JP7342863B2 - Computer-executed programs, information processing systems, and computer-executed methods - Google Patents

Description

TECHNICAL FIELD This disclosure relates to data processing, and more particularly to data processing based on walking trajectories.

Techniques for monitoring residents are known. For example, Japanese Patent Application Laid-open No. 2015-215711 (Patent Document 1) states, ``In order to monitor relative changes in the behavior and ecology of residents in a way that closely follows the lives of residents, we monitor the progress of aging, which has significant individual differences. , a monitoring system that can perform a relative evaluation according to the resident's abilities and environment and present monitoring specifications that match the evaluation (see [Issue] in [Summary]).

Japanese Unexamined Patent Application Publication No. 2017-117423 (Patent Document 2) discloses "a monitoring system that can grasp the daily activities of a person to be monitored, including the walking speed." The monitoring system "includes a plurality of slave units 2 (2A to 2G) installed in residence H and a master unit 3. Each slave unit 2 transmits a notification signal to the base unit 3 when sensing the target person. Based on the time data of the notification signal from the handset 2D and the time data of the notification signal from the handset 2E, the base unit 3 calculates the travel time of the person to be watched from the bedroom PD to the toilet PD, and The walking speed of the person to be watched is calculated based on the time. ” (see [Summary]).

Japanese Patent Application Publication No. 2015-215711 JP 2017-117423 Publication

Certification of the level of care for people who require watching over or nursing care is done based on, for example, interviews with the person and the caregiver, or the opinion of the attending physician, and it is not possible to measure quantitatively, so it is difficult to determine the level of care required. It may take some time. Furthermore, the measured values may change depending on the length of stay. The degree of change in the measured value (for example, walking speed, etc.) differs depending on the resident's physique, age, gender, etc. Therefore, by simply comparing only the measured values, changes in the resident's condition may not be properly understood. Therefore, there is a need for technology that can appropriately grasp changes in a resident's condition.

The present disclosure has been made in view of the above-mentioned background, and an objective in one aspect is to provide a technique that can appropriately grasp changes in a resident's condition.

According to an embodiment, a program executed on a computer is provided. The program instructs the computer to acquire walking trajectory data representing the resident's walking trajectory, to calculate the walking speed of the resident based on a plurality of walking trajectory data, and to calculate the walking speed of the resident in advance based on the resident's attributes. and normalizing the walking speed based on the determined coefficient.

According to one embodiment, the resident attributes include any of age, gender, height, or weight.

According to one embodiment, the program causes the computer to determine the care needs of the resident based on a comparison of normalized values of each walking speed calculated based on walking trajectory data obtained on a plurality of different days. A further step of determining the degree is executed.

According to other embodiments, an information processing device is provided. The information processing device includes a memory and a processor coupled to the memory. The processor acquires walking trajectory data representing the resident's walking trajectory, calculates the walking speed of the resident based on the plurality of walking trajectory data, and adds a predetermined walking speed to the walking speed based on the resident's attributes. The walking speed is configured to normalize the walking speed based on the calculated coefficient.

In the information processing device according to another embodiment, the resident's attributes include age, gender, height, or weight.

According to another embodiment, the processor determines the care level of the resident based on a comparison of values obtained by normalizing each walking speed calculated based on walking trajectory data acquired on a plurality of different days. further configured to determine.

According to yet another embodiment, a computer-implemented method is provided. This method includes the steps of acquiring walking trajectory data representing the resident's walking trajectory, calculating the walking speed of the resident based on a plurality of walking trajectory data, and predetermined walking speed based on the resident's attributes. and normalizing the walking speed based on the determined coefficient.

In a method according to another embodiment, the resident's attributes include any of age, gender, height, or weight.

According to yet another embodiment, the method includes determining whether the resident is in need of care based on a comparison of values obtained by normalizing each walking speed calculated based on walking trajectory data acquired on a plurality of different days. The method further includes the step of determining the degree.

The program executed on a computer, the information processing device, and the method executed on a computer according to the present disclosure can appropriately grasp changes in a resident's condition.

These and other objects, features, aspects and advantages of the present invention will become apparent from the following detailed description of the invention, taken in conjunction with the accompanying drawings.

