No → S216; Yes → S218). The method of notifying of an abnormality is for the control unit 100 to display an error on the display unit. Also, if the control unit 100 cannot determine the position of the center of gravity, it may prioritize monitoring the user and issue a notification from the notification unit in the same format as the notification of leaving the bed in order to call a caregiver.

[0137] Furthermore, if the control unit 100 determines in S212 that the first centroid position is not within the fourth region R14, even though the first centroid position has been detected, it repeatedly executes the process from S202.

[0138] [1.4.1.3.2 Second Prediction Processing] The second prediction process will be explained with reference to Figure 12. First, the control unit 100 determines the range of the fifth region R15 based on the second threshold (region determination threshold). As will be explained in detail later, if the second threshold is 100%, the size of the fifth region R15 will be the size set in advance. Also, if the second threshold becomes smaller, the size of the fifth region R15 will become smaller, and the size of the sixth region R16 will become larger.

[0139] Here, the second threshold refers to a threshold value of the size used by the control unit 100 when determining the fifth region R15 (or the sixth region R16). In other words, the second threshold is the size of the fifth region R15 (or the sixth region R16).

[0140] Next, the control unit 100 does not perform a determination when the first center of gravity position is within the fifth region R15 because it is a dead zone (S242; Yes). The control unit 100 performs a seating position prediction when the first center of gravity position is within the sixth region R16 (S244; Yes → S246). The processing performed by the control unit 100 when a seating position prediction is made may be, for example, the processing described in S208 of Figure 10.

[0141] Here, the statement that the first centroid position is within the sixth region R16 may indicate that the control unit 100 continues to detect the first centroid position within the sixth region R16 for a predetermined period of time.

[0142] Furthermore, if the first center of gravity position is not within the sixth region R16 and the first center of gravity position cannot be detected, the control unit 100 determines that there is an abnormality and issues a notification (S244; No → S248; Yes → S250). The abnormality may be notified by displaying "Error" on the display unit. Also, if the first center of gravity position cannot be determined, the control unit 100 may issue a notification from the notification unit in the same format as the notification of leaving the bed in order to prioritize monitoring the user and call a caregiver. Otherwise, the control unit 100 repeatedly executes the process from S242 (S248; No → S242).

[0143] [1.4.1.4 Methods for defining conditions in a domain] In the first end-of-bed seating prediction process described above, the process of changing the conditions for determining the end-of-bed seating position in the first prediction process and the second prediction process will be explained. The control unit 100 acquires environmental information such as the shape of the bed device 10 (for example, the state of the bed frame and bed bottom), the user's state, and the surrounding environment. Based on the environmental information, the control unit 100 can dynamically change the determination conditions for the first prediction process or the second prediction process (for example, parameters such as the size of the area, the first threshold, and the second threshold). Several specific examples of how the control unit 100 changes the determination conditions will be explained below.

[0144] (1) Shape of the bed device 10 (a) When the back angle is high (when the back bottom is rising) For example, the control unit 100 performs control to make detection more sensitive by, for example, changing the second threshold in the second prediction process to widen the range of the sixth region R16. The following explanation will use the second prediction process as an example, but it may also be applied to the first prediction process.

[0145] For example, in the bed device 10, if the back angle is high, the back bottom is significantly elevated. The user is more likely to lose balance in their upper body and is at high risk of falling. Therefore, it is preferable for the control unit 100 to be more sensitive to detecting edge-sitting predictions in the Y direction compared to when the back angle is low. For example, when the control unit 100 detects a movement in the Y direction of the first center of gravity position, such as when the user turns over, and the back angle of the bed device 10 is high, it determines this to be a dangerous behavior and notifies an edge-sitting prediction. Alternatively, when the back angle of the bed device 10 is low, the control unit 100 may determine that the movement in the Y direction of the first center of gravity position is simply turning over and not notify an edge-sitting prediction.

[0146] In other words, the second threshold when the back angle of the bed device 10 is the first angle is greater than the second threshold when the back angle is the second angle (an angle greater than the first angle). For example, if the back angle of the bed device 10 varies from a minimum of 0 degrees to a maximum of 75 degrees, the second threshold when the first angle is 30 degrees is set to 60% ((*,240) to (*,-240)) with respect to the Y axis, but the second threshold when the second angle is 75 degrees may be applied as 0% ((*,150) to (*,-150)) with respect to the Y axis. As a result, for example, the tolerance in the Y-axis direction of the bed device 10 changes as follows.

[0147] Back angle 0 degrees: The first threshold is applied as 100%. For example, the control unit 100 allows movement in the Y-axis direction up to ±300 mm. Back angle 30 degrees: Apply a first threshold of 60%. For example, the control unit 100 allows movement in the Y-axis direction up to ±240 mm. Back angle 70 degrees: Apply with the first threshold set to 0%. For example, the control unit 100 allows movement in the Y-axis direction up to ±150 mm.

[0148] Furthermore, the control unit 100 may make a determination based on information detectable from the bed device 10, such as the back angle, but it can also estimate the position of the user's center of gravity in the Z direction (vertical direction, which is height) from the position of the center of gravity in the XY plane and the back angle. The control unit 100 may make the detection of edge-sitting prediction more sensitive by determining how easily the user is likely to lose balance from the center of gravity position in the Z direction and increasing the detection range.

[0149] For example, when the back angle is 30 degrees and the height of the first center of gravity is 8 cm, the control unit 100 may apply a first threshold of 60% because the user is less likely to lose their balance. Also, when the back angle is 75 degrees and the height of the first center of gravity is 20 cm, the control unit 100 may apply a threshold of 0% because the user is more likely to lose their balance.

[0150] Furthermore, the control unit 100 can improve the accuracy of its understanding of the user's condition by using a sensor device that detects the presence or absence of a user at the backrest (for example, a sensor device that can acquire the user's biometric information on a sheet or the presence or absence of the user) to determine whether the user is leaning against the backrest. For example, if the user is leaning against the backrest, the control unit 100 will not make the detection more sensitive even if the back angle is high, but when the user's body is away from the backrest, the 0% threshold state described above will be applied, and the control unit 100 will switch to a more sensitive state.

[0151] Furthermore, the control unit 100 may also be made more sensitive to detection in the X direction when the back angle is high. For example, when the back angle is 75 degrees, the control unit 100 can be made more sensitive because the amount of movement of the center of gravity remains the same as when it is 0 degrees, no matter how much the user stands up, and the change is minute.

[0152] In other words, the second threshold when the back angle is the first angle is greater than the second threshold when the back angle is the second angle (an angle greater than the first angle). For example, if the back angle of the bed device 10 changes from a minimum of 0 degrees to a maximum of 75 degrees, the control unit 100 sets the second threshold when the first angle is 30 degrees to a threshold of 60% ((330,*) to (-330,*)) with respect to the X axis, but sets the second threshold when the second angle is 75 degrees to a threshold of 0% ((300,*) to (-300,*)) with respect to the X axis.

[0153] Furthermore, although the above explanation uses the angle of the back bottom (back angle) as an example, the bed device 10's shape and posture, such as the inclination angle of the bed device 10 (inclination angle of the upper frame) and the knee angle (angle of the knee bottom), may also be used to over-detect or reverse the seated position prediction.

[0154] (b) The height (floor height) of the bed device 10 is too high. For example, when the height of the bed device 10 (height of the upper frame) is higher than a predetermined value, the control unit 100 may widen the range of the sixth region R16 in the second determination method to make the detection of the seated position prediction more sensitive.

[0155] This is because, when the bed device 10 is at a high floor height, the severity of injuries in the event of a fall is likely to worsen. Therefore, it is preferable for the control unit 100 to be more sensitive to detecting user predictions in the X and Y directions compared to when the floor height is low. For example, when a user raises their body diagonally to look around, the control unit 100 may determine this to be a dangerous behavior and issue a seated-on-the-edge warning if the floor height is high, but if the floor height is low, it may determine that the user was simply checking their surroundings and not issue a seated-on-the-edge warning.

[0156] In other words, the second threshold for the first height is greater than the second threshold for the second height (a height higher than the first height). For example, if the height of the bed device 10 changes from a minimum of 20 cm to a maximum of 60 cm, the control unit 100 sets the second threshold for the first height of 46 cm to 60% of the threshold ((330,240) to (-330,-240)) with respect to the XY axis, but sets the second threshold for the second height of 60 cm to 0% of the threshold ((300,150) to (-300,-150)) with respect to the XY axis.

[0157] (2) User status (a) The user does not return to the center of the bed device 10 (XY plane) when lying down. For example, the control unit 100 widens the range of the sixth region R16 to make the detection of edge-sitting predictions more sensitive. For example, when a user lies down, if they do not return to the center of the bed device 10 but lie at the edge or remain in an edge-sitting position, the control unit 100 considers the user to be at high risk of falling, and widens the range of the sixth region R16 in the XY direction compared to when the user returns to the center of the bed device 10, thereby making the detection of edge-sitting predictions more sensitive.

[0158] In other words, if the user's lying position does not return to the second region R12 (insensitive region), and that position is (-250,250), the control unit 100 expands the range of the sixth region R16 from a range slightly offset from the current position (-300,300) to (300,-300). As a result, the control unit 100 will not notify if the user returns to the center of the bed, but will notify if there is movement toward the outside.

[0159] (b) The user is awake For example, the control unit 100 widens the range of the sixth region R16 to make the detection of the sitting position prediction more sensitive. For example, the sleep determination device determines the user's sleep state. When the user is awake, it is assumed that there is a high probability that the user will move away from the bed device 10, and the control unit 100 widens the range of the sixth region R16 in the XY direction compared to when the user is asleep to make the detection of the sitting position prediction more sensitive.

