CN113971869A - Method and device for monitoring home safety of old people - Google Patents
Method and device for monitoring home safety of old people Download PDFInfo
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Abstract
The application provides a method and a device for monitoring the home safety of the old, wherein the method comprises the following steps: acquiring at least two monitoring images separated by preset time and voice data in a preset time period, wherein the monitoring images comprise human body characteristics and/or facial expression characteristics; judging whether the monitoring object has an emergency or not according to the human body characteristics and/or the facial expression characteristics in the at least two monitoring images, and/or judging whether the monitoring object sends distress information or not according to voice data in a preset time period; and when the occurrence of an emergency of the monitored object is determined and/or the monitored object sends out distress call information, generating safety alarm information. The current behavior and the sent voice information of the monitored object are determined according to the monitoring image and the voice data in the preset time period, and when the monitored object is determined to have safety risk, an alarm is given to remind a guardian to take corresponding safety measures in time, so that the personal safety of the monitored object is guaranteed.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a method and a device for monitoring the home safety of old people.
Background
At present, due to the fact that the population aging trend is increasingly serious, the nursing problem of middle-low income families is extremely severe, and most of the old people select home nursing. In particular, the home safety of the elderly has been the focus of research to avoid the risk of sudden diseases and the like in the elderly at home.
In the prior art, a mobile terminal is usually worn on an old man to monitor body characteristics of the old man, such as blood pressure, blood sugar and heartbeat, so as to determine whether the old man has sudden diseases.
However, some old people are not used to wear similar mobile terminals, which results in incorrect wearing method of the mobile terminals, lower reliability of monitoring results and is not beneficial to ensuring the home safety of the old people. Therefore, there is an urgent need for a method for monitoring the home safety of the elderly people, which can monitor the home safety of the elderly people in real time, and has an important significance in ensuring the personal safety of the elderly people.
Disclosure of Invention
Therefore, the invention aims to overcome the defect of low reliability of the method for monitoring the home safety of the old people in the prior art, and provides a method and a device for monitoring the home safety of the old people.
The application provides in a first aspect a method for monitoring the home safety of the elderly, comprising: the method comprises the steps of obtaining at least two monitoring images separated by preset time and voice data in a preset time period, wherein the monitoring images comprise human body features and/or facial expression features;
judging whether the monitoring object has an emergency or not according to the human body characteristics and/or the facial expression characteristics in the at least two monitoring images, and/or judging whether the monitoring object sends distress information or not according to voice data in a preset time period;
and when the monitoring object is determined to have an emergency and/or the monitoring object sends out distress call information, generating safety alarm information.
Optionally, the determining whether the monitoring object has an emergency event according to the human body feature and/or the facial expression feature in the at least two monitoring images includes:
determining the current behavior and/or the current expression of the monitored object according to the human body features and/or the facial expression features in the at least two monitored images;
determining the similarity between the current behavior and/or the current expression of the monitored object and each emergency sample according to a preset emergency recognition model and the current behavior and/or the current expression of the monitored object;
and when the similarity between the current behavior and/or the current expression of the monitored object and any emergency sample is larger than a preset behavior threshold value, determining that the emergency happens to the monitored object.
Optionally, the determining, according to the voice data in the preset time period, whether the monitoring object sends the distress message includes:
determining the current voice information sent by the monitoring object according to the voice data in the preset time period;
judging whether the voice information belongs to a distress voice type or not according to a preset voice type recognition model and the voice information sent by the monitored object currently;
and when the voice information belongs to any distress calling voice type, determining that the monitoring object sends out distress calling information.
Optionally, the acquiring at least two monitoring images separated by a preset time includes:
acquiring a monitoring video in the preset time period;
and performing frame processing on the monitoring video according to a preset time interval to obtain at least two monitoring images separated by preset time.
Optionally, before determining whether the monitoring object has an emergency according to the human body features and/or facial expression features in the at least two monitoring images, and/or determining whether the monitoring object sends the distress message according to the voice data in a preset time period, the method further includes:
determining whether the at least two monitored images comprise human body features and/or facial expression features according to the at least two monitored images and a preset human body motion recognition model;
and when the fact that the at least two monitoring images comprise the human body features and/or the facial expression features is determined, the step of judging whether the monitoring object has an emergency or not according to the human body features and/or the facial expression features in the at least two monitoring images and/or judging whether the monitoring object sends out distress call information or not according to voice data in a preset time period is executed.
