CN111436938B - Limb swelling monitoring device and method - Google Patents
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- CN111436938B CN111436938B CN202010367452.9A CN202010367452A CN111436938B CN 111436938 B CN111436938 B CN 111436938B CN 202010367452 A CN202010367452 A CN 202010367452A CN 111436938 B CN111436938 B CN 111436938B
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- 230000008961 swelling Effects 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 10
- 238000012806 monitoring device Methods 0.000 title abstract description 19
- 238000005452 bending Methods 0.000 claims abstract description 90
- 238000012544 monitoring process Methods 0.000 claims abstract description 24
- 238000012937 correction Methods 0.000 claims abstract description 16
- 238000013528 artificial neural network Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 9
- 210000002569 neuron Anatomy 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 1
- 230000000399 orthopedic effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 208000024891 symptom Diseases 0.000 description 1
- 238000002560 therapeutic procedure Methods 0.000 description 1
- 238000000015 thermotherapy Methods 0.000 description 1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1071—Measuring physical dimensions, e.g. size of the entire body or parts thereof measuring angles, e.g. using goniometers
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/107—Measuring physical dimensions, e.g. size of the entire body or parts thereof
- A61B5/1072—Measuring physical dimensions, e.g. size of the entire body or parts thereof measuring distances on the body, e.g. measuring length, height or thickness
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/742—Details of notification to user or communication with user or patient ; user input means using visual displays
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
- A61B2562/02—Details of sensors specially adapted for in-vivo measurements
- A61B2562/0271—Thermal or temperature sensors
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- Surgery (AREA)
- Public Health (AREA)
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- Heart & Thoracic Surgery (AREA)
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- Animal Behavior & Ethology (AREA)
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- Oral & Maxillofacial Surgery (AREA)
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- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
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- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention relates to a limb swelling monitoring device and a limb swelling monitoring method, wherein a bending sensor, a temperature sensor, an MCU processor, a memory and a display screen are matched to form the limb swelling monitoring device, when the monitoring device is used for monitoring, the bending sensor and the temperature sensor of the swelling measuring device are worn on a measured limb, the MCU processor reads the resistance value and the current temperature of the bending sensor in real time, and then the current bending angle of the bending sensor is calculated according to the resistance value and the temperature and by combining correction parameters. And calculating the equivalent perimeter of the measured part according to the length and the bending angle of the bending sensor. The circumference obtained by monitoring the limb at different moments is displayed on a display screen to form a circumference change curve, and medical staff can intuitively acquire the swelling degree and the change trend of the limb according to the change curve.
Description
Technical Field
The invention relates to the field of medical electronics, in particular to a limb swelling monitoring device and method.
Background
Swelling of the limbs is a common clinical symptom, and by observing the change of swelling, the physical condition of the body can be known and diagnosis and treatment can be facilitated, and the accuracy of observing the change of swelling is very critical. The detection of limb swelling is commonly used in orthopedics, and the monitoring of swelling is matched with the cold therapy or the heat therapy after operation, thereby providing help for medical staff. At present, the limb swelling degree is obtained in a manual measurement mode clinically, and great errors exist in accuracy, so that the true health condition cannot be accurately judged, and further treatment measures are not facilitated.
In view of the above, the present inventors have further conceived to develop the present invention in response to various problems associated with monitoring limb swelling.
Disclosure of Invention
The invention aims to provide a limb swelling monitoring device and a limb swelling monitoring method capable of accurately monitoring the swelling degree of a limb.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the limb swelling monitoring device comprises a bending sensor, a temperature sensor, an MCU processor, a memory and a display screen, wherein the bending sensor is connected with the MCU processor and is attached to a limb to be monitored and used for monitoring the swelling degree of the limb to be monitored; the temperature sensor is connected with the MCU processor and is attached to the limb to be measured and used for measuring the temperature of the limb to be measured; the memory is connected with the MCU processor and used for storing correction parameters of the bending sensor; the MCU processor is used for reading the resistance value of the bending sensor and the temperature of the temperature sensor in real time and calculating the swelling degree of the measured limb by combining the correction parameters; the display screen is connected with the MCU processor and used for displaying the limb swelling degree of the limb.
The bending sensor, the temperature sensor, the MCU processor, the memory and the display screen are of an integrated structure.
The bending sensor and the temperature sensor are of an integrated structure and are worn on the limb to be measured; the MCU processor, the memory and the display screen are of an integrated structure.
The bending sensor and the temperature sensor are in communication connection with the MCU processor in a wireless or wired mode.
The bending sensor and the temperature sensor are arranged on the binding belt and are worn on the limb to be measured through the binding belt.