[0160] Furthermore, the control unit 100 detects and notifies a sitting position prediction if, for example, the user makes a movement in the Y direction, such as turning over in bed, and the user is awake. However, if the user is asleep, it determines that the movement is a turning over and does not notify the sitting position prediction.

[0161] In other words, the second threshold when the user is asleep is greater than the second threshold when the user is awake. For example, the control unit 100 sets the second threshold when the user is asleep to 100% ((350,300) to (-350,-300)) with respect to the X and Y axes, but sets the second threshold when the user is awake to 0% ((300,150) to (-300,-150)) with respect to the X and Y axes.

[0162] (c) The user has had a short time since becoming awake. For example, the control unit 100 widens the range of the sixth region R16 to make the detection of edge-sitting prediction more sensitive. For example, when a user has just become awake, they are at high risk of falling due to being drowsy, so the control unit 100 makes the detection of movement in the X or Y direction more sensitive than when a certain amount of time has passed since they became awake. For example, when there is movement in the X direction, such as getting up, if the user has just become awake, the control unit 100 determines that the risk of falling is high and notifies the user, but if a certain amount of time has passed since they became awake, the control unit 100 determines that the risk of falling is low and does not notify the user. For example, when the user is awake, the control unit 100 widens the range of the sixth region R16, but it may gradually decrease it to the normal range every minute. The passage of a certain amount of time is calculated and evaluated as the ratio of the current elapsed time to the upper limit time.

[0163] For example, if the upper limit is set to 10 minutes, the control unit 100 considers the user to be fully awake after 10 minutes, assuming 100% of the time has elapsed, and moderately awake after 5 minutes, assuming 50% of the time has elapsed. In other words, the second threshold for the user after 10 minutes of wakefulness is greater than the second threshold for the user after 5 minutes of wakefulness. For example, the control unit 100 sets the second threshold for the user after 10 minutes of wakefulness at a threshold of 100% ((350,300) to (-350,-300)) with respect to the X and Y axes, but sets the second threshold for the user after 5 minutes of wakefulness at a threshold of 50% ((325,225) to (-325,-225)) with respect to the X and Y axes.

[0164] (d) The user's center of gravity is located closer to the edge of the bed device 10. For example, the control unit 100 widens the range of the sixth region R16 to make the detection of edge-sitting predictions more sensitive. When the user is spending time at the edge of the bed device 10, even slight movements increase the risk of falling, so the control unit 100 makes the detection of movements in the X or Y direction more sensitive than when the user is sleeping near the center of the bed. For example, when there is a movement in the Y direction, such as turning over, the control unit 100 determines that the risk of falling is high and issues an edge-sitting prediction notification if the initial position was near the edge of the bed device 10, but determines that the risk of falling is low and does not issue an edge-sitting prediction notification if the initial position was near the center. The detailed processing is the same as in (2)(a).

[0165] (3) Bedside environment (a) When the surroundings are dark For example, the control unit 100 widens the range of the sixth region R16 to make the detection of edge-sitting prediction more sensitive. When a user is in a dark environment, the control unit 100 may consider the risk of falling to be high because they cannot perceive their surroundings, and may make the detection in the XY direction more sensitive compared to conditions with a bright environment. For example, brightness may be evaluated as the ratio of the current brightness to the upper limit brightness. For example, if the upper limit is 25,000 lux, the control unit 100 will evaluate the brightness as 50% if the current brightness is 12,500 lux and reflect this in the threshold. The detailed operation is the same as in (2)(c).

[0166] (b) When there is no one around to assist you For example, the control unit 100 expands the range of the sixth region R16 to make the detection of the seated position prediction more sensitive. When the caregiver is away from the room where the user is, the control unit 100 may make the detection range in the XY direction more sensitive compared to when the caregiver is nearby, and speed up the timing of the seated position prediction notification, in order to allow time between notification and the caregiver's arrival. The control unit 100 evaluates the distance of the caregiver as the ratio of the current distance to the upper limit distance. For example, if the upper limit is 50m, and the current position of the caregiver is 40m from the room, the distance is evaluated as 80% and reflected in the threshold. The detailed operation is the same as in (2)(c).

[0167] (c) When the caregiver in the vicinity is busy For example, the control unit 100 expands the range of the sixth region R16 to make the detection of edge-sitting predictions more sensitive. For example, the control unit 100 shares information such as "taking in" or "on standby" from the caregiver's terminal device 50 to the bed device 10. If the caregiver is "taking in," the control unit 100 may make the detection range in the XY direction more sensitive compared to when the caregiver is "on standby," and accelerate the notification timing of edge-sitting predictions, in order to allow time between notification and the caregiver's arrival.

[0168] [1.4.1.5 Conditional Change Processing in the Domain] Furthermore, as an example of a process to change the conditions for performing edge-seating prediction in the first and second prediction processes, an example of changing thresholds will be explained with reference to the figure. For example, the first threshold (movement threshold) and the second threshold (region determination threshold) may be determined by the control unit 200 of the server device 20 using a priority table based on environmental information acquired by the control unit 100. In this embodiment, the case in which the server device 20 changes the first threshold (movement threshold) will be explained as an example. Figure 13 is a processing flow showing the process of changing thresholds. Figure 14 is a diagram showing an example of a priority table.

[0169] The control unit 200 acquires user information and environmental information from the bed device 10 (S262). User information includes, for example, one or more pieces of information about the user's sleep state, the user's sleeping position (center of gravity), and the user's excretion status. Here, excretion status may refer to records of excretion by a caregiver, or to records from sensors that detect urination and defecation, indicating what kind of urination and excretion occurred and when. Environmental information may also include information about the surroundings of the bed device 10. Environmental information may include, for example, one or more pieces of information about the bed (angle of the bottom, height of the bed, presence or absence of side rails, etc.), brightness of the room, and information about the caregiver (presence or absence of a caregiver, condition of the caregiver, etc.).

[0170] Next, the control unit 200 determines a coefficient from the priority table (S264). Then, the control unit 200 changes the threshold value based on the coefficient (S266).

[0171] Figure 14 shows an example of a priority table. The priority table lists items from left to right (from 1) in order of increasing probability. For each item, the current value and the baseline value are stored. The judgment value stores how much the threshold should be changed. For example, if the judgment value is 0.0, the pre-stored threshold (100%) is calculated. If the judgment value is 1.0, the value is calculated to be the most suppressed state (0%) relative to the pre-stored threshold.

[0172] The relationship between the judgment value and the threshold will be explained in more detail with reference to the figure. Figure 15 shows the relationship between the first threshold (movement threshold) and the judgment value. Figure 15(a) shows the case where the judgment value is "0.0" and the threshold is "100%". The thresholds for the amount of movement in both the X and Y directions use the pre-set threshold (first threshold). Figure 15(c) shows the case where the judgment value is "1.0" and the threshold is "0%". Note that even when the threshold is set to "0%", the amount of movement used for the actual judgment is not "0 mm". This is to ensure that there is enough room for the amount of movement of the center of gravity due to the user's physiological phenomena. For example, the amount of movement in the X direction is preferably about 3 mm, representing the amount of movement of the diaphragm due to the user's breathing. Also, the amount of movement in the Y direction is preferably about 4 mm, representing the amount of movement due to the user's arm movement.

[0173] Then, the judgment value is determined such that the threshold has a linear relationship between Figure 15(a) and Figure 15(c). For example, Figure 15(b) is the result when the judgment value is "0.5" and the threshold is "50%", which is an intermediate value between Figure 15(a) and Figure 15(c).

[0174] Figure 16 shows the relationship between the second threshold and the judgment value. Figure 16(a) shows the case where the judgment value is "0.0" and the threshold is "100%". The fifth region R15 has the pre-set threshold (second threshold) applied to it. In this case, the fifth region R15 becomes the widest region, and the sixth region R16 becomes the narrowest region.

[0175] Figure 16(c) shows the case where the judgment value is "1.0" and the threshold is "0%". Note that even when the threshold is set to "0%", the size (range) of the fifth region R15 does not become "0". This is to leave a range where the user is lying in the center. For example, in the X direction, a range is left that takes into account the difference depending on the user's height, preferably about 300 mm. Also, in the Y direction, a range is left where the user is lying approximately in the center, preferably about 150 mm.

[0176] Then, the judgment value is determined such that the threshold has a linear relationship between Figure 16(a) and Figure 16(c). For example, Figure 16(b) is the result when the judgment value is "0.5" and the threshold is "50%", which is an intermediate value between Figure 16(a) and Figure 16(c).

[0177] Furthermore, while the X and Y directions are uniformly adjusted in Figures 15 and 16, they can also be adjusted individually. For example, referring to Figure 17, and using the second threshold as an example, Figure 17(a) shows the result of adjusting the X-direction threshold to "0%" and the Y-direction threshold to "100%". Figure 17(b) shows the result of adjusting the X-direction threshold to "100%" and the Y-direction threshold to "0%".

[0178] [1.4.1.6 Changes to the conditions for judgment time] The above explanation uses the example of changing the size of the region and the threshold values ​​for XY movement, but other methods may also be used. For example, the control unit 100 may change the conditions for determining the seating position in the first and second prediction processes by changing the determination time, the number of samples, and the operating mode.

[0179] For example, in the second prediction process, if the time spent in the sixth region R16 (elapsed time) is used as the determination time, the control unit 100 may change the determination time. Also, in the first prediction process, the threshold value of the movement amount (first threshold value) may be made variable by combining these values.

[0180] For example, the control unit 100 may be modified to vary the amount of movement according to the back angle, so that the detection by the amount of movement (first threshold) in the first prediction process becomes more sensitive as the back angle increases.

[0181] Furthermore, the control unit 100 adjusts the number of samples in the moving average when calculating the user's second centroid position, but it may also switch the method for calculating the second centroid position. For example, the control unit 100 normally calculates the second centroid position based on a moving average using the centroid position detected over a predetermined time (e.g., 30 sample data), but if the user moves quickly, the larger the number of samples used in the moving average, the more sluggish the determination becomes.