Optionally, the method further includes: and when the at least two monitored images are determined not to include the human body features and/or the facial expression features, returning to the step of acquiring the monitored video within the preset time period.
Optionally, the method further includes: when it is determined that the monitoring object does not have an emergency and does not send out distress information, determining a behavior class to which the current behavior of the monitoring object belongs according to a preset behavior class analysis model; wherein the behavior categories include sleeping behavior, recreational behavior, and daily behavior.
This application second aspect provides an old man safety monitoring device at home, includes: the device comprises an acquisition module, a judgment module and an alarm module;
the acquisition module is used for acquiring at least two monitoring images separated by preset time and voice data in a preset time period, wherein the monitoring images comprise human body characteristics and facial expression characteristics;
the judging module is used for judging whether the monitoring object has an emergency or not according to the human body characteristics and/or the facial expression characteristics in the at least two monitoring images and/or judging whether the monitoring object sends distress call information or not according to the voice data in a preset time period;
the alarm module is used for generating safety alarm information when the occurrence of an emergency of the monitoring object is determined and/or the monitoring object sends out distress call information.
Optionally, the determining module is specifically configured to: determining the current behavior and/or the current expression of the monitored object according to the human body features and/or the facial expression features in the at least two monitored images;
determining the similarity between the current behavior and/or the current expression of the monitored object and each emergency sample according to a preset emergency recognition model and the current behavior and/or the current expression of the monitored object;
and when the similarity between the current behavior and/or the current expression of the monitored object and any emergency sample is larger than a preset behavior threshold value, determining that the emergency happens to the monitored object.
Optionally, the determining module is specifically configured to: determining the current voice information sent by the monitoring object according to the voice data in the preset time period;
judging whether the voice information belongs to a distress voice type or not according to a preset voice type recognition model and the voice information sent by the monitored object currently;
and when the voice information belongs to any distress calling voice type, determining that the monitoring object sends out distress calling information.
Optionally, the obtaining module is specifically configured to: acquiring a monitoring video in the preset time period;
and performing frame processing on the monitoring video according to a preset time interval to obtain at least two monitoring images separated by preset time.
Optionally, the determining module is further configured to: determining whether the at least two monitored images comprise human body features and/or facial expression features according to the at least two monitored images and a preset human body motion recognition model;
and when the fact that the at least two monitoring images comprise the human body features and/or the facial expression features is determined, the step of judging whether the monitoring object has an emergency or not according to the human body features and/or the facial expression features in the at least two monitoring images and/or judging whether the monitoring object sends out distress call information or not according to voice data in a preset time period is executed.
Optionally, the determining module is further configured to: and when the at least two monitored images are determined not to include the human body features and/or the facial expression features, returning to the step of acquiring the monitored video within the preset time period.
Optionally, the determining module is further configured to: when it is determined that the monitoring object does not have an emergency and does not send out distress information, determining a behavior class to which the current behavior of the monitoring object belongs according to a preset behavior class analysis model; wherein the behavior categories include sleeping behavior, recreational behavior, and daily behavior.
A third aspect of the present application provides an electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored by the memory to cause the at least one processor to perform the method as set forth in the first aspect above and in various possible designs of the first aspect.
A fourth aspect of the present application provides a storage medium containing computer-executable instructions for performing a method as set forth in the first aspect above and in various possible designs of the first aspect when executed by a computer processor.
This application technical scheme has following advantage:
according to the method and the device for monitoring the home safety of the old people, at least two monitoring images separated by the preset time and voice data in the preset time period are obtained, wherein the monitoring images comprise human body characteristics and/or facial expression characteristics; judging whether the monitoring object has an emergency or not according to the human body characteristics and/or the facial expression characteristics in the at least two monitoring images, and/or judging whether the monitoring object sends distress information or not according to voice data in a preset time period; and when the occurrence of an emergency of the monitored object is determined and/or the monitored object sends out distress call information, generating safety alarm information. According to the method for monitoring the home safety of the old people, the current behavior and the sent voice information of the monitored object are determined according to the monitoring image and the voice data in the preset time period, and when the monitored object is determined to have safety risks according to the current behavior and the sent voice information, an alarm is given to remind a guardian of taking corresponding safety measures in time, so that the personal safety of the monitored object is guaranteed.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a schematic structural diagram of a home safety monitoring system for the elderly based on an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for monitoring home safety of an elderly person according to an embodiment of the present application;
fig. 3 is a schematic flow chart of another method for monitoring home safety of an elderly person according to an embodiment of the present application;
fig. 4 is a schematic flowchart of another method for monitoring home safety of an elderly person according to an embodiment of the present application;
fig. 5 is a schematic flowchart of another method for monitoring home safety of an elderly person according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an elderly home safety monitoring device provided in an embodiment of the present application;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the prior art, a mobile terminal is usually worn on an old man to monitor body characteristics of the old man, such as blood pressure, blood sugar and heartbeat, so as to determine whether the old man has sudden diseases. However, some old people are not used to wear similar mobile terminals, which results in incorrect wearing method of the mobile terminals, lower reliability of monitoring results and is not beneficial to ensuring the home safety of the old people.