1 is a diagram showing an example of the configuration of a monitoring system 100. FIG. 1 is a block diagram showing an overview of the configuration of a monitoring system 100. FIG. 3 is a block diagram showing the hardware configuration of a computer system 300 that functions as a cloud server 150. FIG. 1 is a diagram illustrating an example of a schematic device configuration of a monitoring system 100 using a sensor box 119. FIG. FIG. 3 is a diagram showing one aspect of data storage in a hard disk 5 included in a cloud server 150. FIG. FIG. 3 is a diagram showing changes in a resident's walking trajectory in a certain situation. 5 is a diagram showing one mode of data storage in a hard disk 5. FIG. It is a diagram showing the correspondence between walking speed and the level of nursing care or support required that is proposed according to the walking speed. It is a flowchart showing part of the processing executed by the CPU 1 of the cloud server 150. 8 is a diagram showing an example of a display on a monitor 8. FIG. It is a figure showing an example of a diagnostic report of walking speed.

Hereinafter, embodiments of the technical idea according to the present disclosure will be described with reference to the drawings. In the following description, the same parts are given the same reference numerals. Their names and functions are also the same. Therefore, detailed descriptions thereof will not be repeated.

[Technical philosophy]
An overview of the technical idea disclosed in this specification will be explained.

In one aspect, observed values (such as walking speed) of a subject such as a resident of a facility are normalized. Using normalized observed values, it is possible to evaluate all subjects using the same criteria.

For example, there is a person whose walking speed is 70cm/s (Mr. A) and a person whose walking speed is 30cm/s (Mr. B), and Mr. A's walking speed decreases by 5cm/s, and Mr. B's walking speed decreases by 5cm/s. There is a different way of looking at it when it goes down. Use an offset to normalize the difference in how it falls. This makes it easier to determine the level of care required for a subject such as a resident.

In another aspect, a table is prepared that determines normalization coefficients depending on age, sex, and physique (height or weight). By using the coefficients derived from each, a normalized value that is not related to age, gender, height, or weight is obtained.

In yet another aspect, the nursing care level of the resident is determined based on a comparison of values obtained by normalizing walking speeds calculated based on walking trajectory data acquired on a plurality of different days. Walking speed is calculated using the walking trajectory data of each resident selected by the user, and the normalized walking speed is calculated by multiplying by the attribute coefficient. By comparing the normalized values, the presence or absence of a change in each resident's condition and the degree of change are detected.

[Monitoring system configuration]
FIG. 1 is a diagram showing an example of the configuration of a monitoring system 100. The targets to be monitored are, for example, residents in each living room provided in the living room area 180 of the facility. In the monitoring system 100 shown in FIG. 1, living rooms 110 and 120 are provided in a living room area 180. A living room 110 is assigned to a resident 111. The living room 120 is assigned to a resident 121. In the example of FIG. 1, the number of living rooms included in the monitoring system 100 is two, but the number is not limited to this.

In the monitoring system 100, sensor boxes 119 installed in living rooms 110 and 120, a management server 200 installed in a management center 130, and an access point 140 are connected via a network 190. Network 190 may include both an intranet and the Internet.

In the monitoring system 100, a mobile terminal 143 carried by a caregiver 141 and a portable terminal 144 carried by a caregiver 142 can be connected to a network 190 via an access point 140. Further, sensor box 119, management server 200, and access point 140 can communicate with cloud server 150 via network 190.

Living rooms 110 and 120 each include a chest of drawers 112, a bed 113, and a toilet 114 as facilities. A door sensor 118 is installed on the door of the living room 110 to detect whether the door is opened or closed. A toilet sensor 116 is installed on the door of the toilet 114 to detect whether the toilet 114 is opened or closed. An odor sensor 117 is installed on the bed 113 to detect the odor of each resident 111, 121. Each resident 111, 121 is equipped with a vital sensor 290 that detects vital information of the resident 111, 121. The detected vital information includes the resident's body temperature, breathing, heart rate, etc. In the rooms 110 and 120, each resident 111 and 121 can operate the care call handset 115, respectively.

The sensor box 119 has built-in sensors for detecting the behavior of objects in the rooms 110 and 120. An example of a sensor is a Doppler sensor for detecting motion of an object. Another example is a camera. The sensor box 119 may include both a Doppler sensor and a camera as sensors.

Components of the monitoring system 100 will be described with reference to FIG. 2. FIG. 2 is a block diagram showing an overview of the configuration of the monitoring system 100.

[Sensor box 119]
The sensor box 119 includes a control device 101, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, a communication interface 104, a camera 105, a Doppler sensor 106, a wireless communication device 107, and a storage device. 108.