[0182] Therefore, if the user's movement per unit time is large, the control unit 100 may switch to a mode that makes a judgment based on the instantaneous value of one sample data rather than the moving average. In addition, the control unit 100 can improve the situation by observing the user's behavior more carefully when the user's actions are slower and making a judgment more quickly when the user's actions are faster, thereby reducing the excessive or delayed notifications to the caregiver.

[0183] Here, the control unit 100 determines the speed of the user's actions based on the amount of movement of the current center of gravity outside the bed device 10 in 10 seconds, relative to half the width of the bed device 10. For example, if the width of the bed is 900 mm, the standard speed would be 45 mm / s, assuming the center of gravity moves 450 mm in 10 seconds. For example, if the sampling rate is 100 ms, the control unit 100 determines that the user's actions are fast if the distance between samples is greater than 4.5 mm, and slow if the distance between samples is small.

[0184] Furthermore, although the bed device 10 is mostly rectangular in shape, the above standard may be set based on the length of the shorter side (the width of the bed device 10) in order to prevent situations where the patient tends to lean towards the edge of the bed device 10.

[0185] Furthermore, the control unit 100 may switch the above threshold depending on the position of the user's center of gravity. For example, if the user is staying near the center of the bed device 10, the control unit 100 uses the same reasoning as above, and the threshold would be 4.5 mm / sample if the speed at which the user gets out in 10 seconds is used as the standard. However, if the user is staying within 225 mm from the center of the bed device 10, it is preferable to lower the standard further and treat even slight movements as fast actions, as the user is more likely to move to the edge with a slight movement. In this case, the distance from the center to the edge of the bed device 10 may be converted as a percentage, and 225 / 450 mm = 50% may be applied as the standard, switching the standard to 2.25 mm / sample, and fast and slow actions may be determined based on this.

[0186] Furthermore, the control unit 100 may reset the reference values ​​in both the longitudinal and transverse directions relative to the user's center of gravity, and set the lower reference value as the reference. This allows for appropriate reference setting even when the bed is far from the center, given its rectangular shape.

[0187] [1.4.1.7 Changes in conditions due to divergence of the center of gravity] Furthermore, the control unit 100 may switch to a mode in which it determines the user's actions based on the weight on the bed device 10 when the calculation of the user's current center of gravity diverges.

[0188] The method for calculating the user's current center of gravity assumes that the object has weight; therefore, when no load is detected (when it becomes 0 kgf), the control unit 100's first center of gravity position diverges to infinity. If, despite a user being present on the bed device 10, the load detected by the bed device 10 is considered to be 0 kg (weight 0 kg), the control unit 100 will perceive the user's apparent weight as 0 kg, posing a risk of being unable to determine the user's actions.

[0189] Therefore, under conditions where the user's first center of gravity position cannot be calculated and diverges, the control unit 100 switches to a mode that detects the user's actions in response to changes in weight in the bed device 10, rather than the user's first center of gravity position. In other words, the control unit 100 may switch from the first end-sitting position prediction processing to the second end-sitting position prediction processing.

[0190] Furthermore, when the calculation of the user's current center of gravity diverges, the control unit 100 may switch to a mode in which it refers to the total weight of the bed device 10 and determines the user's current center of gravity.

[0191] For example, if the weight detected by the bed device 10 appears to be 0 kg, the control unit 100 may still use not only the weight on the bed device 10 but also the total weight of the bed device 10 to calculate the position of the user's center of gravity in order to estimate the user's approximate behavior.

[0192] For example, the control unit 100 detects not only the weight on the bed device 10 but also the total weight of the bed device 10. By subtracting the weight of the bed device 10 itself from the total weight of the bed device 10, the control unit 100 can obtain the user's weight and calculate the position of the user's center of gravity.

[0193] In addition, if the control unit 100 identifies the weight of the bed device 10 and the weight of the user, it may compensate for the amount of movement of the user's current center of gravity based on their respective ratios. For example, consider a situation where the bed device 10 itself weighs 120 kg and the user weighs 60 kg, totaling 180 kg. In this case, the control unit 100 may assist in the determination by making the amount of movement of the center of gravity appear larger than the total movement of the center of gravity of the 180 kg by multiplying the amount of movement of the center of gravity of the entire 180 kg by 180 / 60 kg. Although the amount of movement of the center of gravity due to the user's movement is mitigated compared to the total weight of the bed device 10, by correcting this using the ratio and amplifying the calculated amount of movement, the control unit 100 can determine the user's condition by judging the approximate user's actions, even if it cannot temporarily grasp the user's actions on the bed device 10.

[0194] [1.4.1.8 Second End Seating Position Prediction Processing] The second end seating position prediction process will now be explained. The second end seating position prediction process is performed by the control unit 100 at S114 in Figure 7. Alternatively, it may be performed in the above-mentioned [1.4.1.7 Condition change due to divergence of the center of gravity].

[0195] The second end-sitting position prediction process predicts the user's end-sitting position based on the load detected on the bed device 10. Here, the control unit 100 may perform the process based on the weight calculated from the load. In this embodiment, load and weight are described as being approximately the same. That is, a load of 1 [kgf] is converted (calculated) as a weight of 1 [kg].

[0196] The second seated position prediction process predicts the user's seated position based solely on weight, without calculating the center of gravity. For example, if the weight change is large, it is determined that the user has left (or remained) the bed. The control unit 100 also determines that the user's posture is seated on the edge of the bed if the weight change is within a predetermined range.

[0197] To give a specific example, when the weight calculated by the bed device 10 drops by -6 kg (for example, equivalent to the weight of the thighs to toes of the minimum user weight assumed by the product), the control unit 100 may consider that the user has placed their feet on the floor and determine that this is a sitting position, and notify the control unit 100 accordingly. Furthermore, when the weight drops by -30 kg (for example, equivalent to the minimum user weight assumed by the product) or more, the control unit 100 may consider that the user has left the bed device 10 and determine that this is an exit from bed, and notify the control unit 100 accordingly.

[0198] [1.4.2 Reporting Function] Next, I will explain the notification function. The notification function is a function in which the control unit 100 issues a notification based on the user's actions on the bed device 10. The actions of the user that trigger a notification are the user's actions and postures, such as getting up, sitting on the edge of the bed, getting out of bed, and being monitored.

[0199] First, the control unit 100 can set whether or not to issue an alert for each action. For example, Figure 18 is a diagram illustrating an example of the setting screen and the alert screen for the alert function. Figures 18(a) and 18(b) are examples of the setting screen for configuring the alert function. On the setting screen, it is possible to set the user's actions and posture, the user's weight, and the time at which the alert is issued. The functions corresponding to the posture and actions that trigger an alert will be described below.

[0200] (1) Get up and report The control unit 100 notifies the user of getting up when it detects that the user has gotten up from the bed device 10. For example, as shown in Figure 18(a), the control unit 100 can switch the timing of the notification based on the time elapsed since the user got up.

[0201] The control unit 100 may also determine the user's standing-up motion from the amount of change in the relative position in the X direction as described above. For example, in the first prediction process, the control unit 100 may detect that the user has stood up when the amount of movement of the center of gravity in the X direction exceeds a predetermined threshold.

[0202] (2) Reporting while sitting on the edge of the table The control unit 100 issues a seated-on-the-edges-of

[0203] Figure 18(b) shows an example of the settings screen when sitting at the edge of the bed is selected as the function. At this time, you can choose whether to send a sitting-at-the-edges-of-beds forecast or a sitting-at-the-edges-of-beds notification.

[0204] Alternatively, the control unit 100 may detect that the user has assumed a seated position on the edge of the table not by a change in the user's current center of gravity, but by a change in weight indicating that the user has placed their feet on the floor.

[0205] (3) Notification of leaving bed The control unit 100 issues a notification when it detects that the user has left the bed device 10. The notification issued by the control unit 100 here refers to the moment the user leaves the bed.

[0206] Alternatively, the control unit 100 may detect that the user has left the bed not by a change in the user's current center of gravity, but by a change in weight indicating that the user has moved away from the bed device 10.

[0207] (4) Monitoring and reporting The control unit 100 issues a notification when a certain period of time has elapsed after the user has left the bed. Here, the control unit 100 can arbitrarily set the certain period of time to, for example, 1 minute to 60 minutes.

[0208] Furthermore, Figure 18(c) shows an example of a notification screen. The notification may be displayed on the display unit 120 of the bed device 10, or it may be displayed on the processing unit 40 or the terminal device 50.

[0209] [1.4.3 Weight Measurement Process] This will be explained in the weight measurement process in this embodiment. The process estimates the user's movements from the center of gravity of the user lying in the bed device 10, and measures and stores the user's weight when the user is in the appropriate position, thereby achieving highly accurate weight storage.

[0210] Furthermore, the weight measurement process in this embodiment can store weight at an appropriate timing. When measuring a user's weight, the timing at which weight measurement is desired differs depending on the purpose. For example, if staff want to measure a user's weight for the purpose of nutritional management, it is preferable to measure the weight at predetermined time intervals. Also, if staff want to measure the weight for the purpose of managing the user's excretion, it is preferable to measure the weight after excretion. Therefore, in this embodiment, the server device 20 is configured with conditions (time, etc.) for measuring weight according to the user's purpose, and the weight is stored at an appropriate timing based on those conditions.

[0211] If the user's weight is not measured and stored at the appropriate time, the bed device 10 or server device 20 notifies the staff. Upon receiving the notification, the staff measures and stores the user's weight, enabling proper weight storage.

[0212] Figure 19 illustrates the process related to weight measurement. It is preferable that some of the processes in Figure 19 be performed by the bed device 10 and the server device 20, respectively. Alternatively, the bed device 10 or the server device 20 may perform the processes independently as needed.