In order to solve the problems, according to the method and the device for monitoring the home safety of the old people, at least two monitoring images separated by a preset time interval and voice data in a preset time period are obtained, wherein the monitoring images comprise human body features and/or facial expression features; judging whether the monitoring object has an emergency or not according to the human body characteristics and/or the facial expression characteristics in the at least two monitoring images, and/or judging whether the monitoring object sends distress information or not according to voice data in a preset time period; and when the occurrence of an emergency of the monitored object is determined and/or the monitored object sends out distress call information, generating safety alarm information. According to the method for monitoring the home safety of the old people, the current behavior and the sent voice information of the monitored object are determined according to the monitoring image and the voice data in the preset time period, and when the monitored object is determined to have safety risks according to the current behavior and the sent voice information, an alarm is given to remind a guardian of taking corresponding safety measures in time, so that the personal safety of the monitored object is guaranteed.
The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
First, the structure of the elderly home safety monitoring system based on this application is explained:
the method and the device for monitoring the home safety of the old people are suitable for monitoring the personal safety of the old people at home. As shown in fig. 1, the structural schematic diagram of the system for monitoring the home safety of the elderly based on the embodiment of the present application mainly includes at least one image acquisition device, at least one voice acquisition device, an electronic device for monitoring the home safety of the elderly, and a handheld mobile terminal for receiving alarm information. Specifically, the installation position of at least one image acquisition device and the installation position of at least one voice acquisition device are determined according to actual conditions, so that dead angles of monitoring and voice acquisition are avoided. The monitoring image is collected based on at least one image collecting device, the collected monitoring image is sent to the electronic device, correspondingly, voice data is collected based on at least one voice collecting device, and the collected voice data is sent to the electronic device. The electronic equipment determines whether the old people have emergencies or not according to the acquired monitoring images and the voice data, judges whether the old people send distress messages or not, generates safety alarm information when determining that the old people have safety risks, and sends the alarm information to the handheld mobile terminal. The guardian (such as a child of the old) can take corresponding safety measures according to the alarm information received by the handheld mobile terminal so as to avoid the danger of the old.
The embodiment of the application provides a method for monitoring the home safety of the old people, which is used for solving the technical problem that the reliability of the method for monitoring the home safety of the old people in the prior art is lower. The execution main body of the embodiment of the application is an electronic device, such as a server, a desktop computer, a notebook computer, a tablet computer and other electronic devices which can be used for monitoring the home safety of the old.
As shown in fig. 2, a schematic flow chart of a method for monitoring home safety of an elderly person provided in an embodiment of the present application is shown, where the method includes:
Wherein the monitored image comprises human body features and/or facial expression features.
It should be explained that the human body feature represents the limb movement condition of the monitored object, and the facial expression feature represents the facial expression change condition of the monitored object.
Specifically, in an embodiment, in order to improve the accuracy of the emergency monitoring result and improve the monitoring efficiency, the current behavior and/or the current expression of the monitored object may be determined according to the human body features and/or the facial expression features in at least two monitored images; determining the similarity between the current behavior and/or the current expression of the monitored object and each emergency sample according to a preset emergency recognition model and the current behavior and/or the current expression of the monitored object; and when the similarity between the current behavior and/or the current expression of the monitored object and any emergency sample is larger than a preset behavior threshold value, determining that the emergency happens to the monitored object.
It should be explained that the preset emergency recognition model may be established according to a large number of emergency sample images, and meanwhile, each emergency sample image is labeled according to the emergency type corresponding to each emergency sample image, where the emergency type mainly includes tumble, twitch, seizure and the like.