A method of monitoring limb swelling comprising the steps of:
step 1, obtaining a bending sensor angle calculation model
Step 1.1, obtaining resistance values of a bending sensor at different angles at different temperatures to form a parameter correction table of the bending sensor;
step 1.2, training a neural network by using a parameter correction table of a bending sensor to obtain a bending sensor angle calculation model, wherein the model contains correction parameters of the bending sensor;
the neural network is a fully-connected neural network, and has two inputs of temperature and resistance, one output at an angle;
step 2, limb swelling measurement
Step 2.1, wearing a bending sensor and a temperature sensor of the swelling measuring device on a measured limb, and reading a resistance value R of the bending sensor and a temperature value T of the temperature sensor in real time by an MCU processor;
step 2.2, inputting a resistance value R of the bending sensor and a temperature value T of the temperature sensor into a trained bending sensor angle calculation model to obtain a bending angle alpha of the bending sensor;
step 2.3, calculating the equivalent circumference C=L×360/alpha of the limb to be measured according to the length L and the bending angle alpha of the bending sensor;
and 3, displaying the perimeter of the limb to be measured on a display screen in real time, and obtaining the change condition of the swelling degree of the limb to be measured according to the change condition of the perimeter C of the limb to be measured on the display screen.
The neural network has four hidden layers, wherein the first two hidden layers each contain 12 neurons and the second two hidden layers contain 6 neurons.
After the scheme is adopted, the bending sensor, the temperature sensor, the MCU processor, the memory and the display screen are matched to form the limb swelling monitoring device, when the monitoring device is adopted for monitoring, the bending sensor and the temperature sensor of the swelling measuring device are worn on a measured limb, the MCU processor reads the resistance value and the current temperature of the bending sensor in real time, and then the current bending angle of the bending sensor is calculated according to the resistance value and the temperature and by combining correction parameters. And calculating the equivalent perimeter of the measured part according to the length and the bending angle of the bending sensor. The circumference obtained by monitoring the limb at different moments is displayed on a display screen to form a circumference change curve, and medical staff can intuitively acquire the swelling degree and the change trend of the limb according to the change curve. Meanwhile, compared with the existing monitoring device, the detection device adopting the limb swelling has higher accuracy.
Drawings
FIG. 1 is a schematic block diagram of a limb swelling monitoring device according to the present invention;
FIG. 2 is a schematic diagram of a limb swelling monitoring device according to the present invention;
FIG. 3 is a schematic diagram of a neural network according to the present invention;
FIG. 4 is a two-dimensional curved surface diagram of a neural network fit according to an embodiment of the present invention.
Detailed Description
As shown in fig. 1, the present invention discloses a limb swelling monitoring device, which comprises at least one bending sensor 2, at least one temperature sensor 3, an MCU processor 1, a memory 4 and a display 5, wherein the bending sensor 2 is connected with the MCU processor 1, and is attached to a measured limb for monitoring the swelling degree of the measured limb, when the swelling degree of the limb changes, the bending angle of the bending sensor 2 also changes, and the resistance value of the bending sensor also changes; the temperature sensor 3 is connected with the MCU processor 1, is attached to the limb to be measured and is used for measuring the temperature of the limb to be measured; the memory 4 is connected with the MCU processor 1 and is used for storing correction parameters of the bending sensor 2; the MCU processor 1 is used for reading the resistance value of the bending sensor 2 and the temperature of the temperature sensor 3 in real time and calculating the swelling degree of the limb to be measured by combining the correction parameters; the display screen 5 is connected with the MCU processor 1 and is used for displaying the limb swelling degree of the limb.
The bending sensor 2 and the temperature sensor 3 are arranged on the binding band 7 and are worn on the limb to be tested of the patient through the binding band 7, and the MCU processor 1, the memory 4 and the display screen 5 can be worn on the limb to be tested along with the bending sensor 2 or can be independent of the bending sensor 2 and the temperature sensor 3. That is to say, the limb swelling monitoring device can be of an integrated structure or a split structure. When the device is in a split structure, the bending sensor 2 and the temperature sensor 3 are integrated and worn on the limb to be tested, and the MCU processor 1, the memory 4 and the display screen 5 are integrated. At this time, the bending sensor 2 and the temperature sensor 3 may be connected to the MCU processor 1 via data lines, or may be connected to the MCU processor 1 via communication via bluetooth, a network, or the like. Namely, the bending sensor 2 and the temperature sensor 3 are connected with the MCU processor 1 in a communication manner by a wired or wireless manner.
As shown in fig. 2, in the embodiment of the present invention, the limb swelling monitoring apparatus includes three bending sensors 2, three temperature sensors 3, an MCU processor 1, a memory 4, and a display 5, wherein. The MCU processor 1, the memory 4 and the display 5 cooperate to form a monitoring main body 6, and a bending sensor 2 and a temperature sensor 3 are arranged on a binding band 7 to form a group of monitoring binding bands for forming three groups of monitoring binding bands. The bending sensor 2 and the temperature sensor 3 in the three groups of monitoring bands are in communication connection with the MCU processor 1 of the monitoring main body 6 through data lines. As shown in fig. 2, three sets of monitoring straps can monitor different locations of the swollen limb simultaneously. When only one position needs to be measured, only one bending sensor 2 and one temperature sensor 3 can be adopted in the limb swelling monitoring device; when it is desired to measure a plurality of positions, a plurality of bending sensors 2 and a plurality of temperature sensors 3 may be employed.
