JP7215481B2 - Computer Executed Programs, Information Processing Devices, and Computer Executed Methods - Google Patents

Description

The present disclosure relates to data processing, and more particularly to data processing based on walking trajectories.

Techniques for watching over residents are known. For example, Japanese Unexamined Patent Application Publication No. 2015-215711 (Patent Document 1) discloses that “In order to monitor the relative changes in the behavior and ecology of residents in a way that is close to the lives of residents, the progress of “aging”, which has significant individual differences, can be monitored. , a watching system that can relatively evaluate according to the ability and environment of the resident and present specifications of watching that match the evaluation” (see [Problem] in [Summary]).

Japanese Patent Laying-Open No. 2017-117423 (Patent Document 2) discloses a “watching system capable of grasping daily behavior including walking speed of a person being watched over”. The monitoring system "includes a plurality of slave units 2 (2A to 2G) and a master unit 3 installed in a residence H. Each slave device 2 transmits a notification signal to the master device 3 when it senses the target person. Based on the time data of the notification signal from child device 2D and the time data of the notification signal from child device 2E, master device 3 calculates the movement time of the watching target from bedroom PD to toilet PD, The walking speed of the person being watched over is calculated based on the time. ] (See [Abstract]).

Japanese Patent Laying-Open No. 2017-151676 (Patent Document 3) discloses “monitored person monitoring device, method and system capable of further improving work efficiency”. This monitored person monitoring apparatus is communicably connected to an event notification communication signal containing event information representing the content of a predetermined event related to a monitored person to be monitored and for notifying the event. The management server device SV as an example thereof includes an independence determination processing unit 223 that determines whether or not the monitored person Ob can become independent based on the image acquired from the sensor device via the network. and a warning notification processing unit 224 that transmits a warning notification communication signal containing warning information indicating a warning to a predetermined terminal device when the independence determination processing unit 223 determines that the monitored person Ob cannot stand on its own. It has a configuration of "Prepared." (See [Abstract]).

JP 2015-215711 A JP 2017-117423 A JP 2017-151676 A

For those who need watching over or nursing care, certification of long-term care is based on, for example, interviews with the subject and caregivers, opinions of the attending physician, etc., and quantitative measurement is not possible. It may take Therefore, there is a need for a technique that enables quantitative measurement and does not take much time for determination.

The present disclosure has been made in view of the background as described above, and an object in one aspect is to provide a technique for deriving quantitative information that can be used for certifying the level of care.

According to one embodiment, a computer-executable program is provided. The program instructs the computer to represent the walking trajectory of a person in the living room, obtain a plurality of trajectory data obtained on different days, and based on the walking characteristics of the resident in the living room or the walking characteristics of non-residents, the acquired a step of discriminating trajectory data of a resident other than a resident in a living room from the trajectory data of a resident from a plurality of trajectory data obtained; and the step of calculating .

According to an embodiment, the discriminating step discriminates, from among the plurality of trajectory data, trajectory data whose starting point and end point of the trajectory are near the bed arranged in the living room as the trajectory data of the resident. Including.

According to an embodiment, the step of determining excludes, from among the plurality of acquired trajectory data, the trajectory data corresponding to the walking speed defined as the walking speed of those other than the resident from the plurality of trajectory data. include.

According to an embodiment, in the determining step, among the plurality of pieces of acquired trajectory data, trajectory data for a predetermined time as a time period in which only the resident is in the living room is discriminated as trajectory data for the resident. including doing

According to one embodiment, the program further causes the computer to output a recommendation to review the resident's level of care based on the results of the walking speed calculation.

According to another embodiment, an information processing apparatus is provided that includes a memory and a processor coupled to the memory. The processor represents the walking trajectory of a person in the living room, acquires a plurality of trajectory data acquired on different days, and calculates the plurality of trajectories acquired based on the gait characteristics of the occupants of the living room or the gait characteristics of non-residents. The trajectory data of non-residents in the living room and the trajectory data of the resident are discriminated from the data, and the walking speed of the resident in the living room is calculated using the discriminated trajectory data of the resident. ing.

