JP7721977B2 - Video processing device, video processing system, video processing method, and video processing program - Google Patents

Description

This disclosure relates to technology that supports medical and nursing care administration.

In an aging society like Japan, the number of people requiring nursing care or care due to illness, injury, or old age is expected to increase further in the future. These people are admitted to facilities such as hospitals and elderly care facilities, where they receive nursing care or care. In such facilities, nurses and caregivers make regular rounds to check on the safety and well-being of the residents.

In this regard, in facilities and other settings, the activities of daily living (ADL) of rehabilitation recipients, elderly people, and others are evaluated. Such evaluations are often carried out, for example, by directly visually observing the ADL of the person being evaluated or by reviewing video recordings of the ADL.

However, at facilities such as day care centers, for example, there may not be staff (such as physical therapists) on-site who have the knowledge to evaluate activities of daily living, making it difficult to visually evaluate the activities of daily living of those being evaluated.

In response to this, the subject's activities of daily living can be recorded, and the video data created from the recording can be provided to a physical therapist or other professional to evaluate the subject's activities of daily living. However, in this case, it would be desirable to have a system that can easily provide information such as the type of activity, when it was performed, and who performed it, along with the video data, and various proposals have been made (Patent Documents 1-3).

Japanese Patent Application Laid-Open No. 2018-185729 Japanese Patent Application Laid-Open No. 2019-160228 Japanese Patent Application Laid-Open No. 2020-190871

In this regard, it is desirable to efficiently support the evaluation of a predetermined action of an evaluation subject.
The present disclosure is intended to solve the above-mentioned problems, and to provide a video processing device, a video processing system, a video processing method, and a video processing program that can efficiently support the evaluation of specified actions of a person being evaluated.

A video processing device according to one aspect of the present disclosure includes an acquisition unit that acquires video data of a subject in a room; an analysis unit that analyzes the video data acquired by the acquisition unit and extracts first information about people, including the subject, and second information about objects in the room; and a tagging processing unit that associates and assigns multiple tags related to the first information and second information extracted by the analysis unit to the video data in order to assist in the evaluation of a predetermined action performed by the subject.

Preferably, the first information includes information regarding the number of people and their actions.
Preferably, the information relating to behavior includes information relating to the subject's behavior and the caregiver's behavior.

Preferably, the second information includes information relating to care products.
Preferably, the tagging processing unit assigns a tag based on a combination of tags related to the first information and/or the second information to the video data in association with the video data.

Preferably, the system further comprises a video storage unit that stores multiple video data items tagged by the tagging processing unit, a tag name input receiving unit that receives tag names input by the user, and a display control unit that displays on the display unit a list of video data items tagged with the names input in the tag input receiving unit from the multiple video data items stored in the video storage unit.

Preferably, the system further includes a statistical processing unit that performs statistical processing based on at least one of multiple tags assigned to video data over a predetermined period and the subject's behavioral trajectory obtained by analyzing the video data, and calculates predetermined statistical values.

Preferably, the predetermined statistical values include values relating to assistance, walking, and transfer.
Preferably, the device further includes an index calculation unit that calculates an index related to ADL evaluation or care level determination based on at least one of the first information, the second information, and the statistical value.

A video processing system according to one aspect of the present disclosure includes an acquisition unit that acquires video data of a subject in a room; an analysis unit that analyzes the video data acquired by the acquisition unit and extracts first information about people, including the subject, and second information about objects in the room; and a tagging processing unit that associates and assigns multiple tags related to the first information and second information extracted by the analysis unit to the video data in order to assist in the evaluation of a predetermined action performed by the subject.

A video processing method according to one aspect of the present disclosure includes the steps of acquiring video data of a subject in a room, analyzing the acquired video data by the acquisition unit to extract first information about people, including the subject, and second information about objects in the room, and associating and assigning multiple tags related to the first information and second information extracted by the analysis unit to the video data to assist in the evaluation of a predetermined action performed by the subject.

