JP2022050273A - System, electronic apparatus, method for controlling electronic apparatus, and program - Google Patents

Abstract

To provide a system, an electronic apparatus, a method for controlling an electronic apparatus, and a program that can contribute to the safety of a subject to be monitored.SOLUTION: A system 1 comprises: an imaging unit 20 that picks up an image of a subject to be monitored; an extraction unit 11 that extracts the coordinates of predetermined parts of the subject to be monitored from the image picked up by the imaging unit; and a controller 15 that, based on timing information indicating a starting point and an ending point of a pica behavior of the subject to be monitored in a sequential image picked up by the imaging unit, performs machine learning of the relationship between the coordinates of the predetermined parts between the starting point and the ending point and the start of the pica behavior of the subject to be monitored.SELECTED DRAWING: Figure 1

Description

The present disclosure relates to systems, electronic devices, control methods for electronic devices, and programs.

For example, in a field such as a long-term care facility, a device for monitoring the behavior of a monitored person such as a nurse or a care recipient has been proposed. For example, Patent Document 1 discloses a monitored person system that detects a predetermined behavior in a monitored person based on an image obtained by an image pickup device. Patent Document 2 discloses a fall prevention system that prevents a subject from falling while walking by attaching a detection device to the foot of the subject. Further, Patent Document 3 discloses a monitoring support device for determining the body position of a human body by detecting a temperature distribution. In addition, Patent Document 4 discloses a medical system for monitoring elderly psychotic patients at home or in an elderly home or a long-term care facility.

Japanese Unexamined Patent Publication No. 2017-91552 Japanese Unexamined Patent Publication No. 2017-221502 Japanese Unexamined Patent Publication No. 2014-106636 Japanese Patent Application Laid-Open No. 2003-91790

It would be beneficial if the monitored object could be safely monitored by monitoring it.

An object of the present disclosure is to provide a system, an electronic device, a control method for the electronic device, and a program that can be safely provided to the monitored object.

The system according to one embodiment is
An imaging unit that captures the monitored object and
An extraction unit that extracts the coordinates of a predetermined portion of the monitored object from the image captured by the imaging unit, and an extraction unit.
Based on the timing information indicating the start time point and the end time point of the pica behavior of the monitored object in the time-dependent image captured by the image pickup unit, the coordinates of the predetermined portion between the start time point and the end time point and the coordinates. , A controller that machine-learns the relationship with the start of pica behavior of the monitored object,
To prepare for.

In addition, the system according to one embodiment is
An imaging unit that captures the monitored object and
An extraction unit that extracts the coordinates of a predetermined portion of the monitored object from the image captured by the imaging unit, and an extraction unit.
The relationship between the coordinates of the predetermined part of the human between the start time and the end time of the human pica and the start of the human pica is extracted by the extraction unit based on the machine learning data obtained by machine learning. A controller that estimates the start of pica behavior of the monitored object from the coordinates of the predetermined portion of the monitored object, and
To prepare for.

The electronic device according to the embodiment is
An extraction unit that extracts the coordinates of a predetermined part of the monitored object from an image captured including the monitored object, and an extraction unit.
The predetermined portion between the start time point and the end time point based on the timing information indicating the start time point and the end time point of the pica behavior of the monitored object in the time-dependent image taken including the monitored object. A controller that machine-learns the relationship between the coordinates of the above and the start of the pica behavior of the monitored object.
To prepare for.

Further, the electronic device according to the embodiment is
An extraction unit that extracts the coordinates of a predetermined part of the monitored object from an image captured including the monitored object, and an extraction unit.
The relationship between the coordinates of the predetermined part of the human between the start time and the end time of the human pica and the start of the human pica is extracted by the extraction unit based on the machine learning data obtained by machine learning. A controller that estimates the start of pica behavior of the monitored object from the coordinates of the predetermined portion of the monitored object, and
To prepare for.

The method for controlling an electronic device according to an embodiment is as follows.
An imaging step that captures the monitored object, and
An extraction step of extracting the coordinates of a predetermined portion of the monitored object from the image captured by the imaging step, and an extraction step.
Based on the timing information indicating the start time point and the end time point of the pica behavior of the monitored object in the time-dependent image captured by the imaging step, the coordinates of the predetermined portion between the start time point and the end time point and the coordinates. , The machine learning step of machine learning the relationship with the start of pica behavior of the monitored object,
including.

Further, the control method of the electronic device according to the embodiment is as follows.
An imaging step that captures the monitored object, and
An extraction step of extracting the coordinates of a predetermined portion of the monitored object from the image captured by the imaging step, and an extraction step.
The relationship between the coordinates of the predetermined part of the human between the start time and the end time of the human pica and the start of the human pica is extracted by the extraction step based on the machine learning data obtained by machine learning. An estimation step for estimating the start of pica behavior of the monitored object from the coordinates of the predetermined portion of the monitored object, and an estimation step.
including.

The program according to one embodiment is
On the computer
An imaging step that captures the monitored object, and
An extraction step of extracting the coordinates of a predetermined portion of the monitored object from the image captured by the imaging step, and an extraction step.
Based on the timing information indicating the start time point and the end time point of the pica behavior of the monitored object in the time-dependent image captured by the imaging step, the coordinates of the predetermined portion between the start time point and the end time point and the coordinates. , The machine learning step of machine learning the relationship with the start of pica behavior of the monitored object,
To execute.

In addition, the program according to one embodiment is
On the computer
An imaging step that captures the monitored object, and
An extraction step of extracting the coordinates of a predetermined portion of the monitored object from the image captured by the imaging step, and an extraction step.
The relationship between the coordinates of the predetermined part of the human between the start time and the end time of the human pica and the start of the human pica is extracted by the extraction step based on the machine learning data obtained by machine learning. An estimation step for estimating the start of pica behavior of the monitored object from the coordinates of the predetermined portion of the monitored object, and an estimation step.
To execute.

According to one embodiment, it is possible to provide a system, an electronic device, a control method for the electronic device, and a program that can be safely provided to the monitored object.

It is a functional block diagram which shows the schematic structure of the system which concerns on one Embodiment. It is a flowchart explaining the operation of the learning phase by the system which concerns on one Embodiment. It is a flowchart which shows the example of the image imaged by the system which concerns on one Embodiment. It is a figure which shows the example of the predetermined part extracted by the system which concerns on one Embodiment. It is a figure which shows the example of the predetermined part extracted from the image imaged by the system which concerns on one Embodiment. It is a figure explaining the coordinates of the predetermined part extracted by the system which concerns on one Embodiment. It is a figure explaining the timing information acquired by the system which concerns on one Embodiment. It is a figure which shows the example of the predetermined part extracted by the system which concerns on one Embodiment. It is a figure which shows the example of the predetermined part extracted by the system which concerns on one Embodiment. It is a flowchart explaining the operation of the estimation phase by the system which concerns on one Embodiment. It is a figure explaining the estimation by the system which concerns on one Embodiment.

In the present disclosure, the "electronic device" may be a device driven by electric power. Further, the "system" may include a device driven by electric power. Further, the "user" may be a person (typically a human being) who uses the system and / or the electronic device according to the embodiment. The user may include a person who monitors the monitored object by using the system and / or the electronic device according to the embodiment. Further, the “monitored target” may be a person (for example, a human or an animal) to be monitored by the system and / or the electronic device according to the embodiment. Further, the user may include a monitored object.