According to one embodiment, the processor determines, among the plurality of trajectory data, trajectory data whose starting and ending points of the trajectory are near the bed placed in the living room as trajectory data of the resident.

According to one embodiment, the processor excludes the trajectory data corresponding to the previously measured walking speed as the walking speed of a person other than the resident from the plurality of acquired trajectory data.

According to one embodiment, the processor determines the trajectory data for a predetermined time period during which only the resident is in the living room among the acquired trajectory data as the trajectory data for the resident.

According to an embodiment, the processor is further configured to output a recommendation to review the resident's level of care based on the results of the walking speed calculation.

According to yet another embodiment, a computer-implemented method is provided. This method represents the walking trajectory of a person in a living room, acquiring a plurality of trajectory data acquired on different days; The walking speed of the resident in the living room is calculated using the step of discriminating the trajectory data of the resident other than the resident in the room from among the plurality of trajectory data and the trajectory data of the resident after the discrimination. and the step of

According to an embodiment, the discriminating step discriminates, from among the plurality of trajectory data, trajectory data whose starting point and end point of the trajectory are near the bed arranged in the living room as the trajectory data of the resident. Including.

According to an embodiment, the determining step excludes, from among the plurality of acquired trajectory data, the trajectory data corresponding to the walking speed defined as the walking speed of those other than the resident from the plurality of trajectory data. including.

According to an embodiment, in the determining step, among the plurality of pieces of acquired trajectory data, trajectory data for a predetermined time as a time period in which only the resident is in the living room is discriminated as trajectory data for the resident. including doing

According to an embodiment, the method further includes outputting a suggestion to review the resident's level of care based on the results of the walking speed calculation.

In one aspect, quantitative information can be derived that can be used for certification of care level.
The above 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.

It is a figure which shows an example of a structure of the watching system 100. FIG. It is a block diagram which shows the outline of a structure of the watching system 100. FIG. 3 is a block diagram showing the hardware configuration of computer system 300 functioning as cloud server 150. FIG. It is a figure which shows an example of an outline of an apparatus structure of the watching system 100 using the sensor box 119. FIG. FIG. 10 is a diagram representing the difference between a resident's walking trajectory and a resident's walking trajectory in living room 110 according to an aspect; 4 is a diagram showing one mode of data storage in hard disk 5 provided in cloud server 150. FIG. FIG. 10 is a diagram showing a distribution of a person's moving speed calculated for each trajectory; 4 is a flowchart showing part of the processing executed by CPU 1 of cloud server 150. FIG. It is a figure showing the transition of the walking speed of 5 residents in a certain nursing facility for 6 months. It is a figure showing the relationship between a resident|tenant's average moving speed and care level.

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 given the same reference numerals. Their names and functions are also the same. Therefore, detailed description thereof will not be repeated.

[Technical concept]
First, an outline of the technical idea disclosed in this specification will be described. In a certain aspect, the movement speed of a subject such as a resident of a facility is measured, and the daily state of the subject can be accurately grasped. Measurements are taken without the subject being aware that they are measuring. For example, the subject may stop or slow down while moving. Therefore, of the trajectory information (for example, walking trajectory) obtained by the movement of the subject, only trajectories having a distance equal to or greater than a certain value can be observed. In one aspect, continuous trajectories are observed, and trajectories whose starting and ending points are on or near the bed are taken as observation targets.

In another aspect, people other than residents who may enter each room of the facility in order to exclude trajectory information of people other than residents (for example, caregivers, nurses and other staff, family members, etc.) The walking speed is measured in advance, and trajectory information in which walking at a certain speed or more is observed can be excluded from observation targets.