Control device 101 controls sensor box 119. Control device 101 is configured, for example, by at least one integrated circuit. The integrated circuit is, for example, at least one CPU (Central Processing Unit), MPU (Micro Processing Unit) or other processor, at least one ASIC (Application Specific Integrated Circuit), at least one FPGA (Field Programmable Gate Array), or any of these. It is composed of a combination of

An antenna (not shown) or the like is connected to the communication interface 104. The sensor box 119 exchanges data with an external communication device via the antenna. External communication devices include, for example, management server 200, mobile terminals 143, 144 and other terminals, access point 140, cloud server 150, and other communication terminals.

Camera 105, in one implementation, is a near-infrared camera. The near-infrared camera includes an IR (Infrared) projector that projects near-infrared light. By using a near-infrared camera, images representing the interiors of living rooms 110 and 120 can be captured even at night. In other implementations, camera 105 is a surveillance camera that receives only visible light. In still other implementations, the camera 105 may be a 3D sensor or a thermography camera. Sensor box 119 and camera 105 may be configured as one body or may be configured as separate bodies.

The Doppler sensor 106 is, for example, a microwave Doppler sensor, and detects the behavior (motion) of objects in the rooms 110 and 120 by emitting and receiving radio waves. Thereby, the biometric information of the residents 111 and 121 in the rooms 110 and 120 can be detected. In one example, the Doppler sensor 106 emits microwaves in the 24 GHz band toward the beds 113 of the rooms 110 and 120, and receives reflected waves reflected by the occupants 111 and 121. The reflected waves undergo a Doppler shift due to the actions of the residents 111 and 121. The Doppler sensor 106 can detect the breathing state and heart rate of the residents 111, 121 from the reflected waves.

Wireless communication device 107 receives signals from care call handset 240, door sensor 118, toilet sensor 116, odor sensor 117, and vital sensor 290, and transmits the signals to control device 101. For example, the care call handset 240 includes a care call button 241. When the button is operated, care call handset 240 transmits a signal to wireless communication device 107 indicating that the button has been operated. Door sensor 118, toilet sensor 116, odor sensor 117, and vital sensor 290 transmit their respective detection results to wireless communication device 107.

The storage device 108 is, for example, a fixed storage device such as a flash memory or a hard disk, or a recording medium such as an external storage device. The storage device 108 stores programs executed by the control device 101 and various data used to execute the programs. The various data may include behavior information of the residents 111 and 121. Details of the behavior information will be described later.

At least one of the above programs and data may be stored in a storage device other than the storage device 108, as long as the storage device is accessible by the control device 101 (for example, the storage area of the control device 101 (such as a cache memory), the ROM 102, It may be stored in the RAM 103 or external devices (for example, the management server 200, the mobile terminals 143, 144, etc.).

[Behavior information]
The above behavior information will be explained. The behavior information is, for example, information indicating that the residents 111, 121 have performed a predetermined behavior. In one example, the predetermined actions include "waking up" indicating that resident 111, 121 has woken up, "getting out of bed" indicating that resident 111, 121 has left the bedding, and "getting out of bed" indicating that resident 111, 121 has fallen off the bedding. It includes four actions: "fall", which indicates that the residents 111 and 121 have fallen, and "fall", which indicates that the residents 111 and 121 have fallen.

In one embodiment, the control device 101 generates each behavior information of the residents 111 and 121 associated with each of the rooms 110 and 120 based on images captured by the cameras 105 installed in each of the rooms 110 and 120. do. For example, the control device 101 detects the heads of the residents 111, 121 from the above-mentioned image, and based on the temporal change in the detected head size of the residents 111, 121, Detects "getting up", "getting out of bed", "falling over", and "falling down". Hereinafter, a specific example of the generation of behavior information will be described in more detail.

First, the storage device 108 stores the location area of each bed 113 in the rooms 110 and 120, a first threshold Th1, a second threshold Th2, and a third threshold Th3. The first threshold value Th1 identifies the size of the resident's head within the area where the bed 113 is located between when the resident is in the lying position and when the resident is in the sitting position. The second threshold Th2 identifies whether or not the resident is in a standing position in the living room 110, 120 excluding the area where the bed 113 is located, based on the size of the resident's head. The third threshold value Th3 identifies whether or not the resident is in the recumbent position based on the size of the resident's head in the living room 110, 120 excluding the area where the bed 113 is located.