It is assumed that the system according to one embodiment is used in a specific situation used by a person engaged in social activities such as a company, a hospital, an elderly home, a school, a sports gym, and a long-term care facility. It may be a facility. For example, in the case of a company, it is extremely important to understand and / or manage the health status of employees. Similarly, it is extremely important to understand and / or manage the health status of patients and medical staff in hospitals, and residents and staff in elderly housing with care. The scene in which the system according to the embodiment is used is not limited to the above-mentioned facilities such as companies, hospitals, and elderly housings, but any facility where it is desired to grasp and / or manage the health condition of the monitored object. May be. Any facility may also include non-commercial facilities, such as the user's home. Further, the scene in which the system according to one embodiment is used may be, for example, a moving body such as a train, a bus, or an airplane, a station, a platform, or the like.

The system according to one embodiment may be used for monitoring the behavior of a monitored object such as a nurse-requiring person or a nursing-requiring person in a nursing care facility or the like. That is, the system according to the embodiment may be a monitoring system. The system according to one embodiment can monitor pica behavior of a monitored object such as a nurse or a care recipient. Here, pica is the act of the monitored object eating something other than food (hereinafter referred to as "foreign matter").

In particular, the system according to one embodiment can issue a predetermined warning before the pica of a monitored person such as a nurse or a care recipient ends, for example, before a foreign substance is put into the mouth. .. Therefore, according to the system according to one embodiment, a staff member such as a long-term care facility eats a pica before a monitored person such as a nurse or a care recipient puts a foreign substance in his or her mouth. Can be recognized.

Hereinafter, the system according to the embodiment will be described in detail with reference to the drawings.

FIG. 1 is a diagram showing a schematic configuration of a system according to an embodiment. As shown in FIG. 1, the system 1 according to an embodiment may be configured to include an electronic device 10 and an image pickup unit 20. The electronic device 10 and the image pickup unit 20 may be connected by wire or wireless, or a combination of wire and wireless. The system 1 according to one embodiment does not have to include a part of the functional unit shown in FIG. 1, and may include a functional unit other than that shown in FIG. For example, the system 1 according to the embodiment does not have to include at least one of the warning unit 17 and the communication unit 19. Further, for example, the system 1 according to the embodiment may include a display capable of displaying an image and / or a slot into which a storage such as a memory card can be inserted.

The image pickup unit 20 shown in FIG. 1 may include an image sensor that electronically captures an image, such as a digital camera. The image pickup unit 20 may be configured to include an image pickup device that performs photoelectric conversion, such as a CCD (Charge Coupled Device Image Sensor) or a CMOS (Complementary Metal Oxide Sensor) sensor. The image pickup unit 20 may take an image of the monitored object T, for example, as shown in FIG. Here, the monitored target T may be, for example, a human being. The image pickup unit 20 may convert the captured image into a signal and transmit it to the electronic device 10. For example, the image pickup unit 20 may transmit a signal based on the captured image to the extraction unit 11, the storage unit 13, and / or the controller 15 of the electronic device 10. The image pickup unit 20 is not limited to an image pickup device such as a digital camera as long as it captures the monitored target T, and may be any device.

In one embodiment, the imaging unit 20 may image, for example, the monitored target T as a still image at predetermined time intervals (for example, 15 frames per second). Further, in one embodiment, the imaging unit 20 may image, for example, the monitored target T as a continuous moving image.

As shown in FIG. 1, the electronic device 10 according to the embodiment may include an extraction unit 11, a storage unit 13, a controller 15, a warning unit 17, and a communication unit 19. The electronic device 10 according to the embodiment does not have to include a part of the functional unit shown in FIG. 1, and may include a functional unit other than that shown in FIG. For example, the electronic device 10 according to the embodiment may include machine learning data 132, which will be described later, stored in the storage unit 13. For example, in the electronic device 10 according to the embodiment, at least a part of the machine learning data 132 described later may be stored in an external device such as an external server.

The extraction unit 11 may have a function of extracting a predetermined feature point from the image captured by the image pickup unit 20. For example, the extraction unit 11 may extract the coordinates of a feature point such as a predetermined part of the body of the monitored object T from the image of the monitored object T captured by the imaging unit 20. Here, the feature points will be further described later. In one embodiment, the extraction unit 11 obtains the coordinates of each part such as the head, trunk, limbs, and / or joints of the monitored object T from the image of the monitored object T captured by the imaging unit 20. It may be extracted. The extraction unit 11 may be configured as dedicated hardware, may be configured to include software at least in part, or may be configured entirely by software. In this way, the extraction unit 11 may extract the coordinates of the predetermined portion of the monitored target T from the image captured by the image pickup unit 20.

The extraction unit 11 may have a function of recognizing an object in the image from the image captured by the image pickup unit 20. In one embodiment, the extraction unit 11 recognizes an object such as a table, food, tableware, tissue paper, flowers, wrapping paper, or a PET bottle lid from the image captured by the image pickup unit 20, and recognizes the recognized object. The position may be specified. Here, object recognition will be further described later.

The storage unit 13 may have a function as a memory for storing various types of information. The storage unit 13 may store, for example, a program executed by the controller 15, the result of processing executed by the controller 15, and the like. Further, the storage unit 13 may function as a work memory of the controller 15. The storage unit 13 can be configured by, for example, a semiconductor memory or the like, but is not limited to this, and can be any storage device. For example, the storage unit 13 may be a storage medium such as a memory card inserted in the electronic device 10 according to the embodiment. Further, the storage unit 13 may be the internal memory of the CPU used as the controller 15 described later, or may be connected to the controller 15 as a separate body.

As shown in FIG. 1, the storage unit 13 may store, for example, machine learning data 132. Here, the machine learning data 132 may be data generated by machine learning. Further, machine learning may be based on AI (Artificial Intelligence) technology that enables a specific task to be executed by training. More specifically, machine learning may be a technique in which an information processing device such as a computer learns a large amount of data and automatically constructs an algorithm or model for performing tasks such as classification and / or prediction. As used herein, parts of the AI may include machine learning. In the present specification, machine learning may include supervised learning that learns the features or rules of input data based on correct answer data. Further, the machine learning may include unsupervised learning in which the features or rules of the input data are learned in the absence of correct answer data. Further, the machine learning may include reinforcement learning for learning the features or rules of the input data by giving a reward or a punishment. Further, in the present specification, machine learning may be any combination of supervised learning, unsupervised learning, and reinforcement learning. The concept of machine learning data 132 of the present embodiment may include an algorithm that outputs a predetermined inference (estimation) result by using an algorithm learned for input data. In this embodiment, as this algorithm, for example, linear regression that predicts the relationship between the dependent variable and the independent variable, a neural network (NN) that mathematically models a human brain nervous system neuron, and a least squares that squares an error are calculated. Other appropriate algorithms such as multiplication, decision trees that make problem solving a tree structure, and regularization that transforms data in a predetermined way can be used. In this embodiment, deep learning, which is a kind of neural network, may be used. Deep learning is a type of neural network, and a neural network with a deep network hierarchy is called deep learning.