In yet another aspect, when a person other than the resident (eg, staff, family member, etc.) enters the room, the person may wear the signal transmitter. The signal emitter may emit a predefined identification number. The signal can be related to the person's walking trajectory at that time. In this way, the walking trajectory to which the identification number is attached can be excluded from the resident's walking trajectory, so the resident's walking trajectory can be extracted. Information associated with the walking trajectory is not limited to the identification number, and may be predetermined data as a flag indicating entry into the room.

[Configuration of monitoring system]
FIG. 1 is a diagram showing an example of the configuration of the watching system 100. As shown in FIG. The watching target 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 a living room area 180 . A living room 110 is assigned to a resident 111 . A living room 120 is assigned to a resident 121 . In the example of FIG. 1, the number of living rooms included in the watching system 100 is two, but the number is not limited to this.

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

In the watching system 100 , a mobile terminal 143 carried by a caregiver 141 and a mobile terminal 144 carried by a caregiver 142 can be connected to a network 190 via an access point 140 . Furthermore, 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 wardrobe 112, a bed 113, and a toilet 114 as equipment. A door sensor 118 is installed on the door of the living room 110 to detect opening and closing of the door. A toilet sensor 116 for detecting opening and closing of the toilet 114 is installed on the door of the toilet 114 . A bed 113 is provided with an odor sensor 117 that detects the odor of each of the residents 111 and 121 . Each resident 111,121 is equipped with a vital sensor 290 for detecting vital information of the resident 111,121. The detected vital information includes the resident's body temperature, respiration, heart rate, and the like. In living rooms 110 and 120, each resident 111 and 121 can operate a care call slave device 115, respectively.

The sensor box 119 incorporates sensors for detecting behavior of objects in the living rooms 110 and 120 . One example of a sensor is a Doppler sensor for detecting motion of an object. Another example is a camera. Sensor box 119 may include both a Doppler sensor and a camera as sensors.

The constituent elements of the watching system 100 will be described with reference to FIG. 2 . FIG. 2 is a block diagram showing an overview of the configuration of the watching system 100. As shown in FIG.

[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.

The control device 101 controls the sensor box 119 . The control device 101 is composed of, for example, at least one integrated circuit. Integrated circuits include, for example, at least one CPU (Central Processing Unit), MPU (Micro Processing Unit) and other processors, 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) and the like are 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, the management server 200, the mobile terminals 143, 144 and other terminals, the access point 140, the cloud server 150 and other communication terminals.

Camera 105, in one implementation, is a near-infrared camera. A near-infrared camera includes an IR (Infrared) projector that projects near-infrared light. By using a near-infrared camera, an image representing the inside of living room 110, 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, camera 105 may be a 3D sensor or a thermographic camera. The sensor box 119 and the camera 105 may be configured integrally or separately.

The Doppler sensor 106 is, for example, a microwave Doppler sensor that emits and receives radio waves to detect the behavior (movement) of objects in the living rooms 110 and 120 . Thereby, the biological information of the residents 111 and 121 in the living 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 living rooms 110 and 120 and receives reflected waves reflected by the residents 111 and 121 . The reflected waves are Doppler-shifted by the motions 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 waves.

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

The storage device 108 is, for example, a fixed storage device such as a flash memory or 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 for executing the programs. Various data may include the behavior information of residents 111 and 121 . Details of the action information will be described later.

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

[Action information]
The above action information will be explained. Action information is, for example, information indicating that residents 111 and 121 have performed a predetermined action. In one example, the predetermined actions are "get up" indicating that the residents 111 and 121 have woken up, "leave the bed" indicating that the residents 111 and 121 have left the bed (bedding) 113, and the residents 111 and 121 It includes four actions of "falling" indicating falling from the bed (bedding) 113 and "falling" indicating that the residents 111 and 121 have fallen.

In one embodiment, the control device 101 generates each action information of the residents 111 and 121 associated with each living room 110 and 120 based on the images captured by the cameras 105 installed in each living room 110 and 120. do. For example, the control device 101 detects the heads of the residents 111 and 121 from the above images, and determines the " Detects "Getting up", "Getting out of bed", "Falling" and "Falling". A specific example of generating behavior information will be described in more detail below.

