JPWO2020003954A1 - Programs that run on your computer, information processing equipment, and how they run on your computer - Google Patents

Abstract

The processing executed by the CPU of the computer system includes a step of acquiring walking locus data from the hard disk (S910), a step of calculating the walking speed of the resident based on a plurality of walking locus data (S920), and a resident. A step of reading a predetermined coefficient based on the attribute of (S930), a step of multiplying the walking speed by the coefficient to normalize the walking speed (S940), and a step of saving the normalized value. (S950), a step of reading out the normalized value calculated based on the walking locus data acquired on a plurality of different days (S960), a step of comparing each value (S970), and the comparison result. It includes a step (S980) of determining the condition of a person skilled in the art based on the above.

Description

The present disclosure relates to data processing, and more specifically to data processing based on a walking locus.

The technology to watch over the residents is known. For example, Japanese Patent Application Laid-Open No. 2015-215711 (Patent Document 1) states, "In order to monitor the relative changes in the behavior and ecology of the resident in a manner that is close to the life of the resident, the progress of" aging "in which individual differences are remarkable is observed. , A monitoring system that can relatively evaluate according to the resident's ability and environment and present the specifications of the monitoring that matches the evaluation ”(see [Issue] in [Summary]).

Japanese Unexamined Patent Publication No. 2017-117423 (Patent Document 2) discloses "a monitoring system capable of grasping daily activities including the walking speed of a person to be monitored". The monitoring system includes "a plurality of slave units 2 (2A to 2G) installed in the residence H and a master unit 3". When each slave unit 2 detects the target person, each slave unit 2 transmits a notification signal to the master unit 3. The master unit 3 calculates the travel time from the bedroom PD to the toilet PD of the person to be watched based on the time data of the notification signal from the slave unit 2D and the time data of the notification signal from the slave unit 2E, and the movement is concerned. Calculate the walking speed of the person being watched over based on the time. ] (See [Summary]).

Japanese Unexamined Patent Publication No. 2015-215711 JP-A-2017-117423

For those who need watching over or long-term care, the degree of long-term care is certified based on, for example, interviews with the target person and the long-term caregiver and the opinions of the attending physician. May take some time. In addition, the measured value may change depending on the period of stay. The degree of change in the measured value (for example, walking speed, etc.) differs depending on the physique, age, gender, etc. of the resident. Therefore, it may not be possible to properly grasp the change in the resident's condition by simply comparing the measured values. Therefore, there is a need for technology that can properly grasp changes in the resident's condition.

The present disclosure has been made in view of the above background, and an object in a certain aspect is to provide a technique capable of appropriately grasping a change in the state of a resident.

According to certain embodiments, a program that runs on a computer is provided. The program has a step of acquiring walking trajectory data representing the walking trajectory of the resident on a computer, a step of calculating the walking speed of the resident based on a plurality of walking trajectory data, and a step of calculating the walking speed of the resident in advance based on the attributes of the resident. The step of normalizing the walking speed and the step of normalizing the walking speed are executed based on the determined coefficient.

According to certain embodiments, resident attributes include either age, gender, height, or weight.

According to one embodiment, the program requires the resident to take care of the resident based on a comparison of each value normalized to each walking speed calculated based on walking trajectory data acquired on a plurality of different days. Further execution of the step of determining the degree.

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 walking trajectory of the resident, calculates the walking speed of the resident based on the plurality of walking trajectory data, and determines the walking speed in advance based on the attributes of the resident. It is configured to normalize the walking speed based on the coefficient.

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

According to another embodiment, the processor determines the degree of care required of the resident based on the comparison of each value obtained by normalizing each walking speed calculated based on the walking locus data acquired on a plurality of different days. Is further configured to determine.

According to still other embodiments, a method performed on a computer is provided. This method is predetermined based on a step of acquiring walking locus data representing the walking locus of the resident, a step of calculating the walking speed of the resident based on a plurality of walking locus data, and an attribute of the resident. It includes a step of normalizing the walking speed based on the obtained coefficient.

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

According to still another embodiment, the method requires care for the resident based on a comparison of each value normalized to each walking speed calculated based on walking trajectory data acquired on a plurality of different days. It further includes a step of determining the degree.

The program executed by the computer, the information processing device, and the method executed by the computer of the present disclosure can appropriately grasp the change in the state of the resident.