In the technique of the present disclosure, it may be assumed that there is generally a certain relationship between the motion a of the body of the monitored object and the motion result A of the monitored object generated from this motion a. The operation result here may include the operation of the monitored object, the operation start time of the monitored object, an accident, an incident or other event generated from the operation of the monitored object. For example, it is assumed that the movement a of the body of the monitored target is performed, and the movement result A of the monitored target is generated from this movement a. Further, it is assumed that the movement b of the body to be monitored is performed, and the movement result B of the monitored target is generated from this movement b. The technique of the present disclosure accumulates the relationship between the operation a and the operation result A, the operation b and the operation result B, and other operations and the operation result as machine learning data. Then, in the technique of the present disclosure, when the motion x is extracted, the motion result X related to the motion x may be estimated by using the machine learning data.

In particular, in one embodiment, the machine learning data 132 may be machine-learned data on the movement of feature points when the monitored target T has a pica. Hereinafter, the act of the monitored target T eating pica and the action related to pica may be referred to as "pica act". Further, the machine learning data 132 may be data obtained by machine learning the movement of a feature point when a specific person (for example, a specific person requiring long-term care) as a monitored target T eats differently. The machine learning data 132 will be further described later.

The controller 15 controls and / or manages the entire electronic device 10 including each functional unit constituting the electronic device 10. The controller 15 may include at least one processor, such as a CPU (Central Processing Unit), in order to provide control and processing power to perform various functions. The controller 15 may be realized collectively by one processor, may be realized by several processors, or may be realized by individual processors. The processor may be realized as a single integrated circuit. The integrated circuit is also referred to as an IC (Integrated Circuit). The processor may be realized as a plurality of communicably connected integrated circuits and discrete circuits. The processor may be implemented on the basis of various other known techniques.

In one embodiment, the controller 15 may be configured as, for example, a CPU and a program executed by the CPU. The program executed by the controller 15, the result of the process executed by the controller 15, and the like may be stored in, for example, the storage unit 13. The controller 15 may appropriately include a memory necessary for the operation of the controller 15. The operation of the controller 15 of the electronic device 10 according to the embodiment will be further described later.

The warning unit 17 may issue a predetermined warning for calling attention to the user of the system 1 or the electronic device 10 based on the predetermined warning signal output from the controller 15. As a predetermined warning, the warning unit 17 may be any functional unit that stimulates at least one of the user's auditory sense, visual sense, and tactile sense, such as sound, voice, light, characters, video, and vibration. Specifically, the warning unit 17 may be at least one of, for example, an audio output unit such as a buzzer or a speaker, a light emitting unit such as an LED, a display unit such as an LCD, and a tactile presentation unit such as a vibrator. .. In this way, the warning unit 17 may issue a predetermined warning based on a predetermined warning signal output from the controller 15. In one embodiment, the warning unit 17 may issue a predetermined alarm as information acting on at least one of auditory, visual, and tactile sensations.

In one embodiment, the warning unit 17 may issue a warning that the monitored target T is at risk of eating a pica, for example, before the monitored target T starts pica. Further, in one embodiment, the warning unit 17 may issue a warning that the monitored target T is at risk of eating a pica before the monitored target T ends the pica. For example, in one embodiment, the warning unit 17 that outputs visual information may warn the user by emitting light or a predetermined display when an act of suspected pica of the monitored target T is detected. Further, in one embodiment, the warning unit 17 that outputs auditory information may warn the user by a predetermined sound or voice when an act of suspected pica of the monitored target T is detected. In the present embodiment, the warning may be a combination of light emission or a predetermined display, and a predetermined sound or voice.

The electronic device 10 shown in FIG. 1 has a built-in warning unit 17. However, in the system 1 according to the embodiment, the warning unit 17 may be provided outside the electronic device 10. In this case, the warning unit 17 and the electronic device 10 may be connected by wire or wireless, or a combination of wire and wireless.

The communication unit 19 has an interface function for communicating by wire or wirelessly. The communication method performed by the communication unit 19 of one embodiment may be a wireless communication standard. For example, wireless communication standards include communication standards for cellular phones such as 2G, 3G, 4G, and 5G. For example, the communication standards for cellular phones are LTE (Long Term Evolution), W-CDMA (Wideband Code Division Multiple Access), CDMA2000, PDC (Personal Digital Cellular), GSM (Registered Trademark) and GlobalS. (Personal Handy-phone System) and the like are included. For example, wireless communication standards include WiMAX (Worldwide Internet Access), IEEE802.11, WiFi, Bluetooth®, IrDA (Infrared Data Association), Co., etc., and FC. The communication unit 19 can support one or more of the above communication standards. The communication unit 19 may be configured to include, for example, an antenna for transmitting and receiving radio waves, an appropriate RF unit, and the like. Further, the communication unit 19 may be configured as an interface such as a connector for making a wired connection to the outside. The communication unit 19 can be configured by a known technique for performing wireless communication.

Various types of information received by the communication unit 19 may be supplied to, for example, the storage unit 13 and / or the controller 15. Various information received by the communication unit 19 may be stored in, for example, a memory built in the storage unit 13 and / or the controller 15. Further, the communication unit 19 may transmit, for example, the processing result by the controller 15, the extraction result by the extraction unit 11, and / or the information stored in the storage unit 13 to the outside.

As shown in FIG. 1, at least a part of each functional unit constituting the electronic device 10 according to the embodiment may be configured by specific means in which software and hardware resources cooperate.

Next, the operation of the system 1 according to the embodiment will be described.

The operation of the system 1 according to one embodiment can be typically divided into a "learning phase" and an "estimation phase". In the learning phase, an operation of machine learning the relationship between the position (coordinates) of each part of the body in a human pica behavior such as the monitored target T and the timing of the pica behavior may be performed. Further, in the estimation phase, based on the result of machine learning in the learning phase, the operation of estimating the start of the pica action may be performed from the position (coordinates) of each part of the body in the pica action of the monitored target T. Here, in the present disclosure, for example, there may be a plurality of systems 1 shown in FIG. 1, and the systems that carry out the "learning phase" and the "estimation phase" may be different systems.

FIG. 2 is a flowchart showing an example of the operation in the learning phase of the system 1 according to the embodiment. FIG. 2 may be a flowchart focusing on the operation of the electronic device 10 included in the system 1 according to the embodiment in the learning phase.

For example, in a long-term care facility, a person with impaired cognitive function, such as the elderly, may be at risk of eating a pica. In the learning phase of the system 1 according to one embodiment, as described above, machine learning is performed on the relationship between the position (coordinates) of each part of the body in a human pica such as the monitored target T and the timing of the pica. You can do it.

At the time when the operation shown in FIG. 2 starts, the imaging unit 20 of the system 1 may start imaging a human such as the monitored target T. The time point at which the operation shown in FIG. 2 starts may be the time point at which the image pickup unit 20 starts image pickup of a human such as the monitored target T. Further, the time point at which the operation shown in FIG. 2 starts may be a time point when a person such as a monitored target T enters the image pickup range of the image pickup unit 20 after the image pickup unit 20 starts image pickup.

When the operation shown in FIG. 2 starts, the controller 15 of the electronic device 10 acquires the image captured by the image pickup unit 20 (step S11).