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

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

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 the size of the head extracted as described above is determined by using the first threshold value Th1. It may be determined that the action "wake up" has occurred when it detects a change from posture magnitude to sitting posture magnitude.

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

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

The control device 101 determines that the position of the head extracted as described above is located in the living room 110 or 120 excluding the location area of the bed 113, and the size of the extracted head uses the third threshold value Th3. It may be determined that the action "fall" has occurred if it detects a change from a size to a lying size by .

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

[Portable terminals 143, 144]
The mobile terminals 143 , 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 implemented as, for example, smart phones, 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) and the like are connected to the communication interface 224 . Mobile terminals 143 and 144 exchange data with external communication devices via the antenna and access point 140 . External communication equipment includes, for example, the sensor box 119, management server 200, and the like.

The display 226 is implemented by, for example, a liquid crystal display, an organic EL (Electro Luminescence) display, or the like. The input device 229 is implemented by a touch sensor provided on the display 226, for example. 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 implemented 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 the cloud server 150 will be described with reference to FIG. FIG. 3 is a block diagram showing the hardware configuration of computer system 300 that functions as cloud server 150. As shown in FIG.

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

Processing in the computer system 300 is implemented by each piece of hardware and software executed by the CPU 1 . Such software may be stored in the hard disk 5 in advance. Also, the software may be stored in 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 a so-called information provider connected to the Internet. Such software is temporarily stored in the hard disk 5 after being read from the recording medium by the optical disk drive 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. CPU1 executes the program.

Each component that makes up the computer system 300 shown in FIG. 3 is conventional. 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 media, or software that can be downloaded via a network. Recording media may include non-transitory computer-readable data recording media. Since the operation of each piece of hardware in computer system 300 is well known, detailed description will not be repeated.

The recording medium is not limited to CD-ROM, FD (Flexible Disk), hard disk, magnetic tape, cassette tape, optical disk (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), flash ROM and other semiconductor memories It may be a medium that literally carries the program.

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

[Device configuration of watching system 100]
Watching using the watching system 100 will be described with reference to FIG. FIG. 4 is a diagram showing an example of a schematic device configuration of the monitoring system 100 using the sensor box 119. As shown in FIG.

The watching system 100 is used to watch over residents 111 and 121 who are watching targets (monitoring targets) and other residents. As shown in FIG. 4, a sensor box 119 is attached to the ceiling of 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 sensor box 119 has a camera as a sensor, the camera takes an image within range 410 .

Sensor box 119 is installed, for example, in nursing homes, medical facilities, homes, and 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, and may be mounted on the side wall of the living room 110 .

The watching system 100 detects a danger to the resident 111 based on a series of images (that is, video) obtained from the camera 105 . By way of example, detectable hazards include resident 111 falling, resident 111 being in a hazardous area (eg, bed rails, etc.), and the like.

When the watching system 100 detects that the resident 111 is in danger, the monitoring system 100 notifies the caregivers 141, 143, etc. of the fact. As an example of the notification method, the watching 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 portable terminals 143 and 144 notify the caregivers 141 and 142 of the danger of the resident 111 by 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 .

Note that FIG. 4 shows an example in which the monitoring system 100 includes one sensor box 119 , but the monitoring system 100 may include a plurality of sensor boxes 119 . Moreover, 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.

[Trajectory classification]
The types of trajectories in the living room will be described with reference to FIG. FIG. 5 is a diagram showing the difference between the walking trajectory of a resident in living room 110 and the walking trajectory of a non-resident according to a certain aspect. A closet 112 , a bed 113 , a toilet 114 , a washbasin 51 and a desk 52 are arranged in the room 110 .

As shown in scene (A) of FIG. 5, two walking trajectories 510 and 520 are detected from the results of image processing observed during a certain period of time on a certain day. Among them, the walking locus 510 starts from the bed 113, reaches the toilet 114, and returns to the bed 113 again. The walking trajectory 520 starts from the door 109, goes to the bed 113, and returns to the door 109 again.