[0213] First, the control unit 100 acquires the load detectable in the bed device 10 (S302). Here, if the control unit 100 exceeds the bed occupancy determination value (S304; Yes), it determines whether the first center of gravity position is within the determination area (S306).

[0214] For example, the occupancy determination value may be determined to be exceeded when the load on the bed device 10 is greater than or equal to a predetermined load (for example, 10 kgf or more, 20 kgf or more, etc.). The control unit 100 may also determine in the first prediction process that the user is in the determination area when the user's current center of gravity is included in the third region R13 or the second region R12. The control unit 100 may also determine in the second prediction process that the user is in the determination area when the user's current center of gravity (first center of gravity) is included in the sixth region R16.

[0215] Furthermore, the control unit 100 may vary the timing at which it determines the presence of a user in bed based on the area where the user's first center of gravity is located. For example, when a user is lying in bed, depending on the user's first center of gravity, it may be difficult to determine whether the user is lying on the edge of the bed or sitting on the edge.

[0216] Here, if the control unit 100 stores the measured weight based on the load when the user is sitting on the edge of the bed, it may store an incorrect value as the user's weight because the weight of the user's feet is omitted. Also, the control unit 100 should be able to immediately store the measurement results when the user is lying down with their body extended on the bed, but at the same time, even in situations where it is not possible to distinguish from the center of gravity whether the user is curled up on the edge of the bed or waiting in a sitting position on the edge of the bed, if the center of gravity is stable in that position, it may be desirable to consider the user as lying down and store that measurement.

[0217] Taking these factors into consideration, the control unit 100 determines the timing for determining the weight measurement value depending on the area. For example, if the user's first center of gravity is within the second area R12, the control unit 100 may determine the measurement value (weight) based on the load as soon as the detected load stabilizes.

[0218] Furthermore, if the user's first center of gravity is within the third region R13, the control unit 100 may determine the measured value (weight) after a certain period of time, such as 5 minutes, has elapsed since the detected load stabilized.

[0219] Furthermore, since the position of the first center of gravity when the user is sitting on the edge of the bed and the position of the first center of gravity when the user is lying down differ depending on the user's height, the control unit 100 may change the determination area based on the user's height data.

[0220] Figure 20 illustrates the seventh region R17. For example, the seventh region R17 may be set using the user's height information stored in the user information. This allows the control unit 100 to determine whether to determine the weight even if the first center of gravity position when a short user is lying down is the same as the first center of gravity position when a tall user is sitting on the edge of the table. For example, the control unit 100 will determine that a short user is lying down and determine the measurement value, but will not determine the load-based measurement value for a tall user unless the first center of gravity position moves to the position expected when lying down.

[0221] Here, the seventh domain R17 may be changed according to the user's height. For example, if the user's first center of gravity is at a distance of 55% of their height from the sole of their foot, and the distance from the calf to the center of gravity during flexion and extension is 20% of their height, then assuming a short user's height of 150cm, the seventh domain R17 would change from (225, 120) to (-75, -120).

[0222] Furthermore, assuming that the tall user's height is 170cm, the R17 region of the seventh domain will be (35,-80) to (135,80).

[0223] The control unit 100 may determine the measured value based on the load as soon as the load on the bed stabilizes, provided that the measurement is within the range of the seventh region R17. In this way, the seventh region may be set as a region in which the measured value based on the detected load is immediately determined.

[0224] Furthermore, if the control unit 100 is outside the seventh region R17 but within the range of the third region R13, it may determine the measured value based on the load when the bed load has been stable for a predetermined time (for example, 5 minutes).

[0225] Furthermore, when there is no information on the user's height, or when the size of the seventh region R17 is below a certain value, such as 100 x 100, the control unit 100 may make the seventh region R17 match the second region R12. Also, when there is no information on the user's height, or when the size of the seventh region R17 is below a certain value, the control unit 100 may set the Y-axis range of the seventh region R17 to be the same as the second region R12, and the X-axis range to be the same as the third region R13. By setting the Y-axis range of the seventh region R17 to be the same as the second region R12 and the X-axis range to be the same as the third region R13, the control unit 100 can apply a range that is expected to cover most users when they are lying down, regardless of their height.

[0226] Returning to Figure 19, the control unit 100 measures the user's weight based on the detected load (weight) (S308). The control unit 100 also acquires the time and environmental information (including one or more pieces of information from among user-related information such as the user's sleeping position, excretion information, and sleep information, as well as ambient information such as the status of the bed equipment and the brightness of the room) when it measures the user's weight (S310).

[0227] The control unit 100 or control unit 200 then determines whether or not weight measurement conditions have been set for the user (S312). If no measurement conditions have been set, the control unit 100 stores the measured weight, time, and environmental information as weight information in the storage unit 110 (S316). The control unit 100 may also transmit the weight information to the server device 20. The server device 20 (control unit 200) stores the received weight information in the storage unit 210.

[0228] Furthermore, the control unit 100 (control unit 200) may store weight information at predetermined intervals. For example, it may store it at regular intervals (6am, 12pm, 6pm, etc.) or at predetermined time intervals. Also, the control unit 100 (control unit 200) may store weight information each time the user gets out of bed and when they get back into bed.

[0229] If measurement conditions exist (S312; Yes), the control unit 100 (control unit 200) determines whether the acquired time, environmental information, and measurement conditions match (S312). To determine if they match, a percentage may be used as the degree of agreement. If it is ±5%, then results within ±72 minutes of time are determined to match and processed accordingly. If the time, environmental information, and measurement conditions match, the control unit 100 (control unit 200) stores the environmental information in the storage unit 110 (storage unit 210). If the measurement conditions do not match (S314; No), the control unit 100 (control unit 200) notifies the user of this fact (S318). If a remeasurement is to be performed, the control unit 100 (control unit 200) repeats the process from S302 to acquire the load on the bed again (S320; Yes → S302). On the other hand, the control unit 100 (control unit 200) terminates the process if it does not perform a remeasurement (S320; No).

[0230] [1.4.4 Measurement Condition Setting Process] The process of setting the measurement condition setting table in the server device 20 will be explained using Figure 21.

[0231] The control unit 100 selects an event corresponding to the measurement conditions (S352). Here, the selection of the event may be done arbitrarily by the staff or automatically by the control unit 100. Next, the control unit 200 displays a list of weights (S354). Then, the control unit 100 selects a representative value from the displayed weights (S356). Then, the control unit 200 reads the time and score corresponding to the representative value (S358) and sets the measurement conditions for the event (S360).

[0232] Figure 22 shows an example of a measurement condition setting table. The measurement condition setting table stores the event to be used as a measurement condition, the conditions for that event, the weight selected at that time, and the time. In addition, the same parameters as the priority table shown in Figure 14 are stored as judgment conditions.

[0233] For example, if a staff member selects nutritional management as an event, the condition is set to weight at the same time, and when the staff member selects a representative weight, the extraction condition becomes "time 6:32" for that weight. Subsequently, from the weights, only those within ±72 minutes of that time will be extracted according to the aforementioned degree of agreement. In another example, if a staff member selects excretion management as an event, the condition is set to the elapsed time after excretion, and when the staff member selects a representative value, the extraction condition becomes "elapsed time after excretion 0.0" for that weight.

[0234] [1.4.5 Anomaly Notification Processing] Next, we will explain the abnormality notification process for weight changes. In this embodiment, weight at appropriate timings is stored for each purpose, making it easier to capture weight changes for each purpose. Therefore, a weight change threshold is set in the condition table for each purpose, and when a weight change exceeds that threshold, the staff is notified. For example, an example of an abnormality judgment table is shown in Figure 23.

[0235] Figure 23 shows the notification scene, the memory table, the memory time for determining the scale of change, and the scale of change. In other words, the control unit 100 determines that an abnormality exists and issues an abnormality notification when the range of the scale of change is exceeded during the memory time.

[0236] For example, if a staff member sets a notification scene for nutritional management for a particular user, the control unit 100 manages the user's weight during the same time period as the representative user's weight, based on the conditions set above. The control unit 100 then indicates that when a certain weight exceeds 0.5 kg in the memory period (set as a memory period of 2 weeks), it triggers a notification to the staff member.

[0237] Here, the measure of change can be weight change [kg], the change in BMI including the user's weight, or the percentage change [%] relative to a representative value. For example, if expressed as a percentage, a consistent value can be set regardless of the user's initial weight, and a consistent index can be applied that is independent of each user's individual weight.

[0238] [1.4.5.1 Processing Flow] An example of an anomaly notification process will be explained with reference to Figure 24. The anomaly notification process is performed on the server device 20.

[0239] The control unit 200 groups the weight by time and score data (S402). First, the control unit 200 obtains the weight for the first period from the present (S404). Here, the first period may be, for example, a period of time in hours. For example, the first period may be set to a predetermined time such as 5 days, 1 week, or 1 month.

[0240] Furthermore, the control unit 200 calculates the user's intake or excretion amount (S406). The user's intake or excretion amount can be calculated, for example, by the control unit 200 from the difference in weight stored when the user gets out of bed and when they stay in bed. The intake and excretion amounts may be treated as the difference in memory before and after the time of eating and excretion as stored by the caregiver, or they may be treated as the difference in memory before and after the time when urination and defecation occurred, based on the memory from sensors that detect urination and defecation, as the excretion amount (amount of urine and amount of feces).

[0241] Next, if data on intake or excretion is available for the second period, the control unit 200 calculates the difference in body weight from the present to the second period (S410). Here, the second period is a shorter period than the first period, and may be, for example, a period of days.

[0242] If the difference calculated in S410 is greater than or equal to the first difference threshold (S412; Yes), the control unit 200 notifies the user that there is an abnormality in the relationship between intake and excretion (S414).