The control device 101 extracts a moving object region as a human region of the residents 111 and 121 from the target image, for example, by a background subtraction method or a frame subtraction method. The control device 101 further performs derivation using a neural network trained for head detection from the extracted moving body region, for example, by circular or elliptical Hough transform, by pattern matching using a head model prepared in advance. The head regions of the residents 111 and 121 are extracted using the determined threshold values. The control device 101 detects "getting up," "getting out of bed," "falling over," and "falling down" from the extracted head position and size.

The control device 101 determines that the position of the head extracted as described above is within the location area of the bed 113 and that the size of the head extracted as described above is determined to be in a lying position by using the first threshold Th1. It may be determined that the action "wake up" has occurred when a change in the size of the posture from the size of the sitting posture is detected.

When the position of the head extracted as described above moves from within the region where the bed 113 is located to outside the region where the bed 113 is located, the control device 101 controls the size of the head extracted as described above. By applying the second threshold Th2, when it is detected that the head has changed from a certain size to the size of the standing posture, it may be determined that the action "leaving the bed" has occurred.

When the position of the head extracted as described above moves from within the region where the bed 113 is located to outside the region where the bed 113 is located, the control device 101 controls the size of the head extracted as described above. By applying the third threshold Th3, when it is detected that the head has changed from a certain size to the size of the lying posture, it may be determined that the action "fall" has occurred.

The control device 101 uses the third threshold Th3 when the position of the head extracted as described above is located in the living room 110, 120 excluding the area where the bed 113 is located, and the size of the extracted head is the third threshold Th3. If it is detected that the size has changed from a certain size to the size of the lying position, it may be determined that the action "fall" has occurred.

As explained above, in one specific example, the control device 101 of the sensor box 119 generates each behavioral information of the residents 111 and 121. Note that in the monitoring system 100 according to another aspect, an element other than the control device 101 (for example, the cloud server 150) generates behavior information of the residents 111 and 121 using images in the rooms 110 and 120. Good too.

[Mobile terminals 143, 144]
Mobile terminals 143 and 144 include a control device 221 , a ROM 222 , a RAM 223 , a communication interface 224 , a display 226 , a storage device 228 , and an input device 229 . In one aspect, the mobile terminals 143 and 144 are realized as, for example, a smartphone, a tablet terminal, a wristwatch type terminal, or other wearable device.

Control device 221 controls mobile terminals 143 and 144. The control device 221 is constituted by, for example, at least one integrated circuit. The integrated circuit is configured, for example, by at least one CPU, at least one ASIC, at least one FPGA, or a combination thereof.

An antenna (not shown) or the like is connected to the communication interface 224. The mobile terminals 143 and 144 exchange data with external communication devices via the antenna and access point 140. External communication devices include, for example, the sensor box 119 and the management server 200.

The display 226 is realized by, for example, a liquid crystal display, an organic EL (Electro Luminescence) display, or the like. Input device 229 is realized, for example, by a touch sensor provided on display 226. The touch sensor receives a touch operation on the mobile terminals 143, 144, and outputs a signal corresponding to the touch operation to the control device 221.

The storage device 228 is realized by, for example, a flash memory, a hard disk or other fixed storage device, or a removable data recording medium.

[Cloud server configuration]
The configuration of cloud server 150 will be described with reference to FIG. 3. FIG. 3 is a block diagram showing the hardware configuration of computer system 300 functioning as cloud server 150.

The computer system 300 includes, as main components, a CPU 1 that executes a program, a mouse 2 and a keyboard 3 that receive instructions from a user of the computer system 300, and data generated by executing a program by the CPU 1 or a mouse 2. Alternatively, a RAM 4 that volatilely stores data input via the keyboard 3, a hard disk 5 that stores data nonvolatilely, an optical disk drive 6, a communication interface (I/F) 7, and a monitor 8. include. Each component is connected to each other by a data bus. The optical disk drive device 6 is loaded with a CD-ROM 9 and other optical disks.

Processing in the computer system 300 is realized by each piece of hardware and software executed by the CPU 1. Such software may be stored in the hard disk 5 in advance. Further, the software may be stored on a CD-ROM 9 or other recording medium and distributed as a computer program. Alternatively, the software may be provided as a downloadable application program by an information provider connected to the Internet. Such software is read from the recording medium by the optical disk drive device 6 or other reading device, or is downloaded via the communication interface 7, and then temporarily stored in the hard disk 5. The software is read from the hard disk 5 by the CPU 1 and stored in the RAM 4 in the form of an executable program. CPU1 executes the program.