Therefore, in a certain aspect, it is possible to classify a person who has walked along a walking trajectory according to the starting point, destination (or turning point), or end point of the trajectory. For example, as shown in scene (B), a person who starts walking from bed 113, reaches toilet 114, and walks back to bed 113 is presumed to be a resident. As shown in scene (C), a person who started from door 109, reached bed 113, and walked back to door 109, is presumed to be someone other than the resident, such as a caregiver. be.

[data structure]
The data structure of the cloud server 150 will be described with reference to FIG. FIG. 6 is a diagram showing one mode of data storage in hard disk 5 included in cloud server 150. As shown in FIG.

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

For example, the XY coordinate values acquired based on the output from the Doppler sensor 106 or the camera 105 in the sensor box 119 and the acquisition date and time are periodically transmitted to the cloud server 150 . In another aspect, data transmitted from sensor box 119 may also be stored on management server 200 .

[Overview of operation of CPU 1]
In one aspect, the CPU 1 acquires a plurality of trajectory data representing a person's walking trajectory in a living room and acquired on different days. Based on the walking characteristics of the residents of the living room or the walking characteristics of the staff and other persons other than the residents, the CPU 1 selects the trajectory data of the residents other than the residents in the living room and the trajectory data of the residents from among the plurality of acquired trajectory data. determine. The CPU 1 calculates the walking speed of the resident in the living room using the trajectory data of the resident after determination.

In a certain aspect, the CPU 1 can determine the trajectory to be the resident's trajectory when the starting and ending points of the trajectory are in the vicinity of the bed. More specifically, the CPU 1 determines the trajectory data whose starting point and end point of the trajectory are near the bed 113 arranged in the room as the trajectory data of the resident.

In one aspect, the CPU 1 excludes, from among the plurality of acquired locus data, the locus data corresponding to the walking speed that is measured in advance as the walking speed of a person other than the resident from the plurality of locus data. Thereby, CU1 can extract the resident's walking locus.

In a certain aspect, the CPU 1 determines the trajectory data of a predetermined time period during which only the resident is in the room among the acquired trajectory data as the trajectory data of the resident.

In one aspect, the CPU 1 outputs a proposal prompting the resident to review the level of nursing care based on the result of the walking speed calculation. For example, the CPU 1 can present on the monitor 8 numerical values or graphs showing changes in walking trajectories for each resident, or output them to a printer (not shown). As a result, caregivers, care managers and other administrators can, for example, review the nursing care judgment for each resident based on objective data, ensuring fairness and convincingness in the judgment content. obtain.

[Movement speed of residents and staff]
The difference in moving speed between residents and staff will be described with reference to FIG. FIG. 7 is a diagram showing the distribution of the moving speed of a person calculated for each trajectory. In FIG. 7, the walking trajectories of 20 people are specified, and the average moving speed is calculated from the data of each walking trajectory. Of these, the moving speeds specified by trajectory numbers 5, 7, 11, 14, 16 and 19 are faster than 800 mm/sec, respectively, and the other trajectory numbers 1-4, 6, 8-10, 12, Travel speeds identified at 13, 15, 17, 18 and 20 are each less than 600 mm/sec. Therefore, it became clear that there is a difference between the distribution of movement speeds of people other than residents such as care staff and the distribution of movement speeds of residents. obtain. For example, in the example of FIG. 7, 700 mm/sec may be a candidate threshold.

In one aspect, management server-200 can display a distribution chart as shown in FIG. 7 based on the average walking speed data calculated by cloud server 150. FIG. A user of the management server 200 (for example, an administrator of a nursing care facility, a care manager, etc.) can objectively confirm changes in the walking ability of each resident while viewing the distribution map.

[Data processing]
Data processing in the cloud server 150 will be described with reference to FIG. FIG. 8 is a flow chart showing part of the processing executed by CPU 1 of cloud server 150 .