FIG. 3 is a diagram showing an example of an image acquired by the controller 15 in step S11 shown in FIG. 2, that is, an image captured by the imaging unit 20.

As shown in FIG. 3, the image pickup unit 20 may take an image of a state in which a person such as a monitored target T is sitting on a chair. The controller 15 acquires an image in which a human such as the monitored target T is seated from the image pickup unit 20.

The image pickup unit 20 may capture each image of a predetermined number of frames per second. Here, the image captured by the image pickup unit 20 may be a still image of continuous frames or a moving image. For example, the image pickup unit 20 may capture an image of 15 frames per second. In step S11, the controller 15 may acquire images of a predetermined number of frames per second captured by the image pickup unit 20.

As shown in FIG. 2, when the image captured in step S11 is acquired, the extraction unit 11 executes object recognition in the image (step S12). Here, the operation in step S12 may be performed by the controller 15 instead of the extraction unit 11.

In step S12, the extraction unit 11 recognizes what the object included in the image is. The object to be recognized may exclude a human such as the monitored object T. In the example of FIG. 3, only the object placed on the table may be recognized. In the present embodiment, the extraction unit 11 recognizes at least a foreign substance contained in the image. Foreign substances are non-food items such as tissue paper, flowers, soap, pieces of cloth, paper diapers, coins, detergents, bleach, tobacco, pesticides, batteries, wrapping paper, PET bottle lids, buttons, rubber, etc. May include plastic bags, keys, etc. Here, the extraction unit 11 does not have to recognize a tool for carrying food such as a spoon, a fork, and chopsticks to the mouth as a foreign substance. The foreign matter in the present disclosure may also include food-related substances such as banana peels and fish bones.

In step S12, the extraction unit 11 may use a known object recognition method. For example, the extraction unit 11 may execute object recognition using a pre-learned deep learning model.

The result of object recognition may be used for determination of an action of gripping a foreign substance, an action of eating a foreign substance, and the like. For example, when the object carried to the mouth of the monitored object T includes an object recognized as a foreign substance, it may be determined that the human action in the image is an action of eating the foreign substance. For example, when the object carried to the mouth of the monitored object T is recognized as food and a spoon, it may be determined that the human action in the image is not the action of eating a foreign substance.

As shown in FIG. 2, when the object recognition is executed in step S12, the extraction unit 11 extracts the coordinates of the predetermined portion of the body of the monitored target T (step S13). Here, the operation in step S13 may be performed by the controller 15 instead of the extraction unit 11.

FIG. 4 is a diagram showing an example of a predetermined portion of the body of the monitored target T extracted in step S13.

In step S13, the extraction unit 11 may extract the coordinates of a predetermined portion of the body of the monitored target T as shown in FIG. 4, for example. As shown in FIG. 4, the predetermined portion from which the coordinates are extracted in step S13 may include, for example, the neck, left shoulder, left elbow, left wrist, right shoulder, right elbow, and right wrist in the body of the monitored object T. .. Further, as shown in FIG. 4, the predetermined portion for which the coordinates are extracted in step S13 further includes, for example, the left hip, left knee, left ankle, right hip, right knee, and right ankle in the body of the monitored target T. It's fine. As described above, the coordinates of the predetermined portion extracted in step S13 may be the coordinates of the predetermined joint point in the body of the monitored target T or the like.

FIG. 5 is a diagram showing an example of coordinates extracted as a predetermined part of the body of the monitored target T in the image shown in FIG. The image of the monitored object T shown in FIG. 5 is the same as the image of the monitored object T shown in FIG. FIG. 5 shows the coordinates extracted as a predetermined part of the body of the monitored object T shown in FIG. 4 in the image of the monitored object T shown in FIG.

In step S13, the extraction unit 11 extracts the coordinates of the plurality of dots shown in FIG. 5 as predetermined parts of the body of the monitored target T shown in FIG. For example, the extraction unit 11 may two-dimensionally extract the coordinates of the plurality of dots shown in FIG. 5 according to the coordinate axes shown in FIG. That is, the extraction unit 11 may indicate the lower left end of the image pickup range of the image captured by the image pickup unit 20 as the origin of the coordinate axis shown in FIG. For example, the extraction unit 11 acquires the coordinates of the position of the neck of the monitored target T shown in FIG. 5 according to the coordinate axes shown in FIG.

Here, when the imaging unit 20 captures each image of a predetermined number of frames per second, the extraction unit 11 may extract the image as a predetermined portion of the body of the monitored target T in the predetermined number of frames per second. Further, even when the controller 15 acquires images of a predetermined number of frames per second, the extraction unit 11 may extract the images of the monitored target T as a predetermined part in the body in the predetermined number of frames per second. As an example, the extraction unit 11 may extract a predetermined portion of the body of the monitored target T at 15 frames per second.

FIG. 6 is a diagram showing the coordinates of predetermined parts extracted in the body of the monitored target T collectively, for example, in 15 frames per second. As shown in FIG. 6, in step S13, the extraction unit 11 may arrange the coordinates of the predetermined portion extracted in the body of the monitored target T side by side for each frame. As shown in FIG. 6, the extraction unit 11 may extract the coordinates of a predetermined portion two-dimensionally (as X and Y coordinates) in the body of the monitored target T for each frame. In the table shown in FIG. 6, each row schematically shows how a predetermined part of the body of the monitored target T is extracted as X and Y coordinates in each frame. Also, in the table shown in FIG. 6, the rows showing each frame are shown from top to bottom over time. The coordinates of the 15 frames shown in FIG. 6 may be, for example, the coordinates of one second in the image (or moving image) as shown in FIG. 5 tracked. Further, even after the 15 frames shown in FIG. 6, the coordinates of a predetermined portion in the body of the monitored target T may be sequentially extracted.

As described above, in one embodiment, the extraction unit 11 two-dimensionally obtains the coordinates of a predetermined number of joint points in the body of the monitored target T for each image of a predetermined number of frames per second captured by the image pickup unit 20. It may be extracted.

After the coordinates of the predetermined portion are extracted in step S13, the extraction unit 11 sets the coordinates according to the maximum and minimum values of the coordinates (X, Y) in the extracted predetermined number of frames (for example, 15 frames per second). Normalize (step S14).

It is assumed that the coordinates of the predetermined portion extracted in step S13 vary due to, for example, the size of the body of the monitored target T. Further, it is assumed that the coordinates of the predetermined portion extracted in step S13 vary due to, for example, the distance between the image pickup unit 20 and the monitored target T, the direction from the image pickup unit 20 toward the monitored target T, and the like. Will be done. Therefore, in one embodiment, by normalizing the X-direction component and the Y-direction component of the coordinates extracted in step S13, the extracted coordinates can be used for machine learning in general.

In this case, for example, the extracted X and Y coordinates may be normalized based on the maximum and minimum values of the extracted X and Y coordinates in 15 frames per second. Here, the maximum value of the X coordinate extracted in step S13 is Xmax, and the minimum value of the X coordinate extracted in step S13 is Xmin. The maximum value of the X coordinate after normalization is X'max. In this case, the X coordinate (X) before normalization can be converted into the X coordinate (X') after normalization by using the following equation (1).