The above and other objectives, features, aspects and advantages of the invention will become apparent from the following detailed description of the invention as understood in connection with the accompanying drawings.

It is a figure which shows an example of the structure of the monitoring system 100. It is a block diagram which shows the outline of the structure of the monitoring system 100. It is a block diagram which shows the hardware composition of the computer system 300 which functions as a cloud server 150. It is a figure which shows an outline example of the apparatus structure of the monitoring system 100 using a sensor box 119. It is a figure which shows one aspect of storing data in the hard disk 5 included in the cloud server 150. It is a figure which shows the change of the walking locus of a resident in a certain aspect. It is a figure which shows one aspect of storing data in a hard disk 5. It is a figure which shows the correspondence between the walking speed, and the level of care-requiring or support-requiring proposed according to the walking speed. It is a flowchart which shows a part of the processing executed by the CPU 1 of the cloud server 150. It is a figure which shows an example of the display of a monitor 8. It is a figure which shows an example of the diagnosis report of walking speed.

Hereinafter, embodiments of the technical concept according to the present disclosure will be described with reference to the drawings. In the following description, the same parts are designated by the same reference numerals. Their names and functions are the same. Therefore, the detailed description of them will not be repeated.

[Technical Thought]
The outline of the technical idea disclosed in this specification will be described.

In a certain aspect, the observed values (walking speed, etc.) of the target person such as the resident of the facility are normalized. Using normalized observations, it is possible to evaluate all subjects on the same basis.

For example, there are a person with a walking speed of 70 cm / s (Mr. A) and a person with a walking speed of 30 cm / s (Mr. B). If it goes down, the way of thinking is different. Use offsets to normalize the difference in descent. This makes it easier to determine the degree of care required of the target person such as a resident.

In another aspect, prepare a table that determines the normalization coefficient by age, gender, and physique (height or weight). By using the coefficients obtained from each, we try to obtain normalized values that are not related to age, gender, height or weight.

In yet another aspect, the degree of care required of the resident is determined based on the comparison of each value obtained by normalizing each walking speed calculated based on the walking locus data acquired on a plurality of different days. The walking speed is calculated using the walking locus data of each resident selected by the user, and the walking speed after normalization is calculated by multiplying by the attribute coefficient. By comparing the normalized values, the presence or absence of change in the state of each resident and the degree of change are detected.

[Monitoring system configuration]
FIG. 1 is a diagram showing an example of the configuration of the watching system 100. The object to be watched over is, for example, a resident in each living room provided in the living room area 180 of the facility. In the monitoring system 100 of FIG. 1, living rooms 110 and 120 are provided in the living room area 180. The living room 110 is assigned to the resident 111. The living room 120 is assigned to the resident 121. In the example of FIG. 1, the number of living rooms included in the watching system 100 is 2, but the number is not limited to this.

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

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

The living rooms 110 and 120 include a chest of drawers 112, a bed 113, and a toilet 114, respectively, as equipment. A door sensor 118 that detects the opening and closing of the door is installed on the door of the living room 110. A toilet sensor 116 that detects the opening and closing of the toilet 114 is installed on the door of the toilet 114. The bed 113 is provided with an odor sensor 117 that detects 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, respiration, heart rate, and the like. In the living rooms 110 and 120, each resident 111 and 121 can operate the care call slave unit 115, respectively.

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

The components of the monitoring system 100 will be described with reference to FIG. FIG. 2 is a block diagram showing an outline of the configuration of the watching 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. It includes 108.

The control device 101 controls the sensor box 119. The control device 101 is composed of, for example, 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 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, a management server 200, mobile terminals 143, 144 and other terminals, an access point 140, a cloud server 150, and other communication terminals.

The camera 105 is a near-infrared camera in one implementation example. Near-infrared cameras include IR (Infrared) floodlights that project near-infrared light. By using a near-infrared camera, images showing the inside of the living rooms 110 and 120 can be taken even at night. In another embodiment, the camera 105 is a surveillance camera that receives only visible light. In still another embodiment, a 3D sensor or a thermography camera may be used as the camera 105. The sensor box 119 and the camera 105 may be configured as one or a separate body.