At step S810, CPU 1 detects the input of the data processing mode. For example, when the staff operates the management server-200 to select the data processing mode, the selection result is sent to the cloud server 150, and the cloud server 150 switches the operation mode to the data processing mode.

In step S820, CPU 1 reads out a plurality of trajectory data representing the walking trajectory of a person in the living room and obtained on different days. For example, when the staff designates a date and time and instructs extraction of a walking locus, the CPU 1 extracts data of a walking locus detected on the designated date and time from the table 60 of the hard disk 5 .

In step S830, the CPU 1 selects the trajectory data of the resident other than the resident in the living room and the trajectory of the resident from among the plurality of acquired trajectory data based on the gait characteristics of the resident in the living room or the gait characteristics of the resident other than the resident. data and discriminate. More specifically, the CPU 1 distinguishes between the resident's walking trajectory and the staff's walking trajectory from the walking trajectories of each resident and each staff member based on the starting point of the walking trajectory (see FIG. 5, for example).

At step S840, the CPU 1 calculates the walking speed of the resident in the living room using the trajectory data of the resident after determination. For example, the CPU 1 calculates the average walking speed based on the coordinate values of the points forming the walking trajectory and the dates and times when the points were extracted.

At step S850, the CPU 1 outputs identification information and walking speed of the resident. For example, after calculating each walking speed, the CPU 1 transmits the resident's identification information and the calculation result to the management server-200. When the management server-200 receives the identification information and the calculation result, it displays these data on the monitor 8 (eg, FIG. 7). Alternatively, the management server-200 can output a form containing these data to a printer or a file. Staff and other caregivers can view the output data and objectively judge the transition of each resident's walking ability.

[Decrease in walking speed]
With reference to FIG. 9, transition of walking speed according to a certain situation will be described. FIG. 9 is a diagram showing six-month changes in walking speed of five residents in a nursing care facility. In one aspect, the monitor 8 of the management server 200 or the display 226 of the mobile terminals 143, 144 can display the graph shown in FIG.

For example, for the resident 910, it can be seen that the walking speed suddenly decreased from June 2017 to August 2017, and then the degree of decrease slowed down. As for the residents 920, 930 and 940, it can be seen that the walking speed decreases at a constant rate, while the rate of decrease differs depending on the residents. Regarding the resident 950, the walking speed once increased from June to July 2017, then decreased until October 2017, and then increased again. Therefore, for example, it is estimated that the decrease in walking speed is suppressed by walking training or the like. Therefore, the staff may use a case like the resident 950 to recommend rehabilitation for other residents, for example.

[Walking speed and level of care]
The relationship between the average moving speed and the level of care will be described with reference to FIG. 10 . FIG. 10 is a diagram showing the relationship between a resident's average moving speed and the level of care. In a certain aspect, it is estimated that there is some correlation between the degree of care and the average moving speed. For example, if the degree of nursing care increases, walking tends to become more difficult, and the average moving speed is considered to decrease. Therefore, for each resident, by plotting the average moving speed of the resident according to the level of care that has already been certified, caregivers can objectively determine whether the current level of care is appropriate. can be judged. For example, the average speed of residents included in group 1010 is below threshold 1020, and it is estimated that their walking speed is decreasing. Therefore, the management server-200 can display on the monitor 8 as a proposal to change the certification of the level of care of the residents included in the group 1010 .

As described above, according to the present embodiment, the walking trajectory observed in the room of the facility is divided into the walking trajectory of the resident and the walking trajectory of the person other than the resident. By using the resident's walking trajectory, it is possible to objectively grasp changes in walking speed. Since the information required for nursing care is provided objectively, it is possible to make fair and highly convincing judgments. In addition, since the walking trajectory is derived using data collected daily, rapid data processing is possible, and information necessary for determination can be provided in a short period of time.

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

The technology is applicable to information obtained in hospitals, nursing homes, nursing homes and other facilities.