[0243] Furthermore, if the difference is less than the first difference threshold (S412; No) or if there is no intake or excretion data for the second period (S408; No), the control unit 200 obtains the weight for the third period from the present (S416). Here, the third period may be a period of days.

[0244] If the change in weight between the third period prior and the current weight is greater than or equal to the second difference threshold (S418; Yes), the control unit 200 notifies the user of an abnormality that may indicate dehydration (S420).

[0245] If the change in weight between the third period prior and the current weight is less than the second difference threshold (S422; No), but the change in weight between the fourth period prior and the current weight is greater than or equal to the third difference threshold (S422; Yes), the control unit 200 notifies the user of an abnormality that may indicate a nutritional risk (S424). Here, the fourth period may be, for example, a period of months.

[0246] Next, we will explain a specific application example using this process. Weight information, including a user's weight, is automatically or manually stored by the process described above. Here, the control unit 200 applies sorting conditions based on the time and environmental information (e.g., score) included in the weight information, making it possible to group the data.

[0247] (Nutritional management) For example, in the case of nutritional management, the user's weight is grouped in S402 based on weight information from the same "time period," averaged monthly, and converted to a monthly weight in S422. Based on the average value obtained in S422, the control unit 200 refers to the most recently acquired user weight that meets the same conditions in S402 (the latest value from the same time period) as the current value, and makes a judgment in S422. At this time, if the change in current weight compared to the weight one month ago is 0.5 kg or more, the control unit 200 notifies the user of an abnormality, indicating a suspected nutritional risk.

[0248] (Excretion amount management) For example, when the purpose is to manage the excretion volume, the weight is grouped by the "elapsed time after excretion" at S402, and the difference between each data and the data immediately before it is calculated as the excretion volume by comparing them. Also at S412, the control unit 200 groups the data for a specific "time zone", and calculates the difference between each data and the data immediately after it as the intake volume by comparing them.

[0249] Here, the specific time zone may be set as the time when the user has scheduled meals such as breakfast, lunch, dinner, and snacks. Also, considering users with irregular excretion and intake timings, the excretion volume and intake volume may be treated at S406 as the amount that has decreased within the data with the same "date" as the excretion volume, and the amount that has increased as the intake volume.

[0250] When the data of the excretion volume and intake volume are stored at S408, the control unit 200 calculates the difference between the excretion volume and intake volume for each date at S410 and treats it as IN - OUT, and does this for example for the past 5 days. Comparing with the IN - OUT volume 5 days ago, if the decrease in the latest IN - OUT volume is large, an abnormal notification is issued assuming a large excretion volume. On the other hand, if the increase in the latest IN - OUT volume is large, an abnormal notification is issued assuming a large intake volume. Examples of abnormalities such as a large excretion volume being suspected of vomiting or diarrhea, and examples of abnormalities such as a large intake volume being suspected of overeating or binge eating can be detected by an administrator who is not a care worker.

[0251] (Symptom management) For example, when the purpose is symptom management, receiving the weight obtained at S32, each weight is grouped by "time zone" and "date" at S33, averaged daily, and made into a daily weight at S38. For the average value obtained at S38, the value that meets the same conditions at S33 among the weights obtained most recently (the latest value within the same time zone) is used as the current value for reference and judged at S39. Comparing with the data 3 days ago, if the current value has decreased by 0.5 kg or more, an abnormal determination is notified suspecting dehydration symptoms in the user. Comparing with the data 7 days ago, if the current value has decreased by 0.5 kg or more, an abnormal determination is notified suspecting loss of appetite or stress in the user.

[0252] If the current location has increased by 0.5 kg or more compared to the data 3 days ago, an abnormal determination is notified suspecting swelling or heart failure.

[0253] [1.5 Operation Example] An operation example when the above-described process is executed will be described using the display screen as an example. The following operation examples are screen examples when using the weight obtained by the weight measurement process of the present embodiment.

[0254] [1.5.1 Nutrition Management] FIG. 25 is an example of a display screen displayed during nutrition management. When the nutrition management mode is selected, the observation period is displayed from a minimum of 1 month, and each data is limited to be displayed if there is a designation for the same time zone. Further, in order to further abstract the results, all data is plotted or plots averaged every certain period such as once a week are displayed. If the notification setting is set to a change of 0.5 kg in 5 weeks, a graph is drawn based on the data 5 weeks ago, and when the change amount is 0.5 kg or more, a notification is made regardless of the increase or decrease. The scale of change may be the weight change [kg], the change amount with respect to the BMI including the user's weight, or the ratio [%] of the change amount with respect to each representative value.

[0255] Similar to the above-described excretion amount management and symptom management modes, the administrator can also input judgment results such as comments for the notification.

[0256] This system determines whether there is an abnormality in the user from information such as the transition of the user's weight and diagnostic data, and compares the determination result with the input result regarding the judgment of the staff.

[0257] When the determination result and the input result are different, the determination result is abnormal, and the input result is normal, a reconfirmation notification or an alert is displayed.

[0258] [1.5.2 Recommendation Display] Figure 26 shows an example of a display screen related to recommendations. For example, when the diaper / pad selection mode is selected as a recommendation, the calculated excretion amount is plotted over a 24-hour period. The data period can be set arbitrarily, and older and newer data can be distinguished by color (in the example figure, the period is set to 2 weeks, with older data being lighter in color). In addition, by setting thresholds for the amount of excretion that can be tolerated for each diaper / pad capacity, which are set based on the facility's experience, it is possible to identify time periods when these thresholds are exceeded, or conversely, time periods when the amount of excretion is less than half of the capacity. If data is found that the amount of excretion exceeds or is less than half of the capacity of the currently set diaper / pad, the user will be prompted to re-select a diaper / pad.

[0259] Possible methods for re-selection include reviewing (increasing or decreasing) the capacity of diapers and pads, as well as reviewing (increasing or decreasing) the frequency of diaper and pad changes. This will allow for a decision on whether to reduce diaper and pad consumption or reduce the frequency of diaper and pad changes.

[0260] Similar to the previously mentioned excretion volume management and symptom management modes, administrators can also input comments and other judgments regarding notifications. [1.5.3 Weight Information Display Screen] Next, we will explain an example of the weight information display screen. Figure 27(a) is an example of the default display screen. Figure 27(b) is an example of the history display screen. Figure 27(c) is an example of the display screen shown when measuring weight.

[0261] The display screen described above is intended to be shown in a web application and includes suggestions related to the measurement results, with the expectation that staff will view it. On the other hand, for screens that anyone can see, such as those on a bedside terminal, it is important to provide accurate information as a simple scale that is easy for the user to understand.

[0262] What users need when measuring their weight is accurate measurement, and the screen layout should be designed to help them determine whether their current measurement is consistent with past results.

[0263] The UI is designed to present a trend graph on the left as supplementary information, and to help users determine whether the "latest recorded weight," which is the most recently recorded weight stored on the right, is at a reasonable level within the weight trend graph. If, after viewing the trend graph, the user has doubts about the automatically stored results, they can perform a manual measurement on the spot. This result will also be plotted individually on the trend graph, allowing the user to determine whether their own measurement is consistent with previous measurement results. The left area of ​​Figure 27(a) plots the weight trend over a predetermined period (e.g., 35 days). The right area displays the stored weight, the currently measured weight, and the change in weight. Staff members can, for example, select "Manual Record" to store the weight displayed in "Current Value" as the "latest recorded weight."

[0264] As an example of UI division that also takes screen size into consideration, one could provide a UI dedicated to measurement that only displays the latest stored value and the measurement of that value again (Figure 27(c)), and a UI for a history screen that allows users to track past history to see if the measurement results were valid (Figure 27(b)).

[0265] The following describes an example of data processing for drawing to a bedside terminal provided near the bed device 10.

[0266] (1) Plotting of the trend graph The horizontal axis displays past dates. The date range can be adjusted as needed. When viewing the trend over the past month, the display changes to one unit per day after 5 weeks. The vertical axis displays the upper and lower limits from the median. The median and upper / lower limits can be adjusted as needed.

[0267] When you want to read the weight change of 0.5 kg from one month ago, the display will change to a scale with 0.5 kg per graduation within the range of ±2 kg. This makes it easier to observe the changes in the set change amount.

[0268] · Plots are color-coded according to their memory history. Plots automatically remembered by the system as weights are displayed in light blue, and plots measured by the user at an arbitrary timing are displayed in orange. By comparing the quality of the automatic measurement results of the system and the user measurement results, the user can evaluate the reliability of the automatic measurement results and can assist in the judgment of whether to reduce the measurement work by the user.

[0269] · Every time the operation time of the system elapses 24 hours, the automatic measurement results and the measurement results by the user on that day are averaged into one plot each, and the drawing becomes one plot each. When all the data is plotted on the screen, everything such as before and after meals and before and after excretion is reflected, and it is difficult to see the trend of change. Therefore, on the bedside terminal, by averaging the data booked at regular intervals, the daily weight change can be drawn so that the user can easily recognize it.

[0270] · In the process of generating the average plot generated every 24 hours, there is a concern that the plot may be disturbed due to the accidental outlier being stored. Therefore, as a method of excluding outliers, the interquartile range is used. Compare the data within that 24 hours, exclude data with a difference of ±1 kg or more from the average value, and plot the result of calculating the average value again. The interquartile range is used only when 4 or more data are collected. When there is only one data, it is drawn as the average value as it is, and when there are 2 or 3 data, their average values are plotted as they are.

[0271] (2) Display values on the right half • The "Latest Recorded Value" section shows the last measurement value recognized by the system, allowing users to identify whether it was automatically stored or manually stored (measured by the user). The cursor position is also linked to which result is being displayed in the trend graph on the left half of the screen.