Based on the same inventive concept, the invention also discloses a limb swelling monitoring method, which comprises the following steps:
step 1, bending a sensor 2 angle calculation model;
step 1.1, obtaining resistance values of the bending sensor 2 at different temperatures and different angles to form a parameter correction table of the bending sensor 2, as shown in table 1;
TABLE 1
Step 1.2, training a neural network by using a parameter correction table of the bending sensor 2 to obtain an angle calculation model of the bending sensor 2, wherein the model contains correction parameters of the bending sensor 2, and can be better fit with a relation curved surface of temperature, angle and resistance value of the bending sensor 2. As shown in fig. 3, the neural network is a fully connected neural network having two inputs of temperature and resistance, one output at an angle, and four hidden layers, wherein the first two hidden layers each contain 12 neurons and the second two hidden layers contain 6 neurons.
A two-dimensional curved surface map obtained by fitting the data in table 1 is shown in fig. 4.
Step 2, limb swelling measurement
Step 2.1, wearing a bending sensor 2 and a temperature sensor 3 of the swelling measuring device on a measured limb, and reading a resistance value R of the bending sensor 2 and a temperature value T of the temperature sensor 3 in real time by an MCU (micro control unit) processor 1;
and 2.2, inputting the resistance value R of the bending sensor 2 and the temperature value T of the temperature sensor 3 into a trained bending sensor 2 angle calculation model to obtain the bending angle alpha of the bending sensor 2.
And 2.3, calculating the equivalent circumference C=L×360/alpha of the limb to be measured according to the length L and the bending angle alpha of the bending sensor 2.
And 3, displaying the perimeter of the limb to be measured on the display screen 5 in real time, and obtaining the change condition of the swelling degree of the limb to be measured according to the change condition of the perimeter C of the limb to be measured on the display screen 5.
The key point of the invention is that the invention adopts the bending sensor 2, the temperature sensor 3, the MCU processor 1, the memory 4 and the display screen 5 to form the limb swelling monitoring device in a matched manner, when the monitoring device is adopted for monitoring, the bending sensor 2 and the temperature sensor 3 of the swelling measuring device are worn on the limb to be measured, the MCU processor 1 reads the resistance value and the current temperature of the bending sensor 2 in real time, and then the current bending angle of the bending sensor 2 is calculated according to the resistance value and the temperature and by combining correction parameters. The equivalent perimeter of the measured part can be calculated according to the length and the bending angle of the bending sensor 2. The circumference of the limb to be measured obtained by monitoring at different moments is displayed on the display screen 5 to form a circumference change curve, and medical staff can intuitively acquire the swelling degree and the change trend of the limb according to the change curve. Meanwhile, compared with the existing monitoring device, the detection device adopting the limb swelling has higher accuracy.
The foregoing embodiments of the present invention are not intended to limit the technical scope of the present invention, and therefore, any minor modifications, equivalent variations and modifications made to the above embodiments according to the technical principles of the present invention still fall within the scope of the technical proposal of the present invention.
Claims (2)
1. A method for monitoring limb swelling, which is characterized by comprising the following steps: the method comprises the following steps:
step 1, obtaining a bending sensor angle calculation model
Step 1.1, obtaining resistance values of a bending sensor at different angles at different temperatures to form a parameter correction table of the bending sensor;
step 1.2, training a neural network by using a parameter correction table of a bending sensor to obtain a bending sensor angle calculation model, wherein the model contains correction parameters of the bending sensor;
the neural network is a fully-connected neural network, and has two inputs of temperature and resistance, one output at an angle;
step 2, limb swelling measurement
Step 2.1, wearing a bending sensor and a temperature sensor of the swelling measuring device on a measured limb, and reading a resistance value R of the bending sensor and a temperature value T of the temperature sensor in real time by an MCU processor;
step 2.2, inputting a resistance value R of the bending sensor and a temperature value T of the temperature sensor into a trained bending sensor angle calculation model to obtain a bending angle alpha of the bending sensor;
step 2.3, calculating the equivalent circumference C=L.360/alpha of the limb to be measured according to the length L and the bending angle alpha of the bending sensor;
and 3, displaying the perimeter of the limb to be measured on a display screen in real time, and obtaining the change condition of the swelling degree of the limb to be measured according to the change condition of the perimeter C of the limb to be measured on the display screen.
2. A method of monitoring limb swelling as defined in claim 1, wherein: the neural network has four hidden layers, wherein the first two hidden layers each contain 12 neurons and the second two hidden layers contain 6 neurons.
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