A video processing program according to one aspect of the present disclosure includes the steps of acquiring video data of a subject in a room, analyzing the video data acquired by the acquisition unit to extract first information about people, including the subject, and second information about objects in the room, and associating and assigning multiple tags related to the first information and second information extracted by the analysis unit to the video data to assist in the evaluation of a predetermined action performed by the subject.

A video processing device according to another aspect of the present disclosure includes an acquisition unit that acquires video data captured of a subject in a room, an analysis unit that analyzes the video data acquired by the acquisition unit, and a tagging processing unit that associates and assigns multiple tags to the video data based on the analysis results of the analysis unit to assist in the evaluation of a predetermined action performed by the subject. Based on the analysis results of the analysis unit, the tagging processing unit assigns to the video data a first classification tag related to the subject, a second classification tag related to an object in the room, and a third classification tag based on a combination of the first classification tag and/or the second classification tag.

A video processing system according to another aspect of the present disclosure includes an acquisition unit that acquires video data of a subject in a room, an analysis unit that analyzes the video data acquired by the acquisition unit, and a tagging processing unit that associates and assigns multiple tags to the video data based on the analysis results of the analysis unit to assist in the evaluation of a predetermined action performed by the subject. Based on the analysis results of the analysis unit, the tagging processing unit assigns to the video data a first classification tag related to the subject, a second classification tag related to an object in the room, and a third classification tag based on a combination of the first classification tag and/or the second classification tag.

A video processing method according to another aspect of the present disclosure includes the steps of acquiring video data of a subject in a room, analyzing the video data acquired by the acquisition unit, and associating and assigning multiple tags to the video data based on the analysis results to assist in the evaluation of a predetermined action performed by the subject. The assigning step includes the step of assigning to the video data, based on the analysis results, a first classification tag related to the subject, a second classification tag related to an object in the room, and a third classification tag based on a combination of the first classification tag and/or the second classification tag.

A video processing program according to another aspect of the present disclosure includes the steps of acquiring video data of a subject in a room, analyzing the video data acquired by the acquisition unit, and associating and assigning multiple tags to the video data based on the analysis results to assist in the evaluation of a predetermined action performed by the subject. The assigning step executes processing including the step of assigning, to the video data, a first classification tag related to the subject, a second classification tag related to an object in the room, and a third classification tag based on a combination of the first classification tag and/or the second classification tag, based on the analysis results.

The video processing device, video processing method, and video processing program can efficiently support the evaluation of specific actions of a subject.

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.

1 is a diagram illustrating an example of a configuration of a monitoring system 100 according to an embodiment. 1 is a block diagram showing an outline of the configuration of a monitoring system 100 according to an embodiment. FIG. 2 is a block diagram showing the functional configuration of a management terminal 200 according to the embodiment. 10A and 10B are diagrams illustrating analysis of moving image data by an analysis unit 12 according to an embodiment. 10A and 10B are diagrams illustrating generation of another tag by tagging processing unit 14 according to the embodiment. FIG. 10 is a diagram illustrating a flow of tagging processing by the management terminal 200 according to the embodiment. FIG. 10 is a diagram illustrating an example of a management screen displayed on a display 226 of a management terminal 200 according to an embodiment. FIG. 10 is a diagram illustrating an example of a processing flow for index calculation by an index calculation unit 20 according to the embodiment. FIG. 10 is a diagram illustrating another example of the management screen displayed on the display 226 of the management terminal 200 according to the embodiment.

Hereinafter, embodiments of the technical concepts of the present disclosure will be described with reference to the drawings. In the following description, identical components are designated by the same reference numerals. Their names and functions are also the same. Therefore, detailed descriptions thereof will not be repeated.

[Overall system that serves as the technical concept]
First, an overview of the technical concept disclosed in this specification will be described. In a certain aspect, a camera is used to capture images of a room of a subject, such as a resident of a facility. By analyzing the captured images, the subject's daily condition can be accurately understood. The images can be captured for a predetermined period of time (for example, 24 hours), or can be captured in response to a specific event.