Each of the components constituting the computer system 300 shown in FIG. 3 is common. Therefore, it can be said that one of the essential parts of the technical idea according to the present disclosure is software stored in the RAM 4, hard disk 5, CD-ROM 9, and other recording media, or software that can be downloaded via a network. The storage medium may include a non-transitory computer readable data storage medium. Note that since the operation of each piece of hardware in the computer system 300 is well known, detailed explanation will not be repeated.

Note that the recording medium is not limited to CD-ROM, FD (Flexible Disk), hard disk, but also magnetic tape, cassette tape, optical disk (MO (Magnetic Optical Disc) / MD (Mini Disc) / DVD (Digital Versatile Disc)). , IC (Integrated Circuit) cards (including memory cards), optical cards, mask ROM, EPROM (Electronically Programmable Read-Only Memory), EEPROM (Electronically Erasable Programmable Read-Only Memory), flash ROM, and other semiconductor memories. It may also be a medium that carries a program.

The program here includes not only programs that can be directly executed by the CPU, but also programs in source program format, compressed programs, encrypted programs, and the like.

[Device configuration of monitoring system 100]
With reference to FIG. 4, monitoring using the monitoring system 100 will be described. FIG. 4 is a diagram showing an example of a schematic device configuration of the monitoring system 100 using the sensor box 119.

The monitoring system 100 is used to watch over the residents 111, 121 and other residents who are the persons to be watched (monitored persons). As shown in FIG. 4, a sensor box 119 is attached to the ceiling of the living room 110. Sensor boxes 119 are similarly installed in other living rooms.

Range 410 represents the detection range by sensor box 119. If the sensor box 119 has a Doppler sensor as described above, the Doppler sensor detects human behavior occurring within the range 410. If the sensor box 119 has a camera as a sensor, the camera takes an image within the range 410.

The sensor box 119 is installed, for example, in a nursing care facility, a medical facility, a home, or the like. In the example of FIG. 4, the sensor box 119 is attached to the ceiling and photographs the resident 111 and the bed 113 from the ceiling. The mounting location of the sensor box 119 is not limited to the ceiling, but may be mounted on the side wall of the living room 110.

The monitoring system 100 detects danger to the resident 111 based on a series of images (ie, video) obtained from the camera 105. As an example, the detectable danger includes a fall of the resident 111, a state where the resident 111 is in a dangerous place (eg, a bed rail, etc.), and the like.

When the monitoring system 100 detects that the resident 111 is in danger, it notifies the caregivers 141, 143, etc. of this fact. As an example of a notification method, the monitoring system 100 notifies the mobile terminals 143, 144 of the caregivers 141, 142 of the danger of the resident 111. When receiving the notification, the mobile terminals 143 and 144 notify the caregivers 141 and 142 of the danger of the resident 111 by means of a message, voice, vibration, etc. Thereby, the caregivers 141 and 142 can immediately understand that the resident 111 is in danger, and can quickly rush to the resident 111.

Although FIG. 4 shows an example in which the monitoring system 100 includes one sensor box 119, the monitoring system 100 may include a plurality of sensor boxes 119. Further, although FIG. 4 shows an example in which the monitoring system 100 includes a plurality of mobile terminals 143 and 144, the monitoring system 100 can also be realized by a single mobile terminal.

[data structure]
The data structure of cloud server 150 will be described with reference to FIG. 5. FIG. 5 is a diagram showing one aspect of data storage in the hard disk 5 included in the cloud server 150.

The hard disk 5 holds a table 60. The table 60 sequentially stores data transmitted from each sensor provided in each room as walking trajectory data. More specifically, the table 60 includes a room ID 61, a date and time 62, an X coordinate value 63, and a Y coordinate value 64. The room ID 61 identifies the resident's room. The date and time 62 identifies the date and time when the signal sent from the sensor was acquired. The X coordinate value 63 represents the point detected at the date and time, that is, the X coordinate value of the resident's position. The Y coordinate value 64 represents the point detected at the date and time, that is, the Y coordinate value of the resident's position. In one aspect, the coordinate axes on which the X-coordinate value and the Y-coordinate value are based are defined, for example, with reference to an end point of the living room (for example, one corner of the room). In another aspect, the coordinate axis may be defined with reference to a certain point in a facility where each room is provided.