The facial expression of the monitored object is usually a painful expression when an emergency happens, and for part of emergency diseases, when the disease attacks occur, the limb changes of the monitored object are probably not large, such as body stiffness occurs, but the facial expression obviously shows that the monitored object is currently in the state of the disease attack.
Specifically, the current behavior and/or the current expression of the monitored object are determined according to human body features and/or facial expression features in at least two monitored images separated by preset time, namely according to the current limb movement condition and facial expression change condition of the monitored object. Further, at least two monitoring images separated by preset time are sequentially input into a preset emergency recognition model, and the similarity between the current behavior and/or the current expression of the monitoring object and each preset emergency sample image is determined based on the preset emergency recognition model. And when the similarity between the current behavior and/or the current expression of the monitoring object and any emergency sample is larger than a preset behavior threshold value, determining that the monitoring object has an emergency, and simultaneously acquiring a label (marked emergency type) of the emergency sample and outputting the label. The preset behavior threshold value may be set according to an actual situation, and the embodiment of the present application is not limited.
For example, if the preset behavior threshold is 0.8, when it is determined that the similarity between the current behavior and/or the current expression of the monitored subject and the fall in the emergency sample is 0.2, the similarity between the current behavior and/or the current expression of the monitored subject and the twitch in the emergency sample is 0.15, and the similarity between the current behavior and the current expression of the monitored subject and the seizure in the emergency sample is 0.85 according to the preset emergency recognition model, it may be determined that the monitored subject has an emergency and the emergency is a seizure.
Specifically, in an embodiment, in order to improve the accuracy and reliability of the voice monitoring result and improve the monitoring efficiency, the voice information currently sent by the monitoring object may be determined according to the voice data in the preset time period; judging whether the voice information belongs to the type of the distress call voice or not according to a preset voice type recognition model and the voice information sent by the monitored object currently; and when the voice information belongs to any distress calling voice type, determining that the monitoring object sends out distress calling information.
It should be explained that the preset speech type recognition model can be established according to a large number of speech samples, and meanwhile, each speech sample is marked according to the speech type corresponding to each speech sample, and the speech type mainly includes a call for help speech type, a daily speech type and an entertainment speech type. The calling-for-help voice types mainly comprise groaning, screaming, emergency calling and the like, the daily voice types mainly comprise daily communication, reciting, laughing and the like, and the entertainment voice types mainly comprise singing, playing and the like.
Furthermore, the generated safety alarm information can be sent to the handheld mobile terminal in a short message mode to remind a guardian to take corresponding safety measures in time, so that the personal safety of the monitored object is guaranteed. The safety alarm information can be reported in the modes of voice, light, mails and the like, and when the voice information of the monitored object is an emergency call, a hospital emergency call can be automatically dialed, so that the monitored object can obtain medical diagnosis and treatment in time, and the home safety of the old is further guaranteed.
On the basis of the foregoing embodiment, in order to improve the real-time performance of the method for monitoring the home safety of the elderly people provided in the embodiment of the present application and improve the reliability of the monitoring result thereof, as shown in fig. 3, which is a schematic flow chart of another method for monitoring the home safety of the elderly people provided in the embodiment of the present application, as an implementable manner, on the basis of the foregoing embodiment, in an embodiment, the obtaining at least two monitoring images separated by a preset time includes:
Specifically, at least one image acquisition device sends acquired monitoring videos to the electronic device in real time, so that the electronic device can acquire the monitoring videos in a preset time period in real time. And performing frame processing on the monitoring video to obtain at least two monitoring images with preset time intervals so as to enable the electronic equipment to monitor the safety of the monitored object in real time.
For example, if the preset time period is set to 9:00-9:01, where the preset time interval is 1 second, 60 monitoring images may be obtained through framing processing within the preset time period, and the current behavior and/or the current expression of the monitored object may be analyzed according to the obtained 60 monitoring images.
Wherein, in order to improve the quality of the obtained monitoring video and save the development cost, the image acquisition equipment can adopt a camera with an annular infrared illumination function. It is provided with cloud platform device, can rotate by horizontal direction, vertical direction, has increased monitoring range to it is provided with storage device, can save the monitoring video who acquires, provides complete monitoring video for the guardian.