[0272] • The "Current Value" displays the calculated weight (body weight) based on the load on the bed that the bed currently recognizes. Pressing the "Zero" button will set the current value to 0.0kg, which refers to the tare weight. The "Manual Record" button is for manually saving the current value. By using these buttons, you can record your weight using the same measurement procedure as a typical bathroom scale.

[0273] • The "Change" is calculated and displayed by sequentially determining the difference between the "Latest Recorded Value" and the "Current Value." The display unit can be changed; in the example, it is set to grams. This is a visualization tool for visually checking changes before and after an action. For example, if observed after a diaper change, it will represent the amount of excretion, and the difference when a person gets out of bed for excretion assistance and then returns to bed afterward will also represent the amount of excretion. The difference between the stored values ​​before and after detection by sensors that detect urination and defecation can also be treated as the amount of urine and feces excreted. It can also be used as reference information to estimate food and fluid intake.

[0274] [1.5.4 Display screen of the administration panel] Figure 28 shows an example of the management screen when a notification is received. Figure 28(a) is a list of users for a facility or hospital. Users with abnormalities are marked with an identification mark.

[0275] Figure 28(b) is an example of a display screen (details screen) that is individually displayed when a user selects from Figure 28(a). On the details screen, it is possible to input judgment results such as comments. This system can determine whether or not there is an abnormality in the user based on information such as the user's weight changes and diagnostic data, and can compare the judgment result with the input result regarding the staff's judgment. For example, if the judgment result and the input result differ, and the judgment result is abnormal while the input result is normal, a notification or alert can be displayed to ask for reconfirmation.

[0276] [1.5.5 Anomaly Notification Display Screen] An example of a display screen when an abnormality notification is received is described below. Figure 29(a) is an example of a display screen shown on the terminal device 50. It is preferable that Figure 29(a) be displayed as a pop-up or widget on the terminal device 50, for example.

[0277] Figures 29(b) and (c) show display screens where data can be entered. When an abnormality is detected, staff can input observation items from the terminal device 50.

[0278] [1.5.6 Weight Input Display Screen] Figure 30(a) shows an example of a display screen where, for example, staff can input a user's weight using the terminal device 50. In other words, this screen is the application UI of the terminal device 50 that supplements the results when the weight obtained does not satisfy the time period and attribute information required by the system.

[0279] After the notification, the bed device 10 measures the weight, and once the result is transmitted, the notification is resolved. However, taking the following situations into consideration, manual input from the terminal device 50 is also accepted.

[0280] • When you cannot perform measurements according to the specified conditions, but want to send a provisional result. • When measuring weight using a function other than the weight measurement function of the bed device 10 and sending the results, the system can also take into account cases where the weight has been stored using other equipment and reflect those results.

[0281] Figure 30(b) is an example of a display screen that shows a notification when weight is not stored under predetermined conditions set by the staff. Preferably, it is displayed as a widget on the terminal device 50.

[0282] [2. Second Embodiment] [2.1 Overview of Embodiments] Next, a second embodiment will be described. The second embodiment describes an embodiment that uses the system for measuring the user's weight described in the first embodiment and automatically stores the weight. Regarding the system configuration, hardware configuration, and software configuration, the common parts with the first embodiment will be omitted from the explanation, and this embodiment will focus on the differences.

[0283] In this embodiment, the objective is to accurately store the user's weight. For example, in this embodiment, various methods are used to solve the following problems.

[0284] The first challenge is that, in a bed device, it is unclear whether a user is actually in the bed when a load equivalent to that of a user is detected. To solve this first challenge, the system stores the weight when a weight generally heavier than the weight of any attached objects is detected. For example, the control unit 100 considers that a user is in the bed and stores the weight when there is a load change of 10 kgf or more. This allows staff to store the weight without having to perform initial setup.

[0285] Furthermore, to solve the first problem, the control unit 100 may exclude measurement values ​​that fall outside a certain range from a preset weight or multiple weight measurements. In this case, the control unit 100 can reliably exclude weights based on loads other than the user's weight, but staff or others will need to set the user's weight in advance.

[0286] Furthermore, external devices may be used to solve the first problem. For example, a camera device for recognizing users may be connected to system 1. When the control unit 100 determines from the image captured by the camera device that a user is in bed, it may store the user's weight.

[0287] Furthermore, a second issue is that, depending on the user's sleeping position on the bed device 10, it is not possible to determine whether the user is lying down (e.g., reclining) or in a seated position on the edge of the bed. Therefore, to solve this second issue, the control unit 100 may immediately determine the measured value as the user's weight when the first center of gravity is in a position that clearly indicates the user is reclining (e.g., in the seventh region R17). Alternatively, if the user is not in a position that clearly indicates the user is reclining (in the seventh region R17), the control unit 100 may consider the user to be reclining and store this value as their weight if the measured value remains stable for a while.

[0288] Furthermore, to address the second issue, the system may determine whether to maintain or cancel the previous measurement result (weight) based on the user's condition. For example, if the detected load is a provisional 50 kgf, and the control unit 100 subsequently detects the user "sitting on the edge" or "getting out of bed," it will not store the detected provisional measurement of 50 kgf, assuming that the user was sitting on the edge of the bed device 10.

[0289] On the other hand, if the detected load is provisionally 60 kgf, and the control unit 100 subsequently detects the user getting up or predicting sitting on the edge of the bed, it may determine that the user was lying down and store 60 kg as the user's weight based on this 60 kgf.

[0290] Furthermore, to solve the second problem, external devices may be utilized as described above. For example, a camera device for recognizing users may be connected to System 1. The control unit 100 may determine whether the user is in bed or not from the image captured by the camera device and store the user's weight.

[0291] Furthermore, a third challenge is that objects other than the user may be placed on or removed from the bed, which can be reflected in the measurement values. Therefore, the control unit 100 may calculate the user's weight based on the difference between the average value over a predetermined period of time before a predetermined timing when the user leaves or enters the bed (for example, the average value over a predetermined period of time one minute prior) and the load. Alternatively, when the measurement values ​​are updated, the control unit 100 may re-detect the reference load (for example, the average value over a predetermined period).

[0292] Furthermore, a fourth challenge is that even with the measures described above, outliers may still be present.

[0293] Therefore, the control unit 100 may implement a method for excluding outliers during aggregation. For example, when aggregating based on conditions, the control unit 100 may calculate the median among the results under the conditions and exclude results outside a certain range from that median. Alternatively, the control unit 100 may calculate a single average value from the excluded results. For example, the control unit 100 may exclude outliers in aggregating 24-hour results, aggregating 1-month results, and weekly aggregating daily measurement results.

[0294] [2.2 Processing Flow] These specific processes will be explained with reference to the diagram. The following terms will be used in the processes described below.

[0295] Latest load: Load sampled at a predetermined period (e.g., 100 milliseconds). Current load: The load averaged over a specified time period (e.g., 2 seconds) based on the most recent load. n-minute average: The average calculated over n minutes of the most recent load. For example, in this embodiment, n=5 is used as an example, but it is not limited to this value.

[0296] m minutes ago n-minute average: The average value of n minutes from m minutes ago, starting from when the memory information update flag was set. For example, in this embodiment, m=1, but it is not limited to this value.

[0297] Load change: Difference between current load and the average load from m minutes ago (n minutes). Memory value: The amount of load change that is retained when the memory condition is met.

[0298] [2.2.1 Automatic Memory Control Processing] Figure 31 illustrates the automatic weight memory control process in this embodiment. The control unit 100 performs a memory information update process (S1002). The memory information update process will be explained later using Figure 32. The control unit 100 also performs a load change determination process (S1004). The load change determination process determines whether or not there has been a load change that appears to be that of a person, and will be explained later using Figure 35.

[0299] The control unit 100 then performs a center of gravity position determination process (S1006). The center of gravity position determination process determines whether the user's position when lying down is at the center (for example, the second or seventh region) or at the edge (for example, the third region). The center of gravity position determination process will be explained later using Figure 36.

[0300] Then, the control unit 100 executes a memory type determination process (S1008). Based on the determination result of the load change determination process and the determination result of the center of gravity position determination process described above, the memory type determination process executes the following types of processes.

[0301] 0: The condition is not met, so weight will not be stored based on load.

[0302] 1: Since the change (increase) is 1.20 kgf or more, and the first center of gravity is near the center, the amount of change will be determined immediately once the current load stabilizes. This is because a person-like figure is lying down in that position, so the weight will be calculated immediately based on the amount of change.

[0303] 2. If the change (increase) is 20 kgf or more, and the center of gravity is located near the edge, the weight will be calculated based on the change in load if the current load remains stable for a specified time (e.g., 5 minutes). This is because, although it appears that a person is on the bed, it is unclear whether they are sleeping or if part of their body is outside the bed (e.g., sitting on the edge), so if the change in load remains stable after observing for a while, the weight will be calculated based on that change.

[0304] 3. If the change (decrease) is 20 kgf or more, and the remaining load after the object dismounts is less than or equal to a predetermined load (e.g., 5 kgf), the weight will be calculated immediately based on the change in load as soon as the current load stabilizes. This means that if something resembling a person dismounts and the amount of weight removed is roughly equal to the weight of the person who was on it, the system will assume that the user has dismounted and immediately calculate the weight based on that change in load.

[0305] 4. If the change (decrease) is 20 kgf or more, and the remaining load after dismounting is equal to or greater than a predetermined load (e.g., 5 kgf), the current load will stabilize for a certain period (e.g., 5 minutes). If the load remains stable, the weight will be calculated based on the change in load. This means that if something resembling a person dismounts and the amount of weight removed is less than the weight of the person who was on it, it is assumed that the user has not yet dismounted, and the situation is monitored. If the change in load remains stable after monitoring for a while, the weight will be calculated based on that change in load.

[0306] The control unit 100 determines whether the memory permission flag (F) is "ON" (S1010). If the memory permission flag F is not "ON", the control unit 100 proceeds to S1024.