Furthermore, the hard disk 5 holds image data sent from the sensor box 119. Image data is acquired at predetermined time intervals. The CPU 1 can identify the resident's condition by performing image analysis using each image data. For example, the CPU 1 can extract the head from each image data, and extract the time the resident is lying down, the time the resident is sitting on the bed 113, and the time the resident is walking.

It should be noted that the people walking in the room may be caregivers or family members in addition to the resident. Therefore, the CPU 1 can exclude walking trajectories of people other than residents. For example, the CPU 1 may calculate the walking speed for each walking trajectory, and may exclude from the image analysis the walking trajectory for which a speed equal to or higher than a certain speed estimated to be the walking speed of a healthy person other than the resident is calculated. In this case, as the walking speed of a healthy person, a walking speed measured in advance or a walking speed measured during image analysis can be used. In addition, in order to calculate the walking speed from the walking trajectory, for example, the CPU 1 calculates the x-coordinate value and y-coordinate value for each point detected at a plurality of times, and the calculation from one point to the next point among each point. It can be calculated based on the time taken to reach .

[Overview of CPU1 operation]
(1) In a certain situation, the CPU 1 reads out from the hard disk 5 a plurality of walking trajectory data representing the resident's walking trajectory and acquired on different days. The CPU 1 calculates the walking speed of the resident based on the plurality of walking trajectory data. For example, the CPU 1 calculates the difference between the x and y coordinate values of two observed points for each walking trajectory, and divides the time required to move between the two points by the difference. Calculate walking speed. The walking trajectory data to be calculated is not particularly limited. For example, walking trajectory data acquired on the day before the calculation is instructed, walking trajectory data acquired three months before the previous day, and walking trajectory data acquired six months before the previous day are used. may be done. The period for looking at changes in walking speed is not limited to three months ago and six months ago. Walking trajectory data from one month ago and two months ago may be used. Alternatively, walking trajectory data from one year ago may be used. The CPU 1 normalizes the walking speed by multiplying the walking speed by a predetermined coefficient based on the resident's attributes.

When normalizing walking speed, for example, the following formula may be used.
Value after normalization = Calculated walking speed × Coefficient according to the first attribute × ... × Coefficient according to the nth attribute (2) In a certain situation, the resident's attributes are age, gender, height, or weight. In this case, the normalized walking speed is calculated as follows, for example.

Walking speed after normalization = Measured walking speed × Age coefficient × Gender coefficient × Height coefficient × Weight coefficient (3) In a certain situation, the CPU 1 calculates walking trajectory data acquired on multiple different days. Based on a comparison of the normalized values of each walking speed, the degree of nursing care required for the resident is determined. The CPU 1 calculates the walking speed using the walking trajectory data of each resident selected by the user, and calculates the normalized walking speed by multiplying the walking speed by a coefficient according to the attribute. By comparing the normalized values, the CPU 1 can detect the presence or absence of a change in each resident's condition and the degree of change.

[Change in walking trajectory]
With reference to FIG. 6, changes in the resident's walking trajectory during the night will be described. FIG. 6 is a diagram showing changes in the resident's walking trajectory in a certain situation.

(Case A) In a certain situation, two walking trajectories 610 and 611 are observed as nighttime walking trajectories based on walking trajectory data that is data transmitted from the sensor box 119. Each walking trajectory 610, 611 indicates a round trip trajectory from the bed 113 to the toilet 114. Therefore, it is presumed that the resident went straight from the bed 113 to the toilet 114. For example, if the resident does not have difficulty walking or has not developed dementia, the walking trajectory 610 is set such that the path from the bed 113 to the toilet 114 takes the shortest course even at night. , 611.

(Case B) After that, if the resident develops dementia and has difficulty walking normally, the resident may be unable to walk even just from the bed 113 to the toilet 114. It is possible that the patient may have problems and be unable to walk straight. In this case, for example, walking trajectories 620 and 621 may be observed.

Alternatively, the resident may walk aimlessly (wandering). In this case, walking trajectories 622 and 623 that are not directed toward a specific destination (eg, washstand 51, chest of drawers 112, desk 52, etc.) may be observed. Therefore, by looking at changes in each resident's walking trajectory, it is possible to objectively understand changes in the resident's condition. For example, if it is detected that the walking trajectory, which was previously heading almost straight from the bed 113 to the destination, is wavering or that the resident is walking around the same location, it is assumed that the resident's condition has changed. .