On the basis of the embodiment, because the monitoring image that at least one image acquisition equipment gathered has the environment monitoring image, does not include the monitoring object in some monitoring images promptly, if electronic equipment all carries out the security monitoring to all monitoring images, then will waste a large amount of system resources, be unfavorable for guaranteeing the monitoring efficiency of the old man safety monitoring method at home that this application embodiment provided.
Therefore, in order to solve the above problems, as shown in fig. 4, which is a schematic flow chart of another method for monitoring the home safety of an elderly person provided in the embodiment of the present application, as an implementable manner, on the basis of the foregoing embodiment, in an embodiment, before determining whether an emergency occurs in a monitored subject according to human body features and/or facial expression features in at least two monitored images, and/or determining whether the monitored subject sends a distress message according to voice data within a preset time period (step 202), the method further includes:
When it is determined that the at least two monitored images include the human body feature and/or the facial expression feature, a step of judging whether the monitored object has an emergency or not according to the human body feature and/or the facial expression feature in the at least two monitored images and/or judging whether the monitored object sends distress call information or not according to voice data in a preset time period is performed (step 202).
Accordingly, when it is determined that the human body feature and/or the facial expression feature are not included in the at least two monitored images, the step of acquiring the monitored video within the preset time period is returned (step 2011).
It should be explained that the preset human motion recognition model can be established according to a large number of human motion sample images. Specifically, by extracting each rotational degree of freedom in the monitoring image, the joint motion characteristics of the monitoring object are determined according to the extracted rotational degree of freedom, and then the current joint motion state is determined. Similarly, by extracting the features of the five sense organs in the monitored image, whether the current facial expression changes is judged according to the deformation condition of the extracted features of the five sense organs.
For example, if the preset human body characteristics corresponding to the monitored object in each monitoring image include seven rotational degrees of freedom, which are the rotational degree of freedom of the neck joint, the rotational degree of freedom of the left shoulder joint, the rotational degree of freedom of the right shoulder joint, the rotational degree of freedom of the left elbow joint, the rotational degree of freedom of the right elbow joint, the rotational degree of freedom of the left knee joint, and the rotational degree of freedom of the right knee joint. The preset human body joint motion recognition model can extract human body features in at least two monitoring images and determine the rotational freedom degree of each joint of a monitoring object. For example, according to the rotational degree of freedom of the right shoulder joint and the rotational degree of freedom of the right elbow joint, it can be determined that the right arm of the monitoring object is doing corresponding movement. I.e. to determine whether a human feature is included in the acquired monitored image. Similarly, whether the current facial expression of the facial features is changed or not can be judged according to the deformation condition of the extracted facial features, namely whether the facial expression features are included in the acquired monitored image or not is determined.
On the basis of the foregoing embodiment, in order to further improve the practicability and universality of the method for monitoring the home safety of the elderly people provided in the embodiment of the present application, so that the method can be applied to more scenes, as shown in fig. 5, which is a schematic flow chart of another method for monitoring the home safety of the elderly people provided in the embodiment of the present application, as an implementable manner, on the basis of the foregoing embodiment, in an embodiment, the method further includes:
The behavior categories include sleeping behavior, recreational behavior, and daily behavior, among others.
It should be explained that the preset behavior type analysis model may be established according to a large number of behavior sample images, and meanwhile, each behavior sample image is labeled according to the corresponding behavior type. The behavior category may further include other behaviors, such as medical behaviors and athletic behaviors, which may be specifically set according to an application scenario, and the embodiment of the present application is not limited.
Furthermore, after the behavior category to which the current behavior of the monitored object belongs is determined, the behavior category of the current behavior of the monitored object can be sent to the handheld mobile terminal according to a preset period, so that a guardian can supervise the physical maintenance of the old, meanwhile, the recent living habit of the old can be determined according to the received behavior category monitoring result, and further, the psychological condition of the old can be judged according to the recent living habit of the old. For example, when the elderly do not perform medical treatment for a long time, such as checking blood sugar and blood pressure, taking medicine, etc., the guardian can remind the elderly to perform corresponding medical treatment in time. When the old man does not carry out the recreational activities for a long time, then can judge that the old man is good in recent mood, then guardian can concern and accompany some to the old man to guarantee the mental health of old man.
The emergency recognition model, the voice category recognition model, the human motion recognition model and the behavior category analysis model provided by the embodiment of the application can be established based on a convolutional neural network, and can be established by adopting other deep learning algorithms, and the embodiment of the application is not limited.