X'= ((X-Xmin) / (Xmax-Xmin)) · X'max (1)

Similarly, the maximum value of the Y coordinate extracted in step S13 is Ymax, and the minimum value of the Y coordinate extracted in step S13 is Ymin. The maximum value of the Y coordinate after normalization is Y'max. In this case, the Y coordinate (Y) before normalization can be converted into the Y coordinate (Y') after normalization by using the following equation (2).

Y'= ((Y-Ymin) / (Ymax-Ymin)) · Y'max (2)

By normalizing the X-direction component and the Y-direction component of the extracted coordinates according to the above equations (1) and (2), the individual difference of the monitored target T and the imaging unit 20 determine the monitored target T. It can be expected to reduce the influence of the imaged environment on machine learning.

As described above, in one embodiment, the extraction unit 11 sets each direction component of the coordinates of a predetermined number of joint points in the body of the monitored target T two-dimensionally extracted to the maximum value and the minimum value of each direction component. It may be normalized based on the value. Further, such an operation may be performed by the controller 15 instead of the extraction unit 11.

By normalizing the coordinates shown in step S14, each of the coordinates (X, Y) shown in FIG. 6 is normalized to the coordinates (X', Y'), respectively.

When the coordinates are normalized in step S14, the controller 15 acquires the timing information (step S15). In order for the controller 15 to acquire the timing information in step S15, the timing information needs to be added (set) in advance to the image acquired in step S11 or the coordinates extracted in step S13. Further, timing information may be given (set) in advance at the coordinates normalized in step S14 instead of the coordinates extracted in step S13.

In one embodiment, as data for the electronic device 10 to perform machine learning, it is necessary to prepare information indicating the start time point and the end time point of the pica behavior by a human being, for example, the monitored target T. If the monitored target T has information (timing information) indicating the start time point and the end time point of the eating behavior, the electronic device 10 can perform machine learning using this timing information as, for example, teacher data. As described above, the information indicating the start time and end time of the pica behavior by the monitored target T is also referred to as "timing information". That is, the "timing information" may be information indicating the start time point and the end time point of the pica action in the time-dependent image captured by the image pickup unit 20.

Such timing information may be given (set) by personnel such as staff. That is, for example, an ethology expert or a staff member of a long-term care facility may add timing information indicating pica while observing (visually recognizing) the image of the monitored target T captured by the imaging unit 20. In addition, by setting a predetermined standard for giving such timing information in advance, it is possible to give timing information even to general personnel who are not ethology specialists or staff of long-term care facilities. can. As described above, the timing information may be added (set) in the image data acquired in step S11. Further, the timing information may be added (set) in the coordinate data extracted in step S13. Further, the timing information may be given (set) in the coordinate data normalized in step S14.

FIG. 7 is a diagram illustrating the setting of timing information. FIG. 7 conceptually shows how the image data is continuous with the passage of time for each frame. That is, in FIG. 7, it is shown that the image data is continuous like frame 1, frame 2, frame 3, ... With the passage of time. Further, FIG. 7 shows an example of adding (setting) timing information to the image data. However, as described above, the image data shown in FIG. 7 may be replaced with the coordinate data or may be replaced with the normalized coordinate data.

At the time of "entering the room" shown in FIG. 7, for example, the state in which the monitored target T entered the room in which the image pickup unit 20 was installed was shown in the image data (image data) captured by the image pickup unit 20. do.

It is assumed that the image data shows that the monitored target T who has entered the room is seated in the chair at the time of "seating" shown in FIG. 7.

It is assumed that the image data shows that the monitored target T has started pica at the time of "start of pica" shown in FIG. 7.

It is assumed that the image data shows that the monitored target T has completed the pica act at the time of "end of pica" shown in FIG. 7.

It is assumed that the image data shows that the monitored object T, who was seated in the chair, left the chair at the time of "leaving the seat" shown in FIG. 7, for example, by standing up.

It is assumed that the image data shows that the monitored object T, who has left the chair, has left the room in which the image pickup unit 20 is installed, for example, at the time of “leaving the room” shown in FIG. 7.

In the image data captured as described above, at least the time point of "start of pica" and the time point of "end of pica" are indicated by, for example, an ethology expert, a staff member of a long-term care facility, or other general personnel. Timing information may be given (set).

Here, the time point of "start of pica" may be, for example, the time point when the monitored object T sitting on the chair starts the movement as shown in FIG. 8, for example. In FIGS. 5 and 8, for the sake of simplification, the coordinate data extracted in step S13 is shown instead of the image data acquired in step S11. FIG. 5 shows how the monitored object T is sitting on a chair. This may indicate the state from the time of "sitting" shown in FIG. 7 to the time of "starting pica".

Generally, when the monitored target T starts pica from the state shown in FIG. 5, the monitored target T reaches for a foreign substance and lifts it as shown in FIG. In one embodiment, timing information may be added (set) with the time point at which the transition from the state shown in FIG. 5 to the state shown in FIG. 8 is started as the time point of "start of pica". Strictly speaking, the time point of "start of pica" may be when the monitored object T grips an object recognized as a foreign substance. Further, in one embodiment, the time point of "start of pica" may be when the monitored object T grasps an object recognized as a foreign substance and lifts it to the height of the chest, for example.

As described above, in one embodiment, the start time point of the pica act in the timing information may indicate the timing when the monitored object T grips the foreign substance. Further, in one embodiment, the start time point of the pica act in the timing information may indicate the timing when the monitored object T grips and lifts the foreign substance.

Further, the time point of "end of pica" may be, for example, the time point when the monitored object T holding the foreign substance starts the movement as shown in FIG. 9, for example, as shown in FIG. In FIG. 9, for the sake of simplification, the coordinate data extracted in step S13 is shown instead of the image data acquired in step S11.

Generally, the monitored object T ends the pica by eating the grasped foreign substance. In one embodiment, timing information may be added (set) with the time point when the state shown in FIGS. 8 to 9 is reached as the time point of "end of pica". Strictly speaking, the time point of "end of pica" may be when the monitored object T brings the grasped foreign substance to his mouth.

As described above, in one embodiment, the end point of the pica act in the timing information may indicate the timing at which the monitored target T brings the grasped foreign substance to the mouth.

In the timing information, the time points of the above-mentioned "start of pica" and "end of pica" do not necessarily have to be given (set) in this order. That is, in the timing information, the time point of "end of pica" may be set first, and then the time point of "start of pica" may be set.

For example, it may not always be easy for personnel such as staff to determine the time point of "start of pica" when reproducing (visually recognizing) the image of the monitored target T captured by the imaging unit 20. is assumed. In such a case, first, when a staff member or the like reproduces (visually recognizes) the image of the monitored target T imaged by the imaging unit 20, the timing information is the time point of "end of pica". May be set as. Next, a staff member or the like may reproduce (visually recognize) the image of the monitored object T captured by the imaging unit 20 in the reverse time from the time of "end of pica". Here, when the image of the monitored target T captured by the image pickup unit 20 is reproduced in reverse in time, the speed of the reverse reproduction may be reduced to some extent. In this way, personnel such as staff can easily set the time point of "start of pica" which is a time point before "end of pica" in the timing information.