[data structure]
The data structure of the system 100 in this embodiment will be further explained with reference to FIG. FIG. 7 is a diagram showing one aspect of data storage in the hard disk 5. As shown in FIG. The data structure shown in FIG. 7 is generated on the hard disk 5 of the cloud server 150 or the management server 200. Hard disk 5 has tables 710, 720, and 730.

Table 710 includes age 711 and age coefficient 712. Age 711 is divided into several years. The range of age 711 is not limited to that illustrated in FIG. 7 . For example, it may be in units of 2 or 3 years, or in units of 10 years.

Table 720 includes gender 721 and gender coefficient 722. Gender 721 is applied to the resident's gender. The gender coefficient 722 is applied depending on the resident's gender.

Table 730 includes height 731 and height coefficient 732. Height 731 represents the resident's height. The height width is not limited to the example shown in FIG. 7 (10 cm), and may be other widths, such as 2 cm, 5 cm, etc. Height coefficient 732 represents a coefficient applied to each height.

FIG. 8 is a diagram showing the correspondence between walking speed and the level of nursing care or support required according to the walking speed.

Hard disk 5 includes a table 800. The table 800 includes a normalized walking speed 810 and a nursing care/support requirement level 820. The normalized walking speed 810 is a walking speed calculated by multiplying by a weighting coefficient according to each attribute shown in FIG. The nursing care/support requirement degree 820 represents the degree of nursing care or support required according to the walking speed.

[Control structure]
The control structure of cloud server 150 will be described with reference to FIG. 9. FIG. 9 is a flowchart showing part of the processing executed by the CPU 1 of the cloud server 150.

In step S910, the CPU 1 acquires a plurality of walking trajectory data from the hard disk 5 based on the data processing command given to the cloud server 150. For example, when a user (administrator, care staff, etc.) of the management server 200 accessing the cloud server 150 instructs data processing, the cloud server 150 processes each stored walking trajectory data (see FIG. 5). Read data from the hard disk 5 to the RAM 4.

In step S920, the CPU 1 calculates the resident's walking speed based on the plurality of walking trajectory data. For example, the CPU 1 uses walking trajectory data observed at specified times in the morning, afternoon, before going to bed, etc., and uses the time required to move between two points and the coordinate values of the two points to Calculate the walking speed of the person. The data serving as the basis for calculating the walking speed may be either image data acquired by the camera 105 or data output from the Doppler sensor 106.

In step S930, the CPU 1 reads predetermined coefficients (FIG. 7) based on the resident's attributes. In another aspect, each coefficient may be input by the user each time during the normalization process.

In step S940, the CPU 1 normalizes the walking speed by multiplying the walking speed by the coefficient. For example, the CPU 1 normalizes the walking speed using the formula "calculated walking speed x age coefficient x gender coefficient x height coefficient." Note that it is not necessary to use all the coefficients shown in FIG. 7 as the coefficients used for normalization, and coefficients other than those shown in FIG. 7 may be used.

In step S950, the CPU 1 stores the normalized value in the hard disk 5.
In step S960, the CPU 1 reads the normalized value calculated based on the walking trajectory data acquired on a plurality of different days. At this time, the CPU 1 may read the values of each of the plurality of residents selected by the user.

In step S970, CPU 1 compares each value. For example, the CPU 1 compares the normalized values of one resident from the previous day, three months ago, and six months ago. If a plurality of residents are selected in another situation, for each resident, the normalized values of the previous day, three months, and six months prior to the resident are similarly compared. Furthermore, the CPU 1 can display each value as a graph on the monitor 8.

In step S980, the CPU 1 determines the resident's condition based on the comparison result. For example, when the CPU 1 detects a resident whose walking speed has significantly decreased, the CPU 1 displays a determination result such as "care level check" or "detailed examination required" for that resident on the monitor 8. Alternatively, the diagnosis result may be output on a form.

[Screen display]
With reference to FIGS. 10 and 11, output of processing results according to an embodiment will be described. FIG. 10 is a diagram showing an example of a display on the monitor 8. As shown in FIG.

As shown in state (A), the monitor 8 is displaying Mr. A's graph 1010 and Mr. B's graph 1020 based on the data before normalization.