According to the method for monitoring the home safety of the old people, at least two monitoring images separated by the preset time and voice data in the preset time period are obtained, wherein the monitoring images comprise human body features and/or facial expression features; judging whether the monitoring object has an emergency or not according to the human body characteristics and/or the facial expression characteristics in the at least two monitoring images, and/or judging whether the monitoring object sends distress information or not according to voice data in a preset time period; and when the occurrence of an emergency of the monitored object is determined and/or the monitored object sends out distress call information, generating safety alarm information. According to the method for monitoring the home safety of the old people, the current behavior and the sent voice information of the monitored object are determined according to the monitoring image and the voice data in the preset time period, and when the monitored object is determined to have safety risks according to the current behavior and the sent voice information, an alarm is given to remind a guardian of taking corresponding safety measures in time, so that the personal safety of the monitored object is guaranteed.
The embodiment of the application provides an old man safety monitoring device at home for solving the technical problem that the reliability of an old man safety monitoring method at home in the prior art is low. As shown in fig. 6, for the structural schematic diagram of the device for monitoring the home safety of the elderly people provided in the embodiment of the present application, the device 60 includes: an acquisition module 601, a judgment module 602 and an alarm module 603;
the acquisition module 601 is configured to acquire at least two monitoring images separated by a preset time and voice data in a preset time period, where the monitoring images include human body features and facial expression features; the judging module 602 is configured to judge whether an emergency occurs to the monitored object according to human body features and/or facial expression features in at least two monitored images, and/or judge whether the monitored object sends a distress message according to voice data in a preset time period; the alarm module 603 is configured to generate safety alarm information when it is determined that an emergency occurs in the monitored object and/or the monitored object sends a distress message.
Optionally, the determining module 602 is specifically configured to: determining the current behavior and/or the current expression of the monitored object according to the human body features and/or the facial expression features in the at least two monitored images;
determining the similarity between the current behavior and/or the current expression of the monitored object and each emergency sample according to a preset emergency recognition model and the current behavior and/or the current expression of the monitored object;
and when the similarity between the current behavior and/or the current expression of the monitored object and any emergency sample is larger than a preset behavior threshold value, determining that the emergency happens to the monitored object.
Optionally, the determining module 602 is specifically configured to: determining the current voice information sent by the monitoring object according to the voice data in the preset time period;
judging whether the voice information belongs to the type of the distress call voice or not according to a preset voice type recognition model and the voice information sent by the monitored object currently;
and when the voice information belongs to any distress calling voice type, determining that the monitoring object sends out distress calling information.
Optionally, the obtaining module 601 is specifically configured to: acquiring a monitoring video within a preset time period;
and performing frame processing on the monitoring video according to a preset time interval to obtain at least two monitoring images separated by preset time.
Optionally, the determining module 602 is further configured to: determining whether the at least two monitored images comprise human body features and/or facial expression features according to the at least two monitored images and a preset human body motion recognition model;
when the fact that the human body features and/or the facial expression features are included in the at least two monitoring images is determined, whether the monitoring object has an emergency or not is judged according to the human body features and/or the facial expression features in the at least two monitoring images, and/or whether the monitoring object sends distress call information or not is judged according to voice data in a preset time period.
Optionally, the determining module 602 is further configured to: and when the human body features and/or the facial expression features are determined not to be included in the at least two monitored images, returning to the step of acquiring the monitored video within the preset time period.
Optionally, the determining module 602 is further configured to: when it is determined that the monitoring object does not have an emergency and does not send out distress information, determining a behavior class to which the current behavior of the monitoring object belongs according to a preset behavior class analysis model; the behavior categories include sleeping behavior, recreational behavior, and daily behavior, among others.
The embodiment of the application provides a safety monitoring device at home for old man for carry out the safety monitoring method at home of old man that above-mentioned embodiment provided, its implementation is the same with the principle, no longer gives unnecessary details.
The embodiment of the application also provides electronic equipment which is used for executing the method provided by the embodiment.
Fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 70 includes: at least one processor 71 and memory 72;
wherein execution of the memory-stored computer-executable instructions by the at least one processor causes the at least one processor to perform the instructions of the method as in any one of the preceding embodiments.
The electronic device provided by the embodiment of the application is used for executing the method for monitoring the home safety of the old people provided by the embodiment, the implementation mode is the same as the principle, and the description is omitted.