[Configuration of monitoring system]
Fig. 1 is a diagram showing an example of the configuration of a monitoring system 100 according to an embodiment. Referring to Fig. 1, the monitoring targets are, for example, residents in each room provided in a room area 180 of a facility. In the monitoring system 100 of Fig. 1, a room 110 is provided in the room area 180. The room 110 is assigned to a resident 111. In the example of Fig. 1, the number of rooms included in the monitoring system 100 is one, but the number is not limited to this.

In the monitoring system 100, the sensor box 119 installed in the room 110 and the management terminal 200 are connected via a network 190. The network 190 may include both an intranet and the Internet.

In the monitoring system 100, the sensor box 119 and management terminal 200 are configured to be able to communicate with each other via a network 190. It may also be possible for them to communicate with a cloud server (not shown).

The living room 110 includes a chest of drawers 112 and a bed 113 as its furnishings. However, the furnishings are not limited to these and may also include, for example, a television, a toilet, or other items.

The sensor box 119 has a built-in sensor for detecting the behavior of objects in the living room 110. One example of a sensor is a camera. The sensor box 119 may also include other sensors in addition to a camera.

Figure 2 is a block diagram showing an overview of the configuration of the monitoring system 100 according to an embodiment. The components of the monitoring system 100 will be described with reference to Figure 2.

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, 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. The integrated circuit is composed of, 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 a combination of these.

An antenna (not shown) and other devices are connected to the communication interface 104. The sensor box 119 exchanges data with external communication devices via the antenna. External communication devices include, for example, management terminals.

In one implementation, camera 105 is a near-infrared camera. The near-infrared camera includes an IR (Infrared) projector that projects near-infrared light. By using a near-infrared camera, images showing the interior of room 110 can be captured even at night. In another implementation, camera 105 is a surveillance camera that receives only visible light. In yet another implementation, camera 105 may be a 3D sensor or a thermographic camera. Sensor box 119 and camera 105 may be configured as an integrated unit or as separate units.

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

At least one of the above programs and data may be stored in a storage device other than storage device 108 (for example, a storage area of control device 101 (e.g., cache memory, etc.), ROM 102, RAM 103, or an external device (e.g., management terminal 200, etc.)) as long as the storage device is accessible by control device 101.

The management terminal 200 includes 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.

The control device 221 controls the management terminal 200. The control device 221 is, for example, composed of at least one integrated circuit. The integrated circuit is, for example, composed of at least one CPU, at least one ASIC, at least one FPGA, or a combination thereof.

An antenna (not shown) and other devices are connected to the communication interface 224. The management terminal 200 exchanges data with external communication devices via the antenna and access point. Examples of external communication devices include the sensor box 119.

The display 226 is realized, for example, by a liquid crystal display, an organic EL (electroluminescence) display, etc. The input device 229 is realized, for example, by a touch sensor provided on the display 226, a keyboard, a mouse, etc.

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

[Functional configuration of management terminal 200]
3 is a block diagram showing the functional configuration of management terminal 200 according to the embodiment. The functional configuration of management terminal 200 will be described with reference to FIG.

The management terminal 200 includes an acquisition unit 10, an analysis unit 12, a tagging processing unit 14, a display control unit 16, a statistical processing unit 18, an index calculation unit 20, and an image data storage unit 22.

The acquisition unit 10 acquires video data captured by a subject who is a resident of a room. The acquisition unit 10 may acquire video data stored in the storage device 228 of the management terminal 200, or may acquire video data acquired from the storage device 108 of the sensor box 119.

The analysis unit 12 analyzes the video data acquired by the acquisition unit 10 and extracts first information about people, including the subject, and second information about objects in the room.

The tagging processing unit 14 associates and assigns multiple tags related to the first information and second information extracted by the analysis unit 12 to the video data to assist in the evaluation of a predetermined action performed by the subject. The first information includes information related to the number of people and their actions. The information related to their actions includes information related to the actions of the subject and the actions of the caregiver. The second information includes information related to care products. The tagging processing unit 14 associates and assigns tags based on a combination of tags related to the first information and/or the second information to the video data.