As shown in state (B), the monitor 8 is displaying Mr. A's graph 1011 and Mr. B's graph 1021 based on the normalized data. According to graphs 1011 and 1021 after normalization, Mr. B's walking speed exceeds that of Mr. A, and the degree of decline in Mr. A's walking ability is greater than the degree of decline in Mr. B's walking ability. This is understood.

FIG. 11 is a diagram showing an example of a walking speed diagnostic report. When a user (administrator, care staff, etc.) of the management server 200 instructs the management server 200 to display a report, the monitor 8 displays a diagnostic report based on FIG. 8. For example, when the user specifies the residents (e.g., Mr. A, Mr. B, and Mr. C) whose diagnosis results are desired and selects the output icon, the monitor 8 displays the attributes and diagnosis results of each of the selected residents. Display. Furthermore, the monitor 8 can display notifications such as "care level check" and "detailed examination required" for residents who require special attention. This allows the user to take necessary measures depending on the degree of change in the resident.

[Summary of embodiments]
As described above, according to the present embodiment, by multiplying the measured value (for example, walking speed) by a coefficient specified according to the resident's attribute, a state in which the influence caused by the difference in attributes is excluded. The condition and abilities (eg, walking ability) of each resident can be displayed numerically. This makes it possible to objectively grasp the condition of each resident and to compare the condition of each resident without being influenced by attributes, making it possible to more accurately grasp the condition of each resident.

The embodiments disclosed this time should be considered to be illustrative in all respects and not restrictive. The scope of the present invention is indicated by the claims rather than the above description, and it is intended that equivalent meanings and all changes within the scope of the claims are included.

This technology is applicable to information processing using data obtained at hospitals, nursing homes, nursing homes, and other facilities.

51 wash basin, 52 desk, 60 table, 100 system, 101,221 control device, 105 camera, 106 Doppler sensor, 107 wireless communication device, 108,228 storage device, 110,120 living room, 111,121 resident, 112 chest of drawers , 113 bed, 114 toilet, 115 care call handset, 116 toilet sensor, 117 sensor, 118 door sensor, 119 sensor box, 130 management center, 140 access point, 141,142 caregiver, 143,144 mobile terminal, 150 cloud server , 180 living area, 190 network, 200 management server.

Claims (9)

A program executed on a computer to determine the level of care, the program causing the computer to:
calculating information on the position coordinates of the resident in the living room based on image data captured at predetermined time intervals by a camera installed in the living room;
acquiring walking trajectory data representing a walking trajectory of the resident based on information on the resident's position coordinates in the living room;
calculating the walking speed of the resident based on the plurality of walking trajectory data;
A program for normalizing the walking speed based on a predetermined coefficient based on attributes of the resident. The program according to claim 1, wherein the resident's attributes include age, gender, height, or weight. The program causes the computer to determine the degree of care required of the resident based on a comparison of values obtained by normalizing each walking speed calculated based on walking trajectory data acquired on a plurality of different days. The program according to claim 1, further causing the program to execute. A camera installed in the living room,
a memory that stores information on the resident's position coordinates in the living room calculated based on image data captured by the camera at predetermined time intervals;
a processor coupled to the memory;
The processor includes:
obtaining walking trajectory data representing a walking trajectory of the resident based on information on the resident's position coordinates in the living room ;
Calculate the resident's walking speed based on multiple walking trajectory data,
An information processing system configured to normalize the walking speed based on a predetermined coefficient based on attributes of the resident. The information processing system according to claim 4, wherein the resident's attributes include age, gender, height, or weight. The processor is further configured to determine the degree of care required of the resident based on a comparison of values obtained by normalizing each walking speed calculated based on walking trajectory data acquired on a plurality of different days. The information processing system according to claim 4, wherein the information processing system is A computer-implemented method, the method comprising:
calculating information on the position coordinates of the resident in the living room based on image data captured at predetermined time intervals by a camera installed in the living room;
acquiring walking trajectory data representing a walking trajectory of the resident based on information on the resident's position coordinates in the living room ;
calculating the walking speed of the resident based on the plurality of walking trajectory data;
normalizing the walking speed based on a predetermined coefficient based on attributes of the resident. 8. The method according to claim 7, wherein the resident's attributes include age, gender, height, or weight. Claim 7, further comprising the step of determining the level of care required of the resident based on a comparison of values obtained by normalizing each walking speed calculated based on walking trajectory data acquired on a plurality of different days. The method described in.

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