The embodiment of the present application provides a storage medium containing computer executable instructions, where the storage medium stores computer processor execution instructions, and when the processor executes the computer execution instructions, the method provided in any one of the above embodiments is implemented.
The storage medium containing the computer executable instructions according to the embodiment of the present application may be used to store the computer executable instructions of the method for monitoring the home safety of the elderly people provided in the foregoing embodiments, and the implementation manner and the principle thereof are the same and are not described in detail.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A home safety monitoring method for the old is characterized by comprising the following steps:
the method comprises the steps of obtaining at least two monitoring images separated by preset time and voice data in a preset time period, wherein the monitoring images comprise human body features and/or facial expression features;
judging whether the monitoring object has an emergency or not according to the human body characteristics and/or the facial expression characteristics in the at least two monitoring images, and/or judging whether the monitoring object sends distress information or not according to voice data in a preset time period;
and when the monitoring object is determined to have an emergency and/or the monitoring object sends out distress call information, generating safety alarm information.
2. The elderly home safety monitoring method according to claim 1, wherein the determining whether an emergency occurs in the monitored subject according to the human body feature and/or the facial expression feature in the at least two monitored images comprises:
determining the current behavior and/or the current expression of the monitored object according to the human body features and/or the facial expression features in the at least two monitored images;
determining the similarity between the current behavior and/or the current expression of the monitored object and each emergency sample according to a preset emergency recognition model and the current behavior and/or the current expression of the monitored object;
and when the similarity between the current behavior and/or the current expression of the monitored object and any emergency sample is larger than a preset behavior threshold value, determining that the emergency happens to the monitored object.
3. The method for monitoring the home safety of the elderly people according to claim 1, wherein the step of judging whether the monitoring object sends out the distress message according to the voice data in the preset time period comprises the following steps:
determining the current voice information sent by the monitoring object according to the voice data in the preset time period;
judging whether the voice information belongs to a distress voice type or not according to a preset voice type recognition model and the voice information sent by the monitored object currently;
and when the voice information belongs to any distress calling voice type, determining that the monitoring object sends out distress calling information.
4. The elderly home safety monitoring method according to claim 1, wherein the acquiring at least two monitoring images separated by a preset time comprises:
acquiring a monitoring video in the preset time period;
and performing frame processing on the monitoring video according to a preset time interval to obtain at least two monitoring images separated by preset time.
5. The elderly home safety monitoring method according to claim 4, wherein before determining whether an emergency occurs in the monitored subject according to the human body features and/or facial expression features in the at least two monitoring images, and/or determining whether the monitored subject sends a distress message according to the voice data in a preset time period, the method further comprises:
determining whether the at least two monitored images comprise human body features and/or facial expression features according to the at least two monitored images and a preset human body motion recognition model;
and when the fact that the at least two monitoring images comprise the human body features and/or the facial expression features is determined, the step of judging whether the monitoring object has an emergency or not according to the human body features and/or the facial expression features in the at least two monitoring images and/or judging whether the monitoring object sends out distress call information or not according to voice data in a preset time period is executed.
6. The elderly home safety monitoring method of claim 5, further comprising:
and when the at least two monitored images are determined not to include the human body features and/or the facial expression features, returning to the step of acquiring the monitored video within the preset time period.
7. The elderly home safety monitoring method of claim 1, further comprising:
when it is determined that the monitoring object does not have an emergency and does not send out distress information, determining a behavior class to which the current behavior of the monitoring object belongs according to a preset behavior class analysis model; wherein the behavior categories include sleeping behavior, recreational behavior, and daily behavior.
8. The utility model provides an old man safety monitoring device at home which characterized in that includes: the device comprises an acquisition module, a judgment module and an alarm module;
the acquisition module is used for acquiring at least two monitoring images separated by preset time and voice data in a preset time period, wherein the monitoring images comprise human body characteristics and facial expression characteristics;
the judging module is used for judging whether the monitoring object has an emergency or not according to the human body characteristics and/or the facial expression characteristics in the at least two monitoring images and/or judging whether the monitoring object sends distress call information or not according to the voice data in a preset time period;
the alarm module is used for generating safety alarm information when the occurrence of an emergency of the monitoring object is determined and/or the monitoring object sends out distress call information.
9. An electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method of any one of claims 1-7.
10. A storage medium containing computer-executable instructions for performing the method of any one of claims 1-7 when executed by a computer processor.
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