The Doppler sensor 106 is, for example, a microwave Doppler sensor that radiates and receives radio waves to detect the behavior (movement) of objects in the living rooms 110 and 120. As a result, the biometric information of the residents 111 and 121 of the living rooms 110 and 120 can be detected. In one example, the Doppler sensor 106 radiates microwaves in the 24 GHz band toward the beds 113 of the rooms 110 and 120, and receives the reflected waves reflected by the residents 111 and 121. The reflected wave is Doppler-shifted by the movements of the residents 111 and 121. The Doppler sensor 106 can detect the respiratory state and heart rate of the residents 111 and 121 from the reflected wave.

The wireless communication device 107 receives signals from the care call slave unit 240, the door sensor 118, the toilet sensor 116, the odor sensor 117, and the vital sensor 290, and transmits the signals to the control device 101. For example, the care call slave unit 240 includes a care call button 241. When the button is operated, the care call slave unit 240 transmits a signal indicating that the operation has been performed to the wireless communication device 107. The door sensor 118, the toilet sensor 116, the odor sensor 117, and the vital sensor 290 transmit their respective detection results to the 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 a program executed by the control device 101 and various data used for executing the program. Various data may include behavioral information of residents 111 and 121. Details of the behavior information will be described later.

If at least one of the above programs and data is a storage device accessible to the control device 101, a storage device other than the storage device 108 (for example, a storage area of the control device 101 (for example, a cache memory), ROM 102, etc.). It may be stored in the RAM 103, an external device (for example, a management server 200, a mobile terminal 143, 144, etc.).

[Behavior information]
The above behavioral information will be described. The action information is, for example, information indicating that the residents 111 and 121 have executed a predetermined action. In one example, the predetermined actions are "getting up" indicating that the occupants 111 and 121 have awakened, "getting out" indicating that the resident 111 and 121 have left the bedding, and the resident 111 and 121 have fallen from the bedding. It includes four actions, "falling" to indicate that, and "falling" to indicate that the residents 111 and 121 have fallen.

In one embodiment, the control device 101 generates behavioral information of the occupants 111, 121 associated with the rooms 110, 120 based on the images captured by the cameras 105 installed in the rooms 110, 120. To do. For example, the control device 101 detects the heads of the occupants 111, 121 from the above image, and based on the time change of the size of the detected heads of the occupants 111, 121, the resident 111, 121 " Detects "wake up", "get out of bed", "fall" and "fall". Hereinafter, a specific example of generating behavioral information will be described in more detail.

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

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

In the control device 101, the position of the head extracted as described above is within the location region of the bed 113, and the size of the head extracted as described above is laid down by using the first threshold Th1. When it is detected that the size of the posture has changed to the size of the sitting posture, it may be determined that the action "wake up" has occurred.

When the position of the head extracted as described above moves from the inside of the location area of the bed 113 to the outside of the location area of the bed 113, the control device 101 refers to the size of the head extracted as described above. By applying the second threshold value Th2, when it is detected that the head has changed from a certain size to a standing posture size, it may be determined that the action “getting out of bed” has occurred.

When the position of the head extracted as described above moves from the inside of the location area of the bed 113 to the outside of the location area of the bed 113, the control device 101 refers to the size of the head extracted as described above. By applying the third threshold value 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 behavior "fall" has occurred.

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

As described above, in one specific example, the control device 101 of the sensor box 119 generates the action information of the residents 111 and 121. In the monitoring system 100 that follows other aspects, elements other than the control device 101 (for example, the cloud server 150) generate behavior information of the residents 111 and 121 using the images in the living rooms 110 and 120. May be good.

[Mobile terminals 143, 144]
The 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 a certain aspect, the mobile terminals 143 and 144 are realized as, for example, smartphones, tablet terminals, wristwatch-type terminals and other wearable devices.

The control device 221 controls the mobile terminals 143 and 144. The control device 221 is composed of, for example, at least one integrated circuit. An integrated circuit is composed of, for example, 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 an external communication device via the antenna and the access point 140. External communication devices include, for example, a sensor box 119, a management server 200, and the like.

The display 226 is realized by, for example, a liquid crystal display, an organic EL (Electro Luminescence) display, or the like. The input device 229 is realized by, for example, a touch sensor provided on the display 226. The touch sensor receives a touch operation on the mobile terminals 143 and 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, a detachable data recording medium, or the like.