When the timing information is added (set) in the image data (or the coordinate data or the normalized coordinate data) as described above, the controller 15 can acquire the timing information in step S15. In step S15, the controller 15 of the electronic device 10 may acquire the timing information as described above from an external network or the like via, for example, the communication unit 19. Further, in step S15, the controller 15 may acquire the timing information as described above from a slot of an electronic device 10 into which a storage such as a memory card can be inserted. Further, when the system 1 includes a display capable of displaying an image, image data or the like may be displayed on the display. In this case, the timing information set by a staff member or the like while visually recognizing the display may be input via, for example, an operation unit of the electronic device 10.

After acquiring the timing information in step S15, the controller 15 machine-learns the relationship between the coordinates extracted from the image data and the start of the pica action based on the timing information (step S16). In step S16, the controller 15 learns the relationship between the coordinates of the predetermined portion (joint point) of the monitored target T between the start time and the end time of the pica action and the start of the pica action based on the timing information. It may be done. Here, the controller 15 may perform machine learning based on the image data (or coordinate data or normalized coordinate data) and the timing information set in the image data. Hereinafter, the data generated as a result of machine learning in step S16 may be referred to as "machine learning data".

By performing the machine learning as described above, the electronic device 10 has the coordinates of the predetermined portion (joint point) of the monitored target T between the start time of the pica behavior and the start time to the end time of the pica behavior. It is possible to grasp the relationship with movement. Therefore, according to the electronic device 10, in the estimation phase described later, the start time of pica can be estimated based on the movement of the coordinates of the predetermined portion (joint point) of the monitored target T.

As described above, in one embodiment, the controller 15 determines the relationship between the coordinates of the predetermined portion of the monitored target T between the start time and the end time of the pica behavior and the start of the pica behavior based on the timing information. Machine learning. Here, the timing information may be information indicating the start time point and the end time point of the pica action in the time-dependent image captured by the image pickup unit 20 as described above. Here, in the present disclosure, the number of relationships between the coordinates of the predetermined portion of the monitored target T between the start time point and the end time point of the pica behavior and the start of the pica behavior, which is machine-learned by the controller 15, is, for example, 1. The number may be at least 1 or more, such as 10 or more, 100 or more, and 1000 or more.

In order to improve the accuracy with which the electronic device 10 estimates the start time of pica in the estimation phase described later, machine learning may be performed on the data of a relatively large number of samples (for example, a human such as a monitored target T). .. By increasing the amount of sample data when performing machine learning, it can be expected that the accuracy when the electronic device 10 estimates the start time of pica behavior will be improved in the estimation phase described later. Therefore, by increasing the sample data when performing machine learning, it is possible to reduce the occurrence of false alarms or false alarms when the electronic device 10 estimates the start of pica and issues a warning. obtain. Here, in the present disclosure, the number of samples machine-learned by the controller 15 may be at least 1 or more, for example, 1 or more, 10 or more, 100 or more, 1000 or more, and the like.

In the above-mentioned machine learning, the timing information is information indicating the start time point and the end time point of the pica action in the time-dependent image captured by the image pickup unit 20. In one embodiment, the timing information may include information indicating a time point other than the start time point and the end time point of the pica in the time-dependent image captured by the image pickup unit 20.

For example, the image data of the section of the learning data (4) shown in FIG. 7 exists between the start time point and the end time point of the pica act. Therefore, the learning data (4) may be machine-learned by the controller 15 as data classified into a dangerous (that is, a high risk of pica) class. The image data of the section of the learning data (4) shown in FIG. 7 starts at substantially the same time as the start time of the pica. On the other hand, the image data in the section of the learning data (4) shown in FIG. 7 ends at a time different from the end time of the pica.

On the other hand, the learning data (1) to (3) and the image data in the section of the learning data (5) to (6) shown in FIG. 7 do not exist between the start time and the end time of the pica. Therefore, these training data may be machine-learned by the controller 15 as data classified into a normal (that is, low risk (low risk of pica)) class. The image data of the section of these learning data starts at a time different from the start time of the pica act and ends at a time different from the end time point of the pica act.

As described above, in one embodiment, the timing information may include information indicating a time point other than the start time point and the end time point of the pica action in the time-dependent image captured by the image pickup unit 20. Based on such timing information, the controller 15 may perform machine learning on the relationship between the coordinates of the predetermined portion of the monitored target T between the start time point and the end time point of the pica behavior and the start of the pica behavior.

As shown in FIG. 7, the controller 15 has not only the learning data (4) classified into the dangerous class, but also the learning data (1) to (3) and the learning data (5) classified into the normal class. Machine learning may be performed based on the learning data as described in (6) to (6). The controller 15 can improve the accuracy of estimating the start of pica by performing machine learning based on the learning data classified into the dangerous class and the learning data classified into the normal class.

FIG. 10 is a flowchart showing an example of the operation in the estimation phase of the system 1 according to the embodiment. FIG. 10 may be a flowchart focusing on the operation in the estimation phase of the electronic device 10 included in the system 1 according to the embodiment.

As mentioned above, in a long-term care facility, for example, a person with impaired cognitive function, such as the elderly, may be at risk of eating a pica. In the estimation phase of the system 1 according to the embodiment, the start of pica behavior is estimated from the position (coordinates) of each part of the body of the monitored target T by using the machine learning data obtained in the learning phase. good. By monitoring the monitored target T by the system 1, for example, a staff member of a nursing care facility can recognize the pica behavior of the monitored target T before the end of the pica behavior.

When the operation shown in FIG. 10 starts, the controller 15 of the electronic device 10 acquires machine learning data (step S21). The machine learning data acquired in step S21 may be data generated as a result of machine learning in step S16 shown in FIG. That is, the machine learning data may be data in which the relationship between the coordinates of a predetermined part of the human between the start time and the end time of the human eating behavior and the start of the human eating behavior is machine-learned.

If the machine learning data has already been acquired at the time when the operation shown in FIG. 10 starts, the controller 15 does not have to acquire the machine learning data again in step S21. Further, the machine learning data acquired in step S21 is used in step S26 described later. Therefore, in one embodiment, the acquisition of machine learning data does not necessarily have to be performed in step S21, and may be performed at any timing up to step 25.

When the machine learning data is acquired in step S21, the controller 15 acquires the image captured by the imaging unit 20 (step S22). The operation in step S22 may be performed in the same manner as the operation in step S11 shown in FIG.

Upon acquiring the image captured in step S22, the extraction unit 11 executes object recognition in the image (step S23). The operation in step S23 may be performed in the same manner as the operation in step S12 shown in FIG. Here, the operation in step S23 may be performed by the controller 15 instead of the extraction unit 11.

When the object to be grasped or to be grasped by the monitored object T is recognized as food (not a foreign substance) in step S23, it is determined that the object is not pica in the estimation of the start of pica (step S26) described later. good. That is, the estimation of the start of pica behavior (step S26), which will be described later, may be influenced by whether or not the object to be gripped by the monitored target T is recognized as a foreign substance in step S23.

Here, as another example, the processing after step S24 may be executed only when the object to be grasped or to be grasped by the monitored object T is recognized as not food (foreign matter) in step S23. That is, when the object to be gripped by the monitored object T is food, the pica does not occur, so that the operation shown in FIG. 10 may be terminated without executing the processing after step S24.