The image data storage unit 22 stores multiple pieces of video data to which tags have been assigned by the tagging processing unit 14. The image data storage unit 22 may be provided as a partial area of the storage device 228, or may be provided in RAM 223. However, this is not limited to this, and the image data may also be stored outside the management terminal 200.

The display control unit 16 displays on the display unit a list of video data tagged with the input name from among the multiple video data stored in the image data storage unit 22.

The statistical processing unit 18 performs statistical processing on the analysis results of the video data over a predetermined period. The statistical processing unit 18 performs statistical processing based on at least one of the multiple tags assigned to the video data and the subject's behavioral trajectory obtained as a result of analyzing the video data, and calculates predetermined statistical values. The predetermined statistical values include values related to assistance, walking, and transfers.

The index calculation unit 20 calculates an index related to the ADL assessment or care level assessment based on at least one of the first information, the second information, and the statistical value.

Processing on the management terminal 200 is realized by software executed by each piece of hardware and the control device 221. Such software may be pre-stored in the storage device 228. Alternatively, the software may be stored on a CD-ROM or other recording medium and distributed as a computer program. Alternatively, the software may be provided as a downloadable application program by an information provider connected to the Internet. Such software is read from the recording medium by an optical disk drive or other reading device, or downloaded via the communication interface 224, and then temporarily stored on the hard disk 5. The software is read from the storage device 228 by the control device 221 and stored in RAM 223 in the form of an executable program. The control device 221 executes the program.

[Specific example of analysis processing]
4 is a diagram illustrating the analysis of video data by the analysis unit 12 according to an embodiment. Referring to FIG. 4, in this example, the analysis unit 12 analyzes scenes of the video data to extract first information about a person and second information and basic information about an object.

The first information to be extracted includes information about the number of people and their actions. Furthermore, the information about the number of people includes information about 0 to 2 people. In the case of two people, it includes information about the calculated distance between the two people. The information about the distance between the two people includes information about whether the distance between the two people is far or close, based on a comparison between the calculated information about the distance between the two people and a predetermined threshold.

Furthermore, behavioral information includes information on walking, turning, getting out of bed, getting up, and rolling over. In the case of walking, information on speed and unsteadiness is included. The analysis unit 12 acquires the person's behavioral trajectory from scenes in the video data and calculates speed and unsteadiness information. Speed is calculated based on the distance traveled/travel time traveled as the behavioral trajectory. Unsteadiness information can be calculated, for example, from the deviation from a linear movement trajectory as the person's behavioral trajectory. For example, when the start and end points of the person's behavioral trajectory are indicated, the degree of unsteadiness may be determined based on the deviation between the linear distance connecting the start and end points and the actual distance traveled. In determining the degree of unsteadiness, a position where the person remains stationary for a certain period of time (e.g., 10 seconds) or more may be used as the start point, and a position where the person remains stationary for a certain period of time (e.g., 10 seconds) or more after starting movement from the start point may be used as the end point. The deviation amount may be compared with a predetermined threshold value, and if the deviation amount is equal to or greater than the predetermined threshold value, the degree of fluctuation may be determined to be large, and if the deviation amount is less than the predetermined threshold value, the degree of fluctuation may be determined to be small.

The second information to be extracted includes information about location and assistive devices. Furthermore, the information about location includes information about bed, toilet, television, and other information. Information about assistive devices includes information about none, walker, cane, chair, and wheelchair.

Basic information includes information about the time of day and the year and month. Furthermore, information about the time of day includes information about morning, evening, daytime, and night. Information about the year and month includes information about the year and month.

The tagging processing unit 14 associates and assigns multiple tags to the video data using the first information, second information, and basic information extracted by the analysis unit 12.