100 system, 101,221 control device, 106 Doppler sensor, 107 wireless communication device, 108 storage device, 109 door, 110,120 living room, 111,121,910,920,930,950 resident, 112 chest, 113 bed, 114 toilet, 115 care call child device, 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, 190 network, 200 management server, 226 display, 229 input device, 241 care call button, 290 vital sensor, 300 computer system, 410 range, 51 washstand, 52 desk, 60 table, 510,520 walking trajectory.

Claims (15)

A program running on a computer, said program causing said computer to:
obtaining a plurality of trajectory data representing a person's walking trajectory in a living room and obtained on different days;
Based on the walking characteristics of the resident in the living room or the walking characteristics of the non-resident, the trajectory data of the resident other than the resident in the living room and the trajectory data of the resident are discriminated from the plurality of acquired trajectory data. and
and calculating the walking speed of the resident in the living room using the trajectory data of the resident after the determination. In the determining step, among the plurality of trajectory data, trajectory data whose starting point and end point of the trajectory are in the vicinity of the bed arranged in the living room are discriminated as trajectory data of the resident, A program according to claim 1. The determining step includes excluding, from the plurality of acquired trajectory data, trajectory data corresponding to a walking speed defined as a walking speed of a person other than the resident from the plurality of trajectory data. Item 1. The program according to item 1. In the step of determining, among the plurality of acquired trajectory data, trajectory data for a predetermined time period during which only the resident is in the living room is discriminated as trajectory data for the resident. 2. The program of claim 1, comprising: 2. The program according to claim 1, wherein said program further causes said computer to output a proposal prompting a resident's level of care to be reviewed based on the result of said walking speed calculation. a memory;
a processor coupled to the memory;
The processor
Representing the walking trajectory of a person in a living room, acquiring multiple trajectory data acquired on different days,
Based on the walking characteristics of the resident in the living room or the walking characteristics of the non-resident, the trajectory data of the resident other than the resident in the living room and the trajectory data of the resident are discriminated from the plurality of acquired trajectory data. death,
An information processing apparatus configured to calculate the walking speed of the resident in the living room using the trajectory data of the resident after the determination. 7. The processor according to claim 6, wherein among the plurality of trajectory data, the trajectory data whose starting point and end point of the trajectory are near the bed arranged in the living room are identified as the trajectory data of the resident. information processing equipment. 7. The processor according to claim 6, wherein from among the plurality of acquired trajectory data, trajectory data corresponding to a walking speed previously measured as a walking speed of a person other than the resident is excluded from the plurality of trajectory data. Information processing equipment. 7. The processor determines, from among the plurality of acquired trajectory data, trajectory data for a predetermined time period during which only the resident is in the living room, as trajectory data for the resident. The information processing device according to . 7. The information processing apparatus according to claim 6, wherein the processor is further configured to output a proposal prompting a resident to review the care level based on the result of the walking speed calculation. A computer implemented method comprising:
obtaining a plurality of trajectory data representing a person's walking trajectory in a living room and obtained on different days;
Based on the walking characteristics of the resident in the living room or the walking characteristics of the non-resident, the trajectory data of the resident other than the resident in the living room and the trajectory data of the resident are discriminated from the plurality of acquired trajectory data. and
and calculating the walking speed of the resident in the living room using the trajectory data of the resident after the determination. In the determining step, among the plurality of trajectory data, trajectory data whose starting point and end point of the trajectory are in the vicinity of the bed arranged in the living room are discriminated as trajectory data of the resident, 12. The method of claim 11. The determining step includes excluding, from the plurality of acquired trajectory data, trajectory data corresponding to a walking speed defined as a walking speed of a person other than the resident from the plurality of trajectory data. Item 12. The method according to Item 11. In the step of determining, among the plurality of acquired trajectory data, trajectory data for a predetermined time period during which only the resident is in the living room is discriminated as trajectory data for the resident. 12. The method of claim 11, comprising: 12. The method according to claim 11, further comprising outputting a suggestion prompting a review of the resident's level of care based on the result of the walking speed calculation.

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