When the object recognition is executed in step S23, the extraction unit 11 extracts the coordinates of the predetermined portion of the body of the monitored target T (step S24). The operation in step S24 may be performed in the same manner as the operation in step S13 shown in FIG. That is, for example, the extraction unit 11 may two-dimensionally extract the coordinates of a predetermined number of joint points in the body of the monitored target T for each image of a predetermined number of frames per second captured by the image pickup unit 20. Here, the operation in step S24 may be performed by the controller 15 instead of the extraction unit 11.

After the coordinates of the predetermined portion are extracted in step S24, the extraction unit 11 sets the coordinates according to the maximum and minimum values of the coordinates (X, Y) in the extracted predetermined number of frames (for example, 15 frames per second). Normalize (step S25). The operation in step S25 may be performed in the same manner as the operation in step S14 shown in FIG. That is, for example, the extraction unit 11 normalizes each direction component of the coordinates of a predetermined number of joint points in the body of the monitored target T extracted two-dimensionally based on the maximum value and the minimum value of each direction component. It may be transformed into.

After the coordinates are normalized in step S25, the controller 15 determines whether or not the start of the eating behavior is estimated from the coordinates normalized in step S25 based on the machine learning data acquired in step S21. (Step S26).

When the start of pica behavior is presumed in step S26, that is, when the risk that pica behavior is about to start increases, the controller 15 outputs a predetermined warning signal (step S27). In step S27, the controller 15 may output a predetermined warning signal to the warning unit 17. As a result, the warning unit 17 can issue a predetermined warning.

On the other hand, if the start of pica behavior is not presumed in step S26, that is, if the risk of starting pica behavior is not increased, the controller 15 skips the operation of step S27 and ends the operation shown in FIG. It's okay. When the operation shown in FIG. 10 is completed, the controller 15 may start the operation shown in FIG. 10 again. For example, the controller 15 may repeat the operation shown in FIG. 10 every time the coordinates are extracted from the image data. That is, for example, when the extraction unit 11 extracts the coordinates (X, Y) from the image data of 15 frames per second, the controller 15 may estimate the start of pica in step S26 15 times per second.

As described above, in one embodiment, the controller 15 may estimate the start of pica from the coordinates of the predetermined portion of the monitored target T extracted by the extraction unit 11 based on the machine learning data. Further, the extraction unit 11 may extract the coordinates of a predetermined portion of the monitored target T from the images of a predetermined number of frames per unit time captured by the imaging unit 20. In this case, the controller 15 may estimate the start of pica from the coordinates of the predetermined portion of the monitored target T extracted by the extraction unit 11.

As shown in FIG. 10, the controller 15 may output a predetermined warning signal in step S27 immediately after estimating the start of pica in step S26. Therefore, the controller 15 may output a predetermined warning signal before the actual pica action is completed. As described above, the controller 15 may output a predetermined warning signal before the end of the pica after estimating the start of the pica. Further, if possible, the controller 15 may output a predetermined warning signal before the actual pica behavior starts. In this way, after estimating the start of the pica, the controller 15 may output a predetermined warning signal before the start of the pica.

According to the system 1 according to the embodiment, a human being, for example, a monitored target T, can machine-learn the relationship between the coordinates of the joint points of the body in the pica behavior and the timing of the pica behavior. Further, according to the system 1 according to the embodiment, it is possible to estimate the start of the pica act from the coordinates of the joint point of the body in which the monitored target T is the pica act, based on the result of machine learning. Therefore, according to the system 1 according to the embodiment, a predetermined warning is issued before the pica behavior of the monitored target T is completed, for example, before the monitored target T puts a foreign substance in the mouth. be able to. Therefore, according to the system 1 according to the embodiment, the staff of, for example, a long-term care facility can recognize the occurrence of pica. Therefore, according to the system 1 according to the embodiment, it is possible to avoid the ingestion of the foreign matter of the monitored target T and provide the monitored target T safely.

FIG. 11 is a diagram further explaining the estimation process described in step S26 of FIG. FIG. 11 shows the state of the monitored object T captured in the image data acquired in step S22 of FIG. 10, for example, in the left column. As shown in FIG. 11, in the captured image data, it is assumed that the monitored target T enters a room in which the imaging unit 20 is installed, sits in a chair, and then starts pica. From the moment when the pica act starts, the controller 15 shall perform the operation after step S22 shown in FIG. 10 as a process of 15 frames per second. That is, in the system 1, the image pickup unit 20 may capture an image of 15 frames per second. Further, in the system 1, the controller 15 may acquire an image of 15 frames per second. Further, in the system 1, the extraction unit 11 may extract the coordinates of the joint points of the body of the monitored target T from the image of 15 frames per second. Further, in the system 1, the controller 15 (or the extraction unit 11) may normalize the coordinates extracted from the image of 15 frames per second. Further, in the system 1, the controller 15 may estimate pica behavior from normalized coordinates of 15 frames per second based on machine learning data.

In the central column of FIG. 11, the controller 15 conceptually shows how to continuously acquire frames of image data. Here, the image data of each frame may be the coordinates extracted from the image data or may be the normalized coordinates. Further, in the central column of FIG. 11, the hatched image data represents a frame for one second after the start of pica.

In such a situation, the controller 15 may estimate the start of pica at that time based on a frame (frames 1 to 15) of 1 second after the start of pica (shown in FIG. 11). Estimate 1). Next, the controller 15 may estimate the start of pica at that time based on the frames from frame 2 to frame 16 (estimation 2 shown in FIG. 11). By repeating the above operation, the controller 15 estimates the start of pica behavior 15 times per second. Therefore, according to the system 1 according to the embodiment, even if the start of the pica behavior that should be originally estimated is not estimated in the estimation 1 and the estimation 2 shown in FIG. 11, for example, the estimation is performed 15 times per second. The risk of misreporting can be reduced.

In the above-described embodiment, for example, as shown in FIG. 4, the extraction unit 11 has described an example in which the extraction unit 11 extracts the coordinates of 13 sites as the joint points of the monitored target T. However, in one embodiment, the extraction unit 11 may extract the coordinates of more than 13 parts, or may extract the coordinates of less than 13 parts. Further, in the above-described embodiment, the system 1 has described an example of processing 15 frames per second. However, in one embodiment, the system 1 or each functional unit constituting the system 1 may process more than 15 frames per second and may process less than 15 frames per second. In one embodiment, the number of joint points handled by the system 1 and / or the number of frames processed may be adjusted so that the estimation of the onset of pica is a reasonable result.

Thus, in one embodiment, the controller 15 may determine at least one of the number of frames and the number of joint points so that the validity of the estimation of the initiation of pica is greater than or equal to a predetermined value.

In the above embodiment, since the captured image data is used, visible light is used as a detection target for monitoring. However, the present disclosure is not limited to such cases, and other detection targets such as arbitrary electromagnetic waves, sound waves, temperatures, and vibrations may be arbitrarily used.

Further, in the technique of the present disclosure, all the components of the electronic device 10 shown in FIG. 1 do not have to be present in one housing or server. For example, each part such as the controller 15 and the storage unit 13 which are the components of the electronic device 10 are connected to each other by a network consisting of wired, wireless or a combination thereof, and different housings, servers, devices, rooms, buildings, regions, countries. It may be arbitrarily arranged in such as.