For example, tags indicating the number of people, "0 people" to "2 people," are associated with the video data and assigned. In the case of the tag "2 people," distance-related tags, "far" and "near," are further associated with the video data and assigned based on the calculated distance information.

As another example of tags, action tags such as "walking," "turning," "getting out of bed," "getting up," and "rolling over" are associated with and assigned to video data. In the case of the tag "walking," speed-related tags such as "fast" and "slow" are further associated with and assigned to the video data based on information about the calculated walking speed. Furthermore, unsteadiness-related tags such as "small" and "large" are associated with and assigned to the video data based on information about the calculated degree of sway.

As another example of tags, location tags such as "bed," "toilet," "television," and "other" are associated with video data. Assistive device tags such as "none," "walking frame," "cane," "chair," and "wheelchair" are associated with video data.

As another example of tags, tags related to basic information are associated with video data and assigned. Tags related to time of day, such as "morning," "evening," "night," and "day," are associated with video data and assigned. Tags related to year and month, such as "year" and "month," are associated with video data and assigned.

The tagging processing unit 14 uses the assigned tags to further associate and assign another tag based on the combination to the video data.

Figure 5 is a diagram illustrating the generation of another tag by the tagging processing unit 14 according to an embodiment. Referring to Figure 5, seven combination patterns PT0 to PT6 are shown here. However, this is not limiting, and other combination patterns may also be provided.

Specifically, pattern PT0 shows a case where the "with or without assistance" tag is assigned based on a combination of the tags "two people" and "close."

Pattern PT1 shows a case where the "transfer" tag is assigned based on a combination of the "rotation" and "wheelchair" tags.

Pattern PT2 shows a case where the tag "Walking (walking alone)" is assigned based on a combination of the tags "Alone," "Walking," and "None."

Pattern PT3 shows a case where the tag "walking (walking along)" is assigned based on a combination of the tags "one person," "walking," and "large."

Pattern PT4 shows a case where the tag "Transfer (unassisted)" is assigned based on a combination of the tags "One person," "Rotation," and "Wheelchair."

Pattern PT5 shows a case where the tag "Transfer (full assistance)" is assigned based on a combination of the tags "2 people," "rotation," "wheelchair," and "close."

Pattern PT6 shows a case where the tag "Walking (walker)" is assigned based on a combination of the tags "1 person," "Walking," and "Walker."

Figure 6 is a diagram illustrating the flow of tagging processing by the management terminal 200 according to an embodiment.

Referring to FIG. 6, the acquisition unit 10 acquires video data (step S2). The acquisition unit 10 acquires video data stored in the storage device 228 of the management terminal 200. For example, video data for every two minutes is stored in the storage device 228. Note that this time is not limited to two minutes and can be set to any time.

Next, the analysis unit 12 executes an analysis process to analyze the video data (step S4). The analysis unit 12 executes an analysis process to analyze every two minutes of video data as one scene.

Based on the analysis processing results, the analysis unit 12 extracts first information about people, including the subject, and second information about objects in the room (step S6). As an example, the analysis unit 12 extracts the first information, second information, and basic information according to the method described in Figure 4.

The tagging processing unit 14 executes a first tag output process that outputs tags based on the extracted first information and second information (step S8). Specifically, the tagging processing unit 14 outputs tags for the number of people, actions, location, assistive devices, and basic information.

Next, the tagging processing unit 14 executes a tag analysis process to analyze the tag combination related to the first information and second information output in the first tag output process (step S10). Specifically, the tagging processing unit 14 executes the tag analysis process to analyze the tag combination according to the method described in FIG. 5.

Next, the tagging processing unit 14 executes a second tag output process to output a tag based on the results of the tag analysis process (step S12). The tagging processing unit 14 outputs a tag of the corresponding pattern PT according to the method described in Figure 5.

Next, the tagging processing unit 14 executes a tagging process to associate and assign the output tag to the video data (step S14). The tagging processing unit 14 assigns the tag and stores the associated video data in the image data storage unit 22.