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

The computer system 300 has, as main components, a CPU 1 that executes a program, a mouse 2 and a keyboard 3 that receive input of instructions from a user of the computer system 300, data generated by executing the program by the CPU 1, or a mouse 2. Alternatively, the RAM 4 that volatilely stores the data input via the keyboard 3, the hard disk 5 that stores the data non-volatilely, the optical disk drive device 6, the communication interface (I / F) 7, and the monitor 8 are used. Including. The components are connected to each other by a data bus. A CD-ROM9 or other optical disc is mounted on the optical disc drive device 6.

The processing in the computer system 300 is realized by each 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 in a CD-ROM9 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 so-called Internet. Such software is temporarily stored in the hard disk 5 after being read from the recording medium by the optical disk driving device 6 or other reading device or downloaded via the communication interface 7. 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. The CPU 1 executes the program.

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

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

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

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

The watching system 100 is used to watch over the residents 111, 121 and other residents who are the monitoring target persons (monitoring target 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 attached to other living rooms.

The range 410 represents the detection range by the sensor box 119. When the sensor box 119 has the above-mentioned Doppler sensor, the Doppler sensor detects the behavior of a person occurring within the range 410. When the sensor box 119 has a camera as a sensor, the camera captures an image within range 410.

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

The watching system 100 detects the danger occurring in the resident 111 based on a series of images (that is, images) 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 present at a dangerous place (for example, a bed fence), and the like.

When the monitoring system 100 detects that the resident 111 is in danger, the monitoring system 100 notifies the caregivers 141, 143 and the like of the danger. As an example of the notification method, the monitoring system 100 notifies the mobile terminals 143 and 144 of the caregivers 141 and 142 of the danger of the resident 111. Upon receiving the notification, the mobile terminals 143 and 144 notify the caregivers 141 and 142 of the danger of the resident 111 by a message, voice, vibration or the like. As a result, the caregivers 141 and 142 can immediately grasp that the resident 111 is in danger and can quickly rush to the resident 111.

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

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

The hard disk 5 holds the table 60. The table 60 sequentially stores the data transmitted from each sensor provided in each living room as walking locus 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 position of the resident. The Y coordinate value 64 represents the point detected at the date and time, that is, the Y coordinate value of the position of the resident. In a certain aspect, the coordinate axes that are the basis of the X coordinate value and the Y coordinate value are defined with reference to, for example, the end point of the living room (for example, one corner of the room). In another aspect, the coordinate axes may be defined relative to a point in the facility where each room is provided.

Further, the hard disk 5 holds the image data sent from the sensor box 119. Image data is acquired at predetermined time intervals. The CPU 1 can identify the state of the resident 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 when the resident is lying down, the time when the resident is sitting on the bed 113, and the time when the resident is walking.

The person walking in the living room may be a caregiver or a family member in addition to the resident. Therefore, the CPU 1 can exclude the walking locus of a person other than the resident. For example, the CPU 1 can calculate the walking speed for each walking locus, and exclude the walking locus for which the walking speed of a healthy person other than the resident is calculated to be a speed equal to or higher than a certain speed, which is estimated, from the target of image analysis. In this case, as the walking speed of a healthy person, a walking speed measured in advance or a walking speed measured at the time of image analysis can be used. Further, in order to calculate the walking speed from the walking locus, for example, the CPU 1 has the x-coordinate value and the y-coordinate value for each point detected at a plurality of times, and the next point from a certain point among the points. It can be calculated based on the time to reach.

[Outline of operation of CPU1]
(1) In a certain aspect, the CPU 1 represents the walking locus of the resident and reads out a plurality of walking locus data acquired on different days from the hard disk 5. The CPU 1 calculates the walking speed of the resident based on the plurality of walking locus data. For example, the CPU 1 calculates the difference between the x-coordinate value and the y-coordinate value of the two observed points for each walking locus, and divides the time required for movement between the two points by the difference. Calculate the walking speed. The walking locus data to be calculated is not particularly limited. For example, the walking locus data acquired on the day before the day in which the calculation is instructed, the walking locus data acquired 3 months before the previous day, and the walking locus data acquired 6 months before the previous day are used. May be done. The period for observing changes in walking speed is not limited to 3 months and 6 months ago. Gait locus data from 1 month and 2 months ago may be used. Alternatively, the walking locus data 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 attributes of the resident.