Although the embodiments according to the present disclosure have been described based on the drawings and examples, it should be noted that those skilled in the art can easily make various modifications or modifications based on the present disclosure. It should be noted, therefore, that these modifications or modifications are within the scope of this disclosure. For example, the functions included in each component or each step can be rearranged so as not to be logically inconsistent, and a plurality of components or steps can be combined or divided into one. Is. Although the embodiment according to the present disclosure has been mainly described with respect to the apparatus, the embodiment according to the present disclosure can also be realized as a method including steps executed by each component of the apparatus. The embodiments according to the present disclosure can also be realized as a method, a program, or a storage medium on which a program is recorded, which is executed by a processor included in the apparatus. It should be understood that these are also included in the scope of this disclosure.

The above-described embodiment is not limited to the implementation as the system 1. For example, the above-described embodiment may be implemented as the electronic device 10 included in the system 1. Further, the above-described embodiment may be implemented as, for example, a monitoring method by the electronic device 10 or a control method of the electronic device 10. Further, the above-described embodiment may be implemented as a program executed by, for example, a device such as an electronic device 10 or an information processing device (for example, a computer).

1 System 10 Electronic equipment 11 Extraction unit 13 Storage unit 132 Machine learning data 15 Controller 17 Warning unit 19 Communication unit 20 Imaging unit

Claims (20)

An imaging unit that captures the monitored object and
An extraction unit that extracts the coordinates of a predetermined portion of the monitored object from the image captured by the imaging unit, and an extraction unit.
Based on the timing information indicating the start time point and the end time point of the pica behavior of the monitored object in the time-dependent image captured by the image pickup unit, the coordinates of the predetermined portion between the start time point and the end time point and the coordinates. , A controller that machine-learns the relationship with the start of pica behavior of the monitored object,
A system equipped with. The controller between the start time point and the end time point based on the timing information including the time point other than the start time point and the end time point of the pica behavior of the monitored object in the time-dependent image captured by the image pickup unit. The system according to claim 1, wherein the machine learns the relationship between the coordinates of the predetermined portion and the start of the pica act of the monitored object. The extraction unit two-dimensionally extracts the coordinates of a predetermined number of joint points in the body to be monitored for each image of a predetermined number of frames per second captured by the imaging unit, according to claim 1 or 2. Described system. The extraction unit normalizes each direction component of the coordinates of a predetermined number of joint points in the body to be monitored, which is two-dimensionally extracted, based on the maximum value and the minimum value of each direction component. The system according to any one of Items 1 to 3. In the timing information, the time point at which the pica behavior of the monitored object ends indicates the timing at which the monitored object holds the foreign substance held by the monitored object in the mouth in the time-dependent image captured by the imaging unit. The system according to any one of 1 to 4. In the timing information, any one of claims 1 to 5, wherein the start time point of the pica behavior of the monitored object indicates the timing when the monitored object grips the foreign substance in the temporal image captured by the imaging unit. The system described in. The system according to any one of claims 1 to 6, wherein the extraction unit recognizes an object included in an image over time captured by the imaging unit. An imaging unit that captures the monitored object and
An extraction unit that extracts the coordinates of a predetermined portion of the monitored object from the image captured by the imaging unit, and an extraction unit.
The relationship between the coordinates of the predetermined part of the human between the start time and the end time of the human pica and the start of the human pica is extracted by the extraction unit based on the machine learning data obtained by machine learning. A controller that estimates the start of pica behavior of the monitored object from the coordinates of the predetermined portion of the monitored object, and
A system equipped with. The extraction unit extracts the coordinates of the predetermined portion of the monitored object from the images of a predetermined number of frames per unit time captured by the imaging unit.
The system according to claim 8, wherein the controller estimates the start of pica behavior of the monitored object from the coordinates of a predetermined portion of the monitored object extracted by the extraction unit. The system according to claim 8 or 9, wherein the controller outputs a predetermined warning signal after estimating the start of the pica activity of the monitored object and before the start of the pica activity of the monitored object. The system according to claim 8 or 9, wherein the controller outputs a predetermined warning signal after estimating the start of the pica activity of the monitored object and before the end of the pica activity of the monitored object. The extraction unit two-dimensionally extracts the coordinates of a predetermined number of joint points in the body to be monitored for each image of a predetermined number of frames per second captured by the imaging unit, according to claims 8 to 11. The system described in either. The extraction unit normalizes each direction component of the coordinates of a predetermined number of joint points in the body to be monitored, which is two-dimensionally extracted, based on the maximum value and the minimum value of each direction component. Item 12. The system according to item 12. 13. System. An extraction unit that extracts the coordinates of a predetermined part of the monitored object from an image captured including the monitored object, and an extraction unit.
The predetermined portion between the start time point and the end time point based on the timing information indicating the start time point and the end time point of the pica behavior of the monitored object in the time-dependent image taken including the monitored object. A controller that machine-learns the relationship between the coordinates of the above and the start of the pica behavior of the monitored object.
Electronic equipment equipped with. An extraction unit that extracts the coordinates of a predetermined part of the monitored object from an image captured including the monitored object, and an extraction unit.
The relationship between the coordinates of the predetermined part of the human between the start time and the end time of the human pica and the start of the human pica is extracted by the extraction unit based on the machine learning data obtained by machine learning. A controller that estimates the start of pica behavior of the monitored object from the coordinates of the predetermined portion of the monitored object, and
Electronic equipment equipped with. An imaging step that captures the monitored object, and
An extraction step of extracting the coordinates of a predetermined portion of the monitored object from the image captured by the imaging step, and an extraction step.
Based on the timing information indicating the start time point and the end time point of the pica behavior of the monitored object in the time-dependent image captured by the imaging step, the coordinates of the predetermined portion between the start time point and the end time point and the coordinates. , The machine learning step of machine learning the relationship with the start of pica behavior of the monitored object,
How to control electronic devices, including. An imaging step that captures the monitored object, and
An extraction step of extracting the coordinates of a predetermined portion of the monitored object from the image captured by the imaging step, and an extraction step.
The relationship between the coordinates of the predetermined part of the human between the start time and the end time of the human pica and the start of the human pica is extracted by the extraction step based on the machine learning data obtained by machine learning. An estimation step for estimating the start of pica behavior of the monitored object from the coordinates of the predetermined portion of the monitored object, and an estimation step.
How to control electronic devices, including. On the computer
An imaging step that captures the monitored object, and
An extraction step of extracting the coordinates of a predetermined portion of the monitored object from the image captured by the imaging step, and an extraction step.
Based on the timing information indicating the start time point and the end time point of the pica behavior of the monitored object in the time-dependent image captured by the imaging step, the coordinates of the predetermined portion between the start time point and the end time point and the coordinates. , The machine learning step of machine learning the relationship with the start of pica behavior of the monitored object,
A program that runs. On the computer
An imaging step that captures the monitored object, and
An extraction step of extracting the coordinates of a predetermined portion of the monitored object from the image captured by the imaging step, and an extraction step.
The relationship between the coordinates of the predetermined part of the human between the start time and the end time of the human pica and the start of the human pica is extracted by the extraction step based on the machine learning data obtained by machine learning. An estimation step for estimating the start of pica behavior of the monitored object from the coordinates of the predetermined portion of the monitored object, and an estimation step.
A program that runs.

Images