Then, the process ends (END).
The tagging processing unit 14 may store not only the tag but also the extracted first and second information and basic information together with the tag as detailed information in the image data storage unit 22 in association with the video data.

Figure 7 is a diagram illustrating an example of a management screen displayed on the display 226 of the management terminal 200 according to an embodiment. Referring to Figure 7, a management screen 300 is provided.

The management screen 300 has a tag input acceptance field 302 for accepting input of tag information, and a video list field 304. The video list field 304 displays thumbnail images of multiple video data.

By selecting a thumbnail image of each video data, it is possible to enlarge and play that video data. Alternatively, by selecting a thumbnail image of each video data, detailed information about that video data may be displayed. For example, tag data associated with that video data may be displayed.

The tag input acceptance field 302 has check boxes corresponding to multiple tags, and these check boxes are set up to accept check input.

As an example, in this example, the tags include "Getting out of bed," "Falling," "Walking frame," "Wheelchair," "Assistance," "Walking," "Walking independently," and "Transferring," and checkboxes can be checked via the input device 229. However, this is not limited to this, and it is also possible to provide checkboxes for multiple tags.

By checking the checkbox, video data associated with the tag stored in the image data storage unit 22 is searched for, and a list of the video data is displayed in the video list field 304.

In this example, the checkbox for the "Getting out of bed" tag is checked. A list of multiple video data items associated with the "Getting out of bed" tag is displayed.

This makes it easy to search for the video data you want based on tags. In this example, not only tags related to people but also tags related to objects are associated with the video data and saved, making it possible to tag from multiple perspectives and efficiently support the evaluation of a specific person's actions.

Figure 8 is a diagram illustrating an example of the processing flow for index calculation by the index calculation unit 20 according to an embodiment. This explains the case of classifying the levels of the basic operation stages of ICF staging.

Referring to FIG. 8, the index calculation unit 20 calculates an index related to the ADL assessment or care level assessment based on at least one of the first information, the second information, and the statistical value. The index calculation unit 20 also calculates an index related to the ADL assessment or care level assessment for video data within a predetermined period. Note that the predetermined period can also be set to one hour, one day, one month, or one year.

The index calculation unit 20 determines whether or not there is an independent walking tag based on the tags assigned to the video data. If it determines that there is an independent walking tag, it determines the walking speed.

The index calculation unit 20 uses the first information to determine whether the walking speed is 1 m/sec or greater.
If the index calculation unit 20 determines that the walking speed is 1 m/sec or more, the level of the basic movement stage is set to level 5.

If the index calculation unit 20 determines that the walking speed is less than 1 m/s, the basic movement stage level is set to level 4.

If the index calculation unit 20 determines that there is no independent walking tag based on the tags assigned to the video data, it uses statistical values to determine the transfer rate. If the statistical value indicates that the transfer rate is 20% or higher, the index calculation unit 20 sets the basic movement stage level to level 4.

If the statistical value of the transfer rate is less than 20%, the index calculation unit 20 sets the level of the basic operation stage to level 3. If the statistical value of the transfer rate is 0%, the index calculation unit 20 determines whether or not there is a turning tag based on the tags attached to the video data. If it determines that there is a turning tag, the index calculation unit 20 sets the level of the basic operation stage to level 2. If it determines that there is no turning tag based on the tags attached to the video data, the index calculation unit 20 sets the level of the basic operation stage to level 1.

The levels of the basic action stages can be classified according to this processing flow. Note that this processing is just one example, and other parameters can be combined, and the levels may be classified into multiple stages rather than just five.

Figure 9 is a diagram illustrating another example of a management screen displayed on the display 226 of the management terminal 200 according to an embodiment. Referring to Figure 9, a management screen 310 is provided.