When normalizing the walking speed, for example, the following formula can be used.
Normalized value = Calculated walking speed x Coefficient according to the first attribute x ... x Coefficient according to the nth attribute (2) In a certain situation, the attributes of the resident are age, gender, height, etc. Or include either weight. In this case, the normalized walking speed is calculated as follows as an example.

Normalized walking speed = measured walking speed x age coefficient x gender coefficient x height coefficient x weight coefficient (3) In a certain situation, CPU1 is calculated based on walking trajectory data acquired on a plurality of different days. Based on the comparison of each value obtained by normalizing each walking speed, the degree of care required of the resident is determined. The CPU 1 calculates the walking speed using the walking locus data of each resident selected by the user, and calculates the walking speed after normalization by multiplying 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 the state of each resident and the degree of the change.

[Changes in walking trajectory]
With reference to FIG. 6, changes in the walking locus of the resident at night will be described. FIG. 6 is a diagram showing a change in the walking locus of a resident in a certain aspect.

(Case A) In a certain aspect, two walking loci 610 and 611 are observed as walking loci at night based on the walking locus data which is the data transmitted from the sensor box 119. Each of the walking loci 610 and 611 shows a round-trip locus from the bed 113 to the toilet 114. Therefore, it is presumed that the resident headed straight from the bed 113 to the toilet 114. For example, if the resident is not inconvenienced in walking or has not developed dementia or the like, the walking locus 610 is such that the route from the bed 113 to the toilet 114 takes the shortest course even at night. , 611.

(Case B) After that, if the resident becomes obstructed to normal walking, such as when the resident develops dementia, the resident can walk by simply going from the bed 113 to the toilet 114. It may be a hindrance and you may not be able to walk straight. In this case, for example, walking loci 620, 621 can be observed.

Alternatively, the resident may walk for no purpose (wandering). In this case, a walking locus 622,623 that does not head toward a specific destination (for example, washbasin 51, chest of drawers 112, desk 52, etc.) can be observed. Therefore, by observing the change in the walking trajectory of each resident, it is possible to objectively grasp the change in the state of the resident. For example, it is presumed that the resident's condition has changed when it is detected that the walking locus, which has been almost straight from the bed 113 to the destination, is swaying or is going around the same place. ..

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

Table 710 includes an age 711 and an age factor 712. Age 711 is categorized every few years. The range of age 711 is not limited to that illustrated in FIG. 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 coefficients 722. Gender 721 applies to the gender of the resident. The gender coefficient 722 is applied according to the gender of the resident.

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

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

The hard disk 5 includes a table 800. The table 800 includes a walking speed of 810 after normalization and a degree of care / support required 820. The walking speed 810 after normalization is a walking speed calculated by multiplying by a weighting coefficient corresponding to each attribute shown in FIG. 7. The degree of need for long-term care / support required 820 represents the degree of need for long-term care or support required according to the walking speed.

[Control structure]
The control structure of the cloud server 150 will be described with reference to FIG. FIG. 9 is a flowchart showing a 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 locus data from the hard disk 5 based on the data processing instruction 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 collects each walking locus data (see FIG. 5). Read from hard disk 5 to RAM 4.

In step S920, the CPU 1 calculates the walking speed of the resident based on the plurality of walking locus data. For example, the CPU 1 uses the walking locus data observed at a specified time such as morning, afternoon, and before going to bed, and uses the time required to move the two points and the coordinate values of the two points to move in. Calculate the walking speed of the person. The data on which the walking speed is calculated may be either the image data acquired by the camera 105 or the data output from the Doppler sensor 106.

In step S930, the CPU 1 reads out a predetermined coefficient (FIG. 7) based on the attributes of the resident. In another aspect, each coefficient may be entered 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 by using the formula “calculated walking speed × age coefficient × gender coefficient × height coefficient”. The coefficients used for normalization may not be all the coefficients shown in FIG. 7, or may be other than the coefficients shown in FIG. 7.

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

In step S970, the CPU 1 compares each value. For example, CPU1 compares the values after each normalization of one resident the day before, 3 months ago, and 6 months ago. If more than one resident is selected in another aspect, the values after each normalization of the day before, 3 months and 6 months before the resident are similarly compared for each resident. Further, the CPU 1 can display each value as a graph on the monitor 8.

In step S980, the CPU 1 determines the state of the resident based on the result of the comparison. For example, when the CPU 1 detects a resident whose walking speed is significantly reduced, the CPU 1 displays a determination result such as "check for degree of care required" or "detailed inspection" for the resident on the monitor 8. Alternatively, it may be output to a form as a diagnosis result.