[0307] Next, when the memory permission F is "ON", the control unit 100 first determines whether the load has decreased (S1012). If the load has decreased, the control unit 100 sets the zero-point update flag (F) to "ON" (S1014).

[0308] Here, zero-point update F is a flag that gives an instruction to update the zero point, which will be described later. Zero-point update F may be manually set to "ON" by the user, staff, etc. Also, in S1014, the control unit 100 may set it to "ON".

[0309] Next, the control unit 100 stores the load change amount (S1016), calculates the user's weight based on this load change amount, and updates it as the latest information (S1018). Then, the control unit 100 updates the reference based on the load change amount (S1020). For example, the control unit 100 updates the n-minute average value and the m-minute previous n-minute average value with the current load change amount.

[0310] Furthermore, the control unit 100 stops any timers that are currently operating (S1022). In this embodiment, the control unit 100 stops the timers that the timer unit 108 is counting, for example, the first timer (for example, a 5-minute timer) and the second timer (for example, a 3-second timer).

[0311] The control unit 100 then repeatedly executes this process until the memory processing is completed (S1024; No → S1002). Also, if the user turns off the automatic weight memory function or instructs the user to terminate this process, the control unit 100 terminates this process (S1024; Yes).

[0312] [2.2.2 Memory Information Update Process] The memory information update process will be explained with reference to Figure 32. The control unit 100 acquires the latest load (S1102). When the zero-point update flag (F) is "ON", the control unit 100 executes the reference value reset process (S1106). When the zero-point update F is "OFF", the control unit 100 executes the n-minute average value update process (S1108) and the m-minute prior n-minute average value update process (S1110). Then, after executing the memory information update process, the control unit 100 sets the zero-point update F to "OFF" (S1112). Note that S1112 may be executed only when S1104 is Yes.

[0313] For example, a decision on whether to reset the baseline value or update the average value over n minutes and the average value over m minutes prior may be made using a process other than those described above.

[0314] For example, the control unit 100 may stop updating the n-minute average value and the m-minute previous n-minute average value when there is a change of load greater than or equal to a predetermined load (e.g., 20 kgf), and then resume updating the n-minute average value and the m-minute previous n-minute average value when it determines that the measured value has stabilized, or it may perform a reset of the reference value.

[0315] For example, the control unit 100 may reset the reference value when the first centroid position leaves the second region R12 or the seventh region R17 and enters the third region R13, and then returns to the second region R12 or the seventh region R17. For example, the control unit 100 may stop updating the n-minute average value and the m-minute previous n-minute average value when the first centroid position leaves the third region R13 and enters the fourth region R14, and then resume updating the n-minute average value and the m-minute previous n-minute average value when it returns to the third region R13.

[0316] Furthermore, these processes may be executed in combination. These processes are effective in ensuring that reference values ​​remain accurately set even under conditions where automatic memory control processing is not performed. For example, even if a caregiver kneels on the bed to assist a user and places an object down, resulting in a change in load, the reference value can be reset and reflected accordingly, so that even if the user gets out of bed afterward, only the user's weight can be tracked.

[0317] Specifically, when a caregiver kneels on the bed, the user's first center of gravity on the bed shifts to the position of the combined center of gravity of the user and the caregiver. This shift moves beyond the second region R12 into the third region R13, and the bed becomes aware of these changes when there is a load change exceeding a predetermined value. When the user returns to their original position, the bed can be considered to be lying down alone again, thus resetting the baseline.

[0318] Furthermore, if a user maintains a seated position on the edge of the bed for a certain period of time, weight is lost from the feet and thighs, resulting in a gradual decrease in the average weight at n minutes and the average weight at m minutes prior. This can lead to a decrease in the weight automatically recorded when the user gets out of bed, but these issues can also be prevented.

[0319] Specifically, when a caregiver attempts to position the user in a seated position, the user's first center of gravity on the bed moves out of the third region R13 and into the fourth region R14. At this point, by stopping the updating of the n-minute average value and the m-minute previous n-minute average value, a gradual decrease in each average value due to the removal of weight from the feet and thighs is prevented, and the weight automatically stored when the user gets out of bed thereafter can accurately record the user's weight.

[0320] The reference value reset process is a process that resets various parameters to reference values. For example, the average value over n minutes, the average value over n minutes from m minutes ago, and the reference value are updated with the latest load obtained in S1102. Also, if the control unit 100 has used parameters such as counters and buffers during execution, they may be cleared to their initial values, or if the parameter indicates a load, it may be updated to the latest load.

[0321] The n-minute average value update process is a process that updates the average value over n minutes. Here, the n minutes in the n-minute average value process include the following two concepts.

[0322] (1) Calculate the average value based on n minutes of data. The control unit 100 acquires the current load for n minutes. For example, when the load (latest load, current load) is acquired in 100ms increments, if the average value over 5 minutes is to be acquired, 300 loads are used as load data. Here, the control unit 100 selects values ​​to be used as loads that are stable. For example, if the acquired loads have a difference of more than a threshold (e.g., 500gf), those loads are not used in the calculation of the average value. In this way, the control unit 100 can calculate the n-minute average value of a stable load.

[0323] (2) Calculate the average value based on data over n minutes. The control unit 100 acquires the current load and goes back n minutes. In the same example as (1), the average value is calculated using the loads from the present to 300 loads prior. In this case, the control unit 100 does not determine whether the load is stable or not, so it can process at high speed.

[0324] The control unit 100 then stores the n-minute average value calculated in the n-minute average value update process in the storage unit 110. Preferably, the control unit 100 stores at least the number of n-minute average values ​​that will be used for the next m-minute prior n-minute average value. The control unit 100 may also store the n-minute average values ​​as needed, deleting older ones, or it may store them cumulatively.

[0325] [2.2.3 Processing to update the average value for n minutes from m minutes ago] Next, the operation of the m-minute prior n-minute average value update process, which the control unit 100 performs in S1110 of Figure 32, will be explained with reference to Figure 33. The control unit 100 determines whether the latest load has changed by more than a predetermined value (S1302). In this embodiment, the predetermined value is 20 kgf as an example.

[0326] The control unit 100 updates the n-minute average value from m minutes ago if there is no change of 20 kgf or more (S1302; Yes → S1304). For example, if m=1, it calculates the n-minute average value based on 1 minute ago from the present.

[0327] For example, if the current time is 9:00:00, m=1, n=5, and the load is stable, the control unit 100 calculates the respective parameter values ​​based on the current load within the following range.

[0328] n-minute average = average value of current load from 8:55:00 to 9:00:00 m minutes prior n minutes average = average value of current load from 8:54:00 to 8:59:00 In this way, the control unit 100 updates the n-minute average value from m minutes ago based on the current time. However, if the control unit 100 determines that the latest load has changed by a predetermined value (20 kgf) or more (S1302; No), it does not update the n-minute average value from m minutes ago and maintains it as is (S1306). As a result, even if the evaluation of the amount of change is triggered when a change of a predetermined value or more occurs, the body weight can be handled based on the difference between the resting load before the change of a predetermined value or more occurred and the current load after the change of a predetermined value or more occurred, enabling more accurate measurement. For example, without this processing, changes of a predetermined value or more would be included in the n-minute average value, which would impair accurate body weight measurement.

[0329] [2.2.4 Load Change Determination Process] The load change determination process performed in S1004 in Figure 31 will be explained with reference to Figure 34.

[0330] The control unit 100 sets the load change flag (F) to "0" as an initial value (S1402). Next, the control unit 100 subtracts a reference value from the current load to determine the load change amount (S1404). Here, if the load change amount is greater than or equal to a predetermined value (for example, 20 kgf in this embodiment), the control unit 100 determines that the load has increased and sets the load change F to "1" (S1406; Yes → S1408). On the other hand, if the load change amount is less than or equal to a predetermined value (for example, -20 kgf in this embodiment), the control unit 100 determines that the load has decreased and sets the load change F to "2" (S1406; No → S1410; Yes).

[0331] In other words, the control unit 100 determines, through load change determination processing, that when the load increases by more than a predetermined threshold, the load change F is set to "1", and when the load decreases by more than a predetermined threshold, the load change F is set to "2".

[0332] The load change F may be reflected in the load condition setting table. For example, the control unit 100 adds an item for weight increase / decrease to the weight, time, and score data (judgment conditions) in Figure 22 of the load condition setting table, and applies the load change F to the input value. This allows the control unit 100 to identify and process whether the stored weight is from when the person got on the bed or when they got off.

[0333] For example, depending on the user's characteristics, when the user gets out of bed, the caregiver may lift the user and get out together, which can disrupt the amount of load change and result in the recorded weight being different from the actual weight. However, when the user is lying down, they can get into bed on their own, making it easier to record accurate load change amounts. In such cases, by adding information about getting in and out of bed to the recorded weight, it is possible to filter out only the data when the user is in bed (load change F=1), allowing only the most accurate recorded values ​​to be used for the required processing. The filter for getting in and out of bed can be set by the staff, or, as described in [1.4.4 Measurement Condition Setting Process], it can be set based on the same load change F as the representative value selected by the staff.

[0334] [2.2.5 Center of Gravity Position Determination Process] The center of gravity position determination process performed in S1006 in Figure 31 will be explained with reference to Figure 35.

[0335] The control unit 100 sets the center of gravity position flag (F) to "0" as an initial value (S1502). Subsequently, the control unit 100 sets the center of gravity position F to "1" if the user's first center of gravity position is within the second or seventh region (S1502; Yes → S1504). Also, the control unit 100 sets the center of gravity position F to "2" if the user's first center of gravity position is within the third region (S1502; No → S1506; Yes → S1508).