The management screen 310 displays walking speed transition data as statistical values calculated by the statistical processing unit 18, posture frame count status 312, and basic movement stage 314 data as an index calculated by the index calculation unit 20. The basic movement stage 314 data shows level 4. The management screen 310 also displays video data of the subject. However, this is not limited to this, and multiple other statistical data and index values may be displayed. Specifically, the statistical processing unit 18 performs statistical processing based on at least one of multiple tags assigned to the video data over a predetermined period (e.g., one week) and the subject's behavioral trajectory, and calculates predetermined statistical values. The predetermined statistical values include values related to assistance, walking, and transfers. The statistical processing unit 18 may perform statistical processing of transfer status, degree of assistance, walking status, etc. based on multiple tags assigned to the video data, and calculate statistical values. For example, as the transfer status, statistical values of the percentage of people not receiving assistance (transfer time), the percentage of people being supervised, the percentage of people receiving partial assistance, and the percentage of people receiving full assistance may be calculated based on tags attached to video data within a specified period. As the walking status, statistical values of the percentage of people walking independently (average walking speed, average degree of unsteadiness), the percentage of people walking with support, and the percentage of people receiving assistance may be calculated based on tags attached to video data within a specified period. Note that the specified period is not limited to this, and can also be in units of one hour, one day, one month, or one year.

Based on this data, it is possible to efficiently support the evaluation of the subject's specified actions.

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

This technology can be applied to information collected in hospitals, nursing homes, care facilities, and other facilities.

5 Hard disk, 104, 224 Communication interface, 10 Acquisition unit, 12 Analysis unit, 14 Tagging processing unit, 16 Display control unit, 18 Statistical processing unit, 20 Index calculation unit, 22 Image data storage unit, 100 Monitoring system, 101, 221 Control device, 102, 222 ROM, 103, 223 RAM, 105 Camera, 108, 228 Storage device, 110 Room, 111 Resident, 112 Dresser, 113 Bed, 119 Sensor box, 180 Room area, 190 Network, 200 Management terminal, 226 Display, 229 Input device, 300, 310 Management screen.

Claims (15)