[Display screen]
The output of the result of the process according to a certain embodiment will be described with reference to FIGS. 10 and 11. FIG. 10 is a diagram showing an example of the display of the monitor 8.

As shown in the state (A), the monitor 8 displays the graph 1010 of Mr. A and the graph 1020 of Mr. B based on the data before normalization.

As shown in the state (B), the monitor 8 displays the graph 1011 of Mr. A and the graph 1021 of Mr. B based on the normalized data. According to the normalized graphs 1011, 1021, the walking speed of Mr. B is higher than the walking speed of Mr. A, and the degree of decrease in the walking ability of Mr. A is larger than the degree of walking ability of Mr. B. Is grasped.

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 the report, the monitor 8 displays the diagnostic report based on FIG. For example, when the user specifies a resident (eg, Mr. A, Mr. B, Mr. C) who wants to know the diagnosis result and selects the output icon, the monitor 8 displays each attribute and the diagnosis result of the selected resident. Is displayed. Further, the monitor 8 can display notifications such as "check for the degree of long-term care" and "detailed inspection" for the resident who needs special attention. As a result, the user can take necessary measures according to the degree of change in the resident.

[Summary of Embodiment]
As described above, according to the present embodiment, by multiplying the measured value (for example, walking speed) by a coefficient defined according to the attribute of the resident, the influence caused by the difference in the attribute is excluded. Then, the state and ability of each resident (eg, walking ability) can be displayed numerically. As a result, the situation of each resident can be objectively grasped, and the state of each resident can be compared without being affected by the attribute, so that the state of each resident can be grasped more accurately.

It should be considered that the embodiments disclosed this time are exemplary in all respects and not restrictive. The scope of the present invention is shown by the claims rather than the above description, and it is intended to include all modifications within the meaning and scope equivalent to the claims.

This technology can be applied to information processing using data obtained in hospitals, nursing homes, nursing homes and other facilities.

51 washbasin, 52 desks, 60 tables, 100 systems, 101,221 controls, 105 cameras, 106 Doppler sensors, 107 wireless communication devices, 108,228 storage devices, 110,120 rooms, 111,121 residents, 112 chests of drawers. , 113 beds, 114 toilets, 115 care call handsets, 116 toilet sensors, 117 sensors, 118 door sensors, 119 sensor boxes, 130 management centers, 140 access points, 141,142 caregivers, 143, 144 mobile terminals, 150 cloud servers , 180 room area, 190 networks, 200 management servers.

Claims (9)

A program that runs on a computer, said program to the computer.
Steps to acquire walking trajectory data representing the walking trajectory of the resident,
A step of calculating the walking speed of the resident based on a plurality of walking locus data, and
A program that executes a step of normalizing the walking speed based on a predetermined coefficient based on the attributes of the resident. The program of claim 1, wherein the resident's attributes include any of age, gender, height, or weight. The program is a step of determining the degree of care required of the resident based on the comparison of each value obtained by normalizing each walking speed calculated based on the walking locus data acquired on a plurality of different days on the computer. The program according to claim 1, further executing the above. Memory and
With a processor coupled to the memory
The processor
Acquire walking locus data representing the walking locus of the resident,
Based on a plurality of walking locus data, the walking speed of the resident is calculated.
An information processing device configured to normalize the walking speed based on a predetermined coefficient based on the attributes of the resident. The information processing device according to claim 4, wherein the resident attribute includes any of age, gender, height, and weight. The processor is further configured to determine the degree of care required of the resident based on the comparison of each value obtained by normalizing each walking speed calculated based on the walking locus data acquired on a plurality of different days. The information processing device according to claim 4. It ’s a method that runs on a computer.
Steps to acquire walking trajectory data representing the walking trajectory of the resident,
A step of calculating the walking speed of the resident based on a plurality of walking locus data, and
A method comprising a step of normalizing the walking speed based on a predetermined coefficient based on the attributes of the resident. The method of claim 7, wherein the resident's attributes include any of age, gender, height, or weight. 7. Claim 7 further includes a step of determining the degree of care required of the resident based on a comparison of each value obtained by normalizing each walking speed calculated based on walking locus data acquired on a plurality of different days. The method described in.

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