[0336] In other words, the control unit 100 determines the center of gravity position and sets the center of gravity position F to "1" when the user's first center of gravity position is near the center on the bed device 10 (for example, in the second or seventh region), and sets the center of gravity position F to "2" when it is at a position other than the center (peripheral region) on the bed device 10 (for example, in the third region).

[0337] [2.2.6 Memory Type Determination Process] The memory type determination process executed in S1008 of Figure 31 will be explained with reference to Figures 36 and 37.

[0338] The control unit 100 sets the memory type flag (F) to "0" and the memory permission flag (F) to "OFF" as initial values.

[0339] Next, the control unit 100 updates the memory type F. Specifically, when the load change F is "1" and the center of gravity position F is "1" (S1604; Yes), the control unit 100 sets the memory type F to "1" (S1606). Also, when the load change F is "1" and the center of gravity position F is "2" (S1604; No → S1608; Yes), the control unit 100 sets the memory type F to "2" (S1610). Also, when the load change F is "2" and the current load is less than 5 kgf (S1608; No → S1612; Yes), the control unit 100 sets the memory type F to "3" (S1614). Furthermore, when the load change F is "2" and the current load is 5 kgf or more, the control unit 100 sets the memory type F to "4" (S1618).

[0340] Next, the process transitions to Figure 37, where the control unit 100 determines whether the weight can be stored if the load is stable and the memory type F is not "0" (S1642; Yes).

[0341] Specifically, the control unit 100 executes a 5-minute timer count process as the first timer when the previous memory type flag (F) was "0" and the memory type F is "2" or "4" (S1644; Yes → S1646). Here, the 5-minute timer count process performs, for example, the following process.

[0342] For example, if the 5-minute timer is not operating as the first timer, the control unit 100 will start a new 5-minute timer. Also, if the 5-minute timer is already operating as the first timer, the control unit 100 will continue the timer's operation.

[0343] Furthermore, the control unit 100 may reset the 5-minute timer as needed. For example, if S1646 is executed again but the memory type F has changed or the current load has changed, the control unit 100 may determine that the user's state has changed and reset the 5-minute timer.

[0344] Then, when the control unit 100 determines that the 5-minute timer has expired (i.e., 5 minutes have passed), it sets the memory permission flag (F) to "ON" (S1648; Yes → S1650). Also, the control unit 100 sets the previous memory type F to the current memory type F (S1652).

[0345] Furthermore, if the previous memory type F was "0" and the current memory type F is "1" or "3", the control unit 100 executes a 3-second timer count process as a second timer (S1660; Yes → S1662). Here, the 3-second timer count process performs, for example, the following:

[0346] For example, the control unit 100, acting as a second timer, starts a new 3-second timer if the 3-second timer is not operating. Also, if the 3-second timer is already operating, the control unit 100 continues the timer's operation.

[0347] Furthermore, the control unit 100 may reset the 3-second timer as needed. For example, if step S1662 is executed again but the memory type F has changed or the current load has changed, the control unit 100 may determine that the user's state has changed and reset the 3-second timer.

[0348] Then, when the control unit 100 determines that the 3-second timer has expired (i.e., 3 seconds have elapsed), it sets the memory permission flag (F) to "ON" (S1664; Yes → S1666). Also, the control unit 100 sets the previous memory type F to the current memory type F (S1652).

[0349] Here, the control unit 100 stops the timers if the load is unstable (S1642; Yes), if the memory type F is "0" (S1642; Yes), if the previous memory type F is not "0" (S1644; No → S1660; No), or if there are timers already in operation (a 5-minute timer as the first timer and a 3-second timer as the second timer) (S1670). Also, the control unit 100 sets the previous memory type F to "0" (S1672).

[0350] Memory type F may be reflected in the load condition setting table. For example, an item called "memory type for weight" can be added to the weight, time, and score data (judgment conditions) in Figure 22, and memory type F can be applied to the input value. This makes it possible to identify and process the weight measured when the user got in and out of bed. For example, depending on the user's characteristics, even if they lie down on their own, they may fall asleep with part of their body resting on something other than the bed, and their sleeping position may be in the third region R13, resulting in a stored weight that is not the actual weight. However, if a caregiver (staff, etc.) puts the user to sleep, the stored weight may include information on how the user got into the bed. In such cases, if the entire body is in the bed and the sleeping position is in the second region R12, and an accurate weight tends to be obtained, filtering only when the sleeping position after lying down is in the second region R12 (memory type F=1) allows only the most accurate stored value to be used for the required processing.

[0351] The filter for memory type F may be set by the staff, or, as described in [1.4.4 Measurement Condition Setting Process], it may be set based on the same memory type F as the representative value selected by the staff.

[0352] [3. Effects, etc.] By applying each of the embodiments described above, any of the bed device, server device, and terminal device of the present disclosure, or a combination thereof, can achieve the following effects.

[0353] Firstly, the bed device of this disclosure can appropriately predict when the user's posture will be a seated position on the edge of the bed. For example, the bed device of this disclosure can predict and notify the user that they will be in a seated position on the edge of the bed based on the user's center of gravity position (first center of gravity position, second center of gravity position) and the amount of movement of the center of gravity position while they are on the bed device 10.

[0354] Secondly, the bed device of this disclosure can vary the area used for predicting and determining whether the user is sitting on the edge of the bed, or vary the threshold used for determination, based on a table that evaluates the state of the bed device, the user's state, and the surrounding environment. This makes it possible for the bed device of this disclosure to make an appropriate prediction of whether the user is sitting on the edge of the bed, depending on the state of the bed device, the user's state, and the surrounding environment.

[0355] Thirdly, the bed device of this disclosure can appropriately store the user's weight based on the load detected by the bed device 10. In particular, the bed device of this disclosure can appropriately store the user's weight by utilizing the user's center of gravity.

[0356] Fourth, the bed device of this disclosure can store the user's weight when the user leaves the bed device 10. The bed device of this disclosure can also store the weight information (e.g., in a table) along with the measurement conditions (attribute information) of that weight. This makes it possible for staff, for example, to filter the necessary information according to the desired event. Furthermore, the table that stores the weight may also store various other information necessary for evaluating the bed device's status, the user's status, and the surrounding environment, such as information about the bed device, information from the sleep sensor, and information from the urination / excretion detection sensor.

[0357] Fifth, the bed device and the like of this disclosure can, when automatically memorizing the user's weight, appropriately determine whether to update, stop updating, or reset the reference value based on the user's center of gravity, and execute each process accordingly. As a result, the reset of the reference value, which would normally be done at the discretion of the staff, will be performed at the appropriate time without them having to consciously think about it.

[0358] Sixth, when the bed device, etc. of this disclosure automatically stores the user's weight, measurement conditions (attribute information) for getting on and off the bed and how to get on and off are assigned to store the weight, which are reflected in an evaluation table and can be filtered according to the application.

[0359] Seventh, the bed device of this disclosure can notify staff of any abnormalities when the purpose of weight measurement is specified by staff, etc., and can share instructions with the assigned caregiver or a remote caregiver. In addition, the bed device of this disclosure can prompt the assigned caregiver to take the measurement if the user has insufficient memory.

[0360] Eighth, the bed device, etc. of this disclosure can provide a user interface tailored to the purpose of weight measurement in any of the devices. For example, the bed device, etc. of this disclosure can be provided as a user interface for a web application.

[0361] [4. Variant] Although embodiments of this invention have been described in detail above with reference to the drawings, the specific configuration is not limited to these embodiments, and designs and the like that do not depart from the spirit of this invention are also included in the scope of the claims.

[0362] Furthermore, in the embodiments, the programs that run in each device are programs that control the CPU and other components (programs that make the computer function) in order to realize the functions of the embodiments described above. The information handled by these devices is temporarily stored in a temporary storage device (e.g., RAM) during processing, and then stored in various storage devices such as ROMs, HDDs, and SSDs, and read, modified, and written by the CPU as needed.

[0363] Furthermore, when distributing the program to the market, it can be stored on a portable recording medium and distributed, or transferred to a server computer connected via a network such as the Internet. In this case, the storage device of the server computer is, of course, also included in the present invention. [Explanation of symbols]

[0364] 1 System 10 Bed equipment 100 Control Unit 110 Storage section 110A ROM; 110B RAM; 110C Storage 120 Display section 130 Operation section 140 Hochi Department 150 Drive control unit 155 Drive unit 160 Detection unit 165 Load detection device 190 Communications Department 20 Server Devices 200 Control Unit 210 Storage section 210A ROM; 210B RAM; 210C Storage 290 Communications Department 30 Terminal devices 300 Control Unit 310 Storage section 310A ROM; 310B RAM; 310C Storage 320 Display 330 Operation section 340 Hochi Department 390 Communications Department 40 Processing Unit 400 Control Unit 410 Storage section 410A ROM; 410B RAM; 410C Storage 420 Display section 430 Operation section 440 News Department 490 Communications Department 50 Terminal devices

Claims

1. A bed device comprising a detection unit for detecting the load of a user on the bed device and a control unit, The control unit, A first area and a second area are set on the surface of the bed device where the user is located. From the aforementioned load, the current center of gravity position of the user on the bed device is determined, The amount of movement between the current center of gravity position and the past center of gravity position is calculated. When the current center of gravity is in the first region, it is predicted that the user will assume a seated position based on the current center of gravity and the amount of movement. When the current center of gravity is in the second region, it is predicted that the user will assume a seated position based on the current center of gravity. Bed equipment.

2. The control unit, A third area and a fourth area are set on the surface of the bed device where the user is located. From the aforementioned load, the current center of gravity position of the user on the bed device is determined, When the current center of gravity is in the third region, and the load was stable for the first time period, the load after the first time period is stored as the user's weight. When the current center of gravity is in the fourth region, and the load remained stable for the second time period, the load after the second time period is stored as the user's weight. The bed device according to claim 1.

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