an acquisition unit that acquires video data of a subject in a room;
an analysis unit that analyzes the video data acquired by the acquisition unit and extracts first information about people including the subject and second information about objects in the room;
a tagging processing unit that associates a plurality of tags related to the first information and the second information extracted by the analysis unit with the video data in order to assist in evaluation of the predetermined action performed by the subject;
A video processing device comprising: a statistical processing unit that performs statistical processing based on at least one of the multiple tags assigned to the video data over a predetermined period and the subject's behavioral trajectory obtained by analyzing the video data, and calculates predetermined statistical values . The video processing device described in claim 1, wherein the first information includes information regarding the number of people and their behavior. The video processing device of claim 2, wherein the information regarding the behavior includes information regarding the subject's behavior and the caregiver's behavior. The video processing device described in any one of claims 1 to 3, wherein the second information includes information about nursing care products. The video processing device described in any one of claims 1 to 4, wherein the tagging processing unit associates a tag based on a combination of tags related to the first information and/or the second information with the video data and assigns it to the video data. a video storage unit that stores a plurality of video data items tagged by the tagging processing unit;
a tag name input receiving unit that receives an input of the tag name by a user;
The video processing device according to any one of claims 1 to 5, further comprising a display control unit that displays on a display unit a list of video data tagged with the name input in the tag name input receiving unit from among the plurality of video data stored in the video storage unit. The video processing device described in any one of claims 1 to 6, wherein the predetermined statistical values include values related to assistance, walking, and transfers. The video processing device of any one of claims 1 to 7, further comprising an index calculation unit that calculates an index related to ADL assessment or care level assessment based on at least one of the first information, the second information, and the statistical value. an acquisition unit that acquires video data of a subject in a room;
an analysis unit that analyzes the video data acquired by the acquisition unit and extracts first information about people including the subject and second information about objects in the room;
a tagging processing unit that associates a plurality of tags related to the first information and the second information extracted by the analysis unit with the video data in order to assist in evaluation of the predetermined action performed by the subject;
A video processing system comprising: a statistical processing unit that performs statistical processing based on at least one of the multiple tags assigned to the video data over a predetermined period and the subject's behavioral trajectory obtained by analyzing the video data, and calculates predetermined statistical values . A video processing method executed by a computer of a video processing device, comprising:
acquiring video data of a subject in a room;
analyzing the acquired video data to extract first information about people including the subject and second information about objects in the room;
assigning a plurality of tags relating to the extracted first information and the extracted second information to the video data in association with the video data in order to assist in evaluation of the predetermined action performed by the subject;
A video processing method comprising a step of performing statistical processing based on at least one of the plurality of tags assigned to the video data over a predetermined period of time and the subject's behavioral trajectory obtained by analyzing the video data, and calculating a predetermined statistical value . acquiring video data of a subject in a room;
analyzing the acquired video data to extract first information about people including the subject and second information about objects in the room;
assigning a plurality of tags relating to the extracted first information and the extracted second information to the video data in association with the video data in order to assist in evaluation of the predetermined action performed by the subject;
A video processing program that causes a computer to execute processing, the program comprising: a step of performing statistical processing based on at least one of the plurality of tags assigned to the video data over a predetermined period of time and the subject's behavioral trajectory obtained by analyzing the video data, and calculating a predetermined statistical value . an acquisition unit that acquires video data of a subject in a room;
an analysis unit that analyzes the video data acquired by the acquisition unit;
a tagging processing unit that assigns a plurality of tags to the video data in association with the tags based on an analysis result of the analysis unit in order to assist in evaluation of the predetermined action performed by the subject,
the tagging processing unit assigns, to the video data, a first classification tag related to the subject, a second classification tag related to an object in the room, and a third classification tag based on a combination of the first classification tag and/or the second classification tag based on the analysis result of the analysis unit ;
The video processing device further includes a statistical processing unit that performs statistical processing based on at least one of the tags of the first to third classifications assigned to the video data during a predetermined period and the behavioral trajectory of the subject obtained by analyzing the video data, and calculates predetermined statistical values . an acquisition unit that acquires video data of a subject in a room;
an analysis unit that analyzes the video data acquired by the acquisition unit;
a tagging processing unit that assigns a plurality of tags to the video data in association with the tags based on an analysis result of the analysis unit in order to assist in evaluation of the predetermined action performed by the subject,
the tagging processing unit assigns, to the video data, a first classification tag related to the subject, a second classification tag related to an object in the room, and a third classification tag based on a combination of the first classification tag and/or the second classification tag based on the analysis result of the analysis unit;
The video processing system further includes a statistical processing unit that performs statistical processing based on at least one of the tags of the first to third classifications assigned to the video data during a predetermined period and the behavioral trajectory of the subject obtained by analyzing the video data, and calculates predetermined statistical values . A video processing method executed by a computer of a video processing device, comprising:
acquiring video data of a subject in a room;
analyzing the acquired video data;
and assigning a plurality of tags to the video data based on the analysis results in order to assist in evaluation of the predetermined motion performed by the subject.
the assigning step includes a step of assigning, to the video data, a first classification tag related to the subject, a second classification tag related to an object in the room, and a third classification tag based on a combination of the first classification tag and/or the second classification tag, based on the analysis result ;
The video processing method further comprises a step of performing statistical processing based on at least one of the tags of the first to third classifications assigned to the video data during a predetermined period and the behavioral trajectory of the subject obtained by analyzing the video data, and calculating a predetermined statistical value . acquiring video data of a subject in a room;
analyzing the acquired video data;
and assigning a plurality of tags to the video data based on the analysis results in order to assist in evaluation of the predetermined motion performed by the subject.
the assigning step includes a step of assigning, to the video data, a first classification tag related to the subject, a second classification tag related to an object in the room, and a third classification tag based on a combination of the first classification tag and/or the second classification tag, based on the analysis result ;
A video processing program that causes a computer to execute processing, further comprising a step of performing statistical processing based on at least one of the tags of the first to third classifications assigned to the video data over a predetermined period and the behavioral trajectory of the subject obtained by analyzing the video data, and calculating a predetermined statistical value .