CN110659558A - Automatic drip alarm device based on image recognition and implementation method thereof - Google Patents
Automatic drip alarm device based on image recognition and implementation method thereof Download PDFInfo
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- CN110659558A CN110659558A CN201910711988.5A CN201910711988A CN110659558A CN 110659558 A CN110659558 A CN 110659558A CN 201910711988 A CN201910711988 A CN 201910711988A CN 110659558 A CN110659558 A CN 110659558A
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000005070 sampling Methods 0.000 claims abstract description 38
- 238000001802 infusion Methods 0.000 claims abstract description 29
- 230000005540 biological transmission Effects 0.000 claims abstract description 21
- 238000004891 communication Methods 0.000 claims abstract description 12
- 239000007788 liquid Substances 0.000 claims description 17
- 238000012545 processing Methods 0.000 claims description 6
- 238000013527 convolutional neural network Methods 0.000 claims description 4
- 239000000284 extract Substances 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims 1
- 238000011084 recovery Methods 0.000 claims 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
- G16H20/17—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients delivered via infusion or injection
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- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
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- Infusion, Injection, And Reservoir Apparatuses (AREA)
Abstract
The invention discloses an automatic drip alarm device based on image recognition and an implementation method thereof. The invention comprises an infusion end, a control terminal and a console. The infusion end comprises a sample image, sampling equipment and wireless transmission equipment; the sample image and the sampling device are respectively arranged at two sides of the transfusion drip tube and are symmetrically arranged; the control terminal is provided with a database, a matching module and a wireless communication module; the control terminal matches the received image with a sample image in a database through a matching module, and if the matching is successful, a message is sent to a console through a wireless communication module; the control console is provided with a plurality of medical staff and can process in time according to the information received by the control console. The invention improves the accuracy of information, provides accurate and timely operation suggestions for medical personnel and ensures the order stability in the drip chamber.
Description
Technical Field
The invention provides an automatic drip alarm device based on image recognition and an implementation method thereof.
Background
As time is less and less, many people hope to use the time to the maximum within a limited time, for example, in the process of infusion, many people are willing to sleep and rest for spending, but in order to prevent the infusion from being finished but not replaced in time, people are difficult to fully use the time. In order to solve the increasingly serious doctor and patient problems, the invention aiming at various problems in hospitals is developed as required.
In the prior infusion process, a patient needs to stare at the infusion bottle consciously, and the infusion bottle needs to be replaced by the patient or not, and then the patient walks to a nurse station for replacement. The whole process is not only for patients but also for nurses, and unnecessary troubles in time are caused by manual errors and the like. With the social trend of digitalization, convenience and rapid development of technologies such as image recognition and wireless transmission, a certain innovation should be made in the aspect of replacement process.
Considering that a person who is subjected to infusion is possibly in a tired and weak state, possibly in a sleeping state and the like in the process of infusion, and the infusion condition cannot be reflected to a nurse station in time; the ward is too much, and the manpower resources can not be satisfied. The invention solves the problem, and can communicate and process in time to process the drip status. The device can reasonably deal with the situations of pipe blockage, no liquid in the dropping bottle and the like.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in order to solve the problems of untimely interaction and untimely processing of drip condition information between nurses and patients, an automatic drip alarm device based on image recognition and an implementation method thereof are provided.
The technical scheme adopted by the invention for solving the technical problems is as follows: the automatic drip alarm device based on image recognition comprises a transfusion end, a control terminal and a console.
The infusion end comprises a sample image, sampling equipment and wireless transmission equipment; the sample image and the sampling device are respectively arranged at two sides of the transfusion drip tube and are symmetrically arranged; the sampling equipment is a miniature camera with a storage function and is used for sampling a sample image on the other side of the infusion drip tube at fixed time; the wireless transmission equipment sends the sampled image to the control terminal;
the control terminal is provided with a database, a matching module and a wireless communication module; the control terminal extracts feature point information from the received image, matches the feature point information prestored in the sample image in the database through the matching module, and sends a message to the console through the wireless communication module if the matching is successful;
the control console is provided with a plurality of medical staff and can process in time according to the information received by the control console.
When the wireless transmission equipment sends the sampled image to the control terminal, the wireless transmission equipment also sends the unique serial number ID of the current wireless transmission equipment together, so that medical personnel in a subsequent control console can conveniently and directly check the serial number ID;
according to the actual requirement, each transfusion seat is provided with wireless transmission equipment with a unique serial number ID at the transfusion end; thereby facilitating the direct viewing of medical staff at the subsequent control console;
the control terminal firstly carries out SLAM-based restoration processing on the received image, then matches the restored result image with the sample image in the database, compares the matching result with a preset result, and sends a comparison result message to the console through the wireless communication module if the matching result is within the range of a set notification threshold;
a plurality of sample images are stored in the database;
an implementation method of automatic drip alarm equipment based on image recognition comprises the following steps:
step 1: the method comprises the following steps of setting a sample image capable of being accurately sensed at a transfusion end, wherein the sample image is preset and has detailed information, and the method comprises the following specific steps:
1-1, ambient brightness information to which a sample image is adapted;
1-2, feature point information which can be extracted under a set condition;
1-3, using the set detailed information as state information for generating alarm;
step 2: sampling a sample image by a miniature camera at the infusion end, and the specific process is as follows:
2-1, setting a sampling period, wherein the sampling period time is less than the time of the liquid flowing through the miniature camera;
2-2, adjusting the brightness in real time according to the change of the external environment;
and 2-3, sampling the sample image arranged on the corresponding side of the infusion drip tube by using the adapted brightness and the set sampling period to construct a picture set.
And step 3: judgment of drip state
And 3-1, receiving the picture from the infusion end by the control terminal, and carrying out graying and convolutional neural network processing on the received picture to restore the obtained picture.
Thereby achieving a real-time feedback state;
3-2, comparing the characteristic point information of the recovered real-time sampling picture with the characteristic point information of the sample image actually prestored in the control terminal database, and judging whether the liquid in the pipe is in a normal state or not according to the matching degree of the characteristic point information: if the matching degree result is in the set range and the characteristics of a certain same characteristic point of the front and back continuous N sampling pictures are consistent, directly sending a corresponding instruction to the console; otherwise, continuing to detect;
the judgment of the front and back continuous N sampling pictures is used for detecting the accuracy of judgment information, and the accuracy is possibly influenced because a plurality of bubbles or human errors exist in the drip.
The invention has the beneficial effects that:
the invention feeds back the external information in real time aiming at different external environments and improves the accuracy of the information. In the method, the camera sampling under the influence of external factors such as liquid shielding in the tube is analyzed, so that accurate and timely operation suggestions are provided for medical personnel, and the order stability in a drip chamber is ensured.
To above-mentioned actual conditions, it is different according to the liquid adulterant in the drip tube to propose one kind, draw the characteristic point of liquid, after liquid does not pass through the place that detects, send out the signal to nurse's platform through wireless transmission automatically, then in time handled by the nurse. The method can safely and efficiently maintain the order in the hospital and reduce the working pressure of medical workers. The traditional analysis method is mostly used for detecting liquid in a bottle, so that the technical content and the cost of the product are increased, and the feasibility is low. Or judging whether the drip is normal by using infrared rays to judge the drip speed. Considering the effect of the liquid droplet on refraction of infrared rays, etc., and when the liquid droplet is a colored liquid, the reliability will be greatly reduced.
Drawings
FIG. 1 is a schematic view of the present invention.
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings and examples.
The invention provides a drip alarm based on image recognition, which detects an image passing through liquid in a calculation mode of a convolutional neural network through the incompleteness of the image under the conditions that light is transmitted in the liquid and blocked and the like, calculates the difference value of the two images through comparison with a set value, and sends an instruction to a console to realize wireless communication before doctors and patients when only deviation caused by a plastic pipe exists.
The automatic drip alarm device based on image recognition comprises a transfusion end, a control terminal and a console.
As shown in fig. 1, the infusion end comprises a sample image 1, a sampling device 2 and a wireless transmission device 3; the sample image 1 and the sampling device 2 are respectively arranged at two sides of the transfusion drip tube and are symmetrically arranged; the sampling device 2 is a micro camera with a storage function and is used for sampling the sample image 1 on the other side of the infusion drip tube at fixed time; the wireless transmission equipment sends the sampled image to the control terminal;
the control terminal is provided with a database, a matching module and a wireless communication module; the control terminal extracts feature point information from the received image, matches the feature point information prestored in the sample image in the database through the matching module, and sends a message to the console through the wireless communication module if the matching is successful;
the control console is provided with a plurality of medical staff and can process in time according to the information received by the control console.
When the wireless transmission equipment sends the sampled image to the control terminal, the wireless transmission equipment also sends the unique serial number ID of the current wireless transmission equipment together, so that medical personnel in a subsequent control console can conveniently and directly check the serial number ID;
according to the actual requirement, each transfusion seat is provided with wireless transmission equipment with a unique serial number ID at the transfusion end; thereby facilitating the direct viewing of medical staff at the subsequent control console;
the control terminal firstly carries out SLAM-based restoration processing on the received image, then matches the restored result image with the sample image in the database, compares the matching result with a preset result, and sends a comparison result message to the console through the wireless communication module if the matching result is within the range of a set notification threshold;
an implementation method of automatic drip alarm equipment based on image recognition comprises the following steps:
step 1: the method comprises the following steps of setting a sample image capable of being accurately sensed at a transfusion end, wherein the sample image is preset and has detailed information, and the method comprises the following specific steps:
1-1, ambient brightness information to which a sample image is adapted;
1-2, feature point information which can be extracted under a set condition;
1-3, using the set detailed information as state information for generating alarm;
step 2: sampling a sample image by a miniature camera at the infusion end, and the specific process is as follows:
2-1, setting a sampling period, wherein the sampling period time is less than the time of the liquid flowing through the miniature camera;
2-2, adjusting the brightness in real time according to the change of the external environment;
and 2-3, sampling the sample image arranged on the corresponding side of the infusion drip tube by using the adapted brightness and the set sampling period to construct a picture set.
And step 3: judgment of drip state
And 3-1, receiving the picture from the infusion end by the control terminal, and carrying out graying and convolutional neural network processing on the received picture to restore the obtained picture.
Thereby achieving a real-time feedback state;
3-2, comparing the characteristic point information of the recovered real-time sampling picture with the characteristic point information of the sample image actually prestored in the control terminal database, and judging whether the liquid in the pipe is in a normal state or not according to the matching degree of the characteristic point information: if the matching degree result is in the set range and the characteristics of a certain same characteristic point of the front and back continuous N sampling pictures are consistent, directly sending a corresponding instruction to the console; otherwise, continuing to detect;
the judgment of the front and back continuous N sampling pictures is used for detecting the accuracy of judgment information, and the accuracy is possibly influenced because a plurality of bubbles or human errors exist in the drip.
Claims (8)
1. The automatic drip alarm equipment based on image recognition is characterized by comprising an infusion end, a control terminal and a console;
the infusion end comprises a sample image, sampling equipment and wireless transmission equipment; the sample image and the sampling device are respectively arranged at two sides of the transfusion drip tube and are symmetrically arranged; the sampling equipment is a miniature camera with a storage function and is used for sampling a sample image on the other side of the infusion drip tube at fixed time; the wireless transmission equipment sends the sampled image to the control terminal;
the control terminal is provided with a database, a matching module and a wireless communication module; the control terminal extracts feature point information from the received image, matches the feature point information prestored in the sample image in the database through the matching module, and sends a message to the console through the wireless communication module if the matching is successful;
the control console is provided with a plurality of medical staff and can process in time according to the information received by the control console.
2. The automatic drip alarm device based on image recognition according to claim 1, wherein when the wireless transmission device sends the sampled image to the control terminal, the wireless transmission device also sends a unique serial number ID where the current wireless transmission device is located, so that the subsequent console medical staff can conveniently and directly view the serial number ID.
3. An image recognition-based drip autoalarm as claimed in claim 2, wherein the infusion end is provided with a wireless transmission device with a unique serial number ID for each infusion seat according to actual requirements; thereby facilitating direct viewing by medical personnel on the subsequent console.
4. The automatic drip alarm device based on image recognition as claimed in claim 1, 2 or 3, wherein the control terminal performs a SLAM-based recovery process on the received image, matches the recovered result image with the sample image in the database, compares the matching result with a preset result, and sends a comparison result message to the console through the wireless communication module if the comparison result is within a set notification threshold range.
5. The method of claim 4 for implementing an image recognition-based drip automatic alarm device, comprising the steps of:
step 1: setting a sample image which can be accurately sensed at the infusion end, wherein the sample image is preset and has detailed characteristic point information;
step 2: sampling a sample image by a miniature camera at the infusion end;
and step 3: and the control terminal judges the state of the drip to obtain a result.
6. The method for implementing an automatic drip alarm device based on image recognition according to claim 5, wherein the step 1 is implemented as follows:
the characteristic point information comprises:
ambient brightness information to which the sample image is adapted;
the characteristic points can be extracted under the set conditions.
7. The method for implementing an image recognition-based drip automatic alarm device according to claim 6, wherein the step 2 is implemented as follows:
2-1, setting a sampling period, wherein the sampling period time is less than the time of the liquid flowing through the miniature camera;
2-2, adjusting the brightness in real time according to the change of the external environment;
and 2-3, sampling the sample image arranged on the corresponding side of the infusion drip tube by using the adapted brightness and the set sampling period to construct a picture set.
8. The method for implementing an image recognition-based drip automatic alarm device according to claim 7, wherein the step 3 is implemented as follows:
3-1, the control terminal receives the picture from the infusion end, and carries out graying and convolutional neural network processing on the received picture so as to restore the obtained picture;
3-2, comparing the characteristic point information of the recovered real-time sampling picture with the characteristic point information of the sample image actually prestored in the control terminal database, and judging whether the liquid in the pipe is in a normal state or not according to the matching degree of the characteristic point information: if the matching degree result is in the set range and the characteristics of a certain same characteristic point of the front and back continuous N sampling pictures are consistent, directly sending a corresponding instruction to the console; otherwise, the detection is continued.
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| CN201910711988.5A CN110659558A (en) | 2019-08-02 | 2019-08-02 | Automatic drip alarm device based on image recognition and implementation method thereof |
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| CN201910711988.5A CN110659558A (en) | 2019-08-02 | 2019-08-02 | Automatic drip alarm device based on image recognition and implementation